WO2019169706A1 - 需量预测方法、需量控制方法及系统 - Google Patents

需量预测方法、需量控制方法及系统 Download PDF

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Publication number
WO2019169706A1
WO2019169706A1 PCT/CN2018/084249 CN2018084249W WO2019169706A1 WO 2019169706 A1 WO2019169706 A1 WO 2019169706A1 CN 2018084249 W CN2018084249 W CN 2018084249W WO 2019169706 A1 WO2019169706 A1 WO 2019169706A1
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power
power consumption
information
demand
expected
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PCT/CN2018/084249
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English (en)
French (fr)
Inventor
陈光濠
苏明
王春光
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亿可能源科技（上海）有限公司
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Publication of WO2019169706A1 publication Critical patent/WO2019169706A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the present application relates to the field of industrial control technologies, and in particular, to a demand prediction method, a demand control method, and a system.
  • the power supply company will sign a contract with them to estimate the electricity demand of the enterprise in the next power cycle and limit the instantaneous power consumption of the enterprise to exceed the contractual requirements signed in the contract. Quantity, for the excess part, the power supply company will give a punitive electricity bill.
  • the purpose of the present application is to provide a demand forecasting method, a demand control method and a system for solving the problem of how to reasonably predict the power demand of an enterprise in the prior art, and Based on the predicted electricity demand, the problem of electricity management for actual electricity use is realized.
  • a first aspect of the present application provides a demand prediction method, including: acquiring power-related information including a power usage plan within a power usage cycle; and determining a plurality of power usage based on a predetermined Predicting the corresponding relationship between the respective operating states of the system and the power consumption, and predicting the timing information of each of the expected power consumptions of the power usage plan under the power consumption related information constraint; and based on the expected The timing information of the total amount of electricity determines the expected power demand for the power cycle.
  • the step of performing timing information of the expected total power consumption during the power consumption plan under the information constraint includes: predicting execution in a power cycle based on the correspondence relationship and the at least first scheduling information in the power usage related information The first timing information of the expected total amount of power used by the power usage plan.
  • the step of performing time series information of the expected total power consumption period during the power consumption planning includes: adjusting at least the second scheduling information determined based on the power usage related information based on the correspondence relationship; The second scheduling information predicts timing information of the expected total power consumption during the execution of the power usage plan in a power usage cycle; repeating the steps of adjusting the power consumption related information and predicting the timing information of the total expected power consumption until The second timing information is superior to the first timing information.
  • the step of obtaining second timing information that is better than the first timing information includes: based on the correspondence relationship and at least a power usage plan in the power usage related information And the electricity price information, according to at least one of the electricity usage related information, selecting the timing of at least one of the lowest electricity consumption, the minimum fluctuation of the expected electricity consumption, and the minimum peak value of the expected total electricity consumption.
  • the information is used as the second timing information.
  • the demand prediction method further includes: predicting a power supply amount of the self-power supply system based on the power consumption related information; and compensating the timing information by using the power supply amount The expected total amount of power used to determine the expected power demand based on the compensated timing information.
  • the step of determining an expected power demand for the power cycle based on timing information of a total amount of expected power usage includes at least one of: a peak maximum value in the electrical timing information as the expected power demand; amplifying a peak maximum value in the expected power consumption timing information according to a preset ratio to obtain the expected power demand; based on the expected power consumption The fluctuation of the timing information selects the expected power demand from the expected power consumption timing information.
  • the demand prediction method further includes updating respective operations of the plurality of power systems based on historical power consumption of each of the power systems in at least one last power cycle The step of the relationship between the state and the amount of electricity used.
  • the power usage related information further includes at least one of the following: personnel information, weather forecast information, scheduling information, maintenance information of each power system, and electricity price information.
  • the power system includes at least one of the following: a power system for manufacturing and a power system for living in the office.
  • a second aspect of the present application provides a demand control method, including: monitoring a total amount of actual power consumption of a plurality of power systems that are operated during execution of a power usage plan; and determining, based on the acquired power usage plan including a power usage plan Information and the total amount of actual power usage monitored, predicting the total amount of power consumption timing information during the period of the power consumption plan; when the power consumption total time series information approaches or reaches a preset expected power demand According to the actual amount of electricity actually monitored, the electricity consumption is regulated.
  • the predicting the unexecuted period of the power usage plan based on the acquired power usage related information including the power usage plan and the monitored actual power usage total amount includes: predicting, according to the power-related information and the monitored actual power consumption, the timing information of the total power consumption of the at least one unit prediction duration.
  • the power consumption is performed according to the current total monitored power usage.
  • the step of regulating includes: determining a predicted duration of the total amount of power that is close to or reaching a preset expected power demand in the total amount of power consumption timing information from the current time; based on the predicted duration and the currently monitored actual usage The total amount of electricity is regulated by electricity consumption.
  • the method further includes continuously monitoring actual power consumption of each of the powered systems; and timing information based on the continuously monitored actual power usage and a preset power usage system The step of determining the current operating state of the power system by the correspondence between the operating state and the power consumption.
  • the step of performing power consumption regulation according to the total amount of actual power usage currently monitored includes the following steps: when the power consumption total time series information approaches or reaches a pre-stage When the expected power demand is set, the operating state of the at least one power system is adjusted according to the current operating state of each of the power systems to reduce the actual power consumption in the corresponding period.
  • the demand prediction method further includes the step of updating the correspondence based on historical power usage of each of the powered systems that have been monitored.
  • the step of performing power consumption regulation according to the total amount of actual power usage currently monitored includes: when the power consumption total time series information approaches or reaches a preset state When the power demand is expected, the self-powered system is controlled to compensate the power supply lines of each of the power systems to reduce the actual power consumption in the corresponding period.
  • the demand prediction method further includes the step of predicting a power supply amount of the self-powered system based on the power usage related information.
  • a third aspect of the present application provides a demand forecasting system, including: an obtaining module, configured to acquire power-related information including a power usage plan in a power usage cycle; and a prediction module, configured to determine, based on the plurality of power usages Predicting the corresponding relationship between the respective operating states of the system and the power consumption, and predicting the timing information of the total amount of expected power consumption of the power usage plan under the constraint of the power consumption related information; and The timing information of the expected total amount of power is used to determine an expected power demand of the power cycle to control the power usage during the power cycle based on the expected power demand.
  • the predicting module predicts to perform the use in a power usage period based on the correspondence relationship and the at least first scheduling information in the power usage related information The first timing information of the expected total amount of electricity used by the electricity plan.
  • the prediction module further adjusts at least second scheduling information determined based on the power usage related information based on the correspondence relationship; based on the adjusted at least second row
  • the production information predicts time series information of the expected total power consumption during the execution of the power usage plan in a power usage cycle; and repeats the steps of adjusting the power consumption related information and predicting the timing information of the total expected power consumption until the time is obtained And second timing information of the first timing information.
  • the manner in which the prediction module obtains second timing information that is better than the first timing information includes: selecting, according to at least the power usage plan in the power usage related information, The timing information of at least one of the lowest electric cost, the minimum fluctuation of the expected total power consumption, and the minimum peak maximum value of the expected total power consumption is used as the second timing information.
  • the prediction module is further configured to predict an amount of power supplied from the power supply system based on the power-related information; and to compensate the timing information by using the power supply amount The expected total amount of power used to determine the expected power demand based on the compensated timing information.
  • the manner in which the prediction module determines an expected power demand of the power cycle based on timing information of a total amount of power expected includes at least one of the following: Expecting a peak maximum value in the power consumption timing information as the expected power demand; or amplifying a peak maximum value in the expected power consumption timing information according to a preset ratio to obtain the expected power demand amount; The fluctuation of the expected power consumption information is expected to select the expected power demand from the expected power consumption timing information.
  • the demand prediction system further includes an update module, configured to update the plurality of power consumptions based on the power consumption of each of the power systems in at least one last power cycle The corresponding relationship between the operating status of the system and the power consumption.
  • the power usage related information includes: power consumption system information for operating a power usage plan, power usage planning information, and at least one of the following: personnel information, weather prediction information, Maintenance information, scheduling information and electricity price information of the electricity system.
  • the power system includes at least one of the following: a power system for manufacturing and a power system for living in the office.
  • a fourth aspect of the present application provides a server, including: an interface unit, configured to acquire power-related information including a power usage plan in a power usage cycle; a storage unit configured to store at least one program; and a processing unit configured to: The at least one program is invoked to coordinate the interface unit and the storage unit to perform the demand prediction method as described in the first aspect.
  • a fifth aspect of the present application provides a demand control system, including: a monitoring module, configured to monitor a total amount of actual power used by a plurality of powered systems during execution of a power usage plan; and a prediction module for Obtaining the electricity-related information including the electricity consumption plan and the total amount of actual electricity used for monitoring, predicting the time series information of the total electricity consumption during the non-execution period of the power consumption plan; and the control module for using the total amount of electricity used When the timing information approaches or reaches the preset expected power demand, the power consumption is adjusted according to the actual monitored total power consumption.
  • the prediction module predicts the total amount of power consumption timing information for the at least one unit prediction duration according to the power usage related information and the monitored actual power usage amount.
  • the regulation module when the power consumption total timing information approaches or reaches a preset expected power demand, the regulation module is configured according to the actual monitored actual power consumption.
  • the method for controlling the power consumption includes: determining a predicted duration of the total amount of power that is close to or reaching a preset expected power demand in the total time series information of the power usage, and a current time interval; The actual amount of electricity used for monitoring is regulated by electricity consumption.
  • the monitoring module is further configured to continuously monitor timing information of actual power consumption of each of the power systems and a preset operating state and power consumption of the power system. The corresponding relationship between the quantities determines the current operating state of the power system.
  • the regulation module when the power consumption total timing information approaches or reaches a preset expected power demand, the regulation module is configured according to the current operation of each of the power systems.
  • the state adjusts the operating state of at least one of the power systems to reduce the actual amount of power used in the corresponding period.
  • the demand control system further includes an update module, configured to update the correspondence based on the historical power consumption of each of the powered systems that have been monitored.
  • the manner in which the control module performs power consumption regulation according to the total amount of actual power usage currently monitored further includes: when the power consumption total time series information approaches or reaches When the preset expected power demand is used, the self-powered system is controlled to compensate the power supply lines of the power systems to reduce the actual power consumption in the corresponding period.
  • the prediction module is further configured to predict an amount of power supplied by the self-powered system based on the power-related information.
  • the sixth aspect of the present application provides a computer device, including: an interface unit, configured to acquire power-related information corresponding to a currently used power consumption plan, and a storage unit configured to store at least one program; The at least one program is invoked to coordinate the interface unit and the storage unit to perform the demand prediction method as described in the second aspect.
  • a seventh aspect of the present application provides a demand control system, comprising: at least one metering device for measuring a cumulative power consumption of a connected power system; and a computer device communicably connected to each of the metering devices and having an interface unit And for obtaining the cumulative power consumption of each of the metering devices from the metering device, acquiring power-related information including the power usage plan from the interface unit, and performing the demand forecasting method according to the second aspect.
  • the demand prediction method, the demand control method, and the system of the present application have the following beneficial effects: the demand prediction method provided by the present application can more accurately introduce the power-related information in the power cycle. Forecast the expected electricity demand, so that enterprises can plan their electricity more rationally.
  • the present application also provides a demand control method to effectively control the power consumption efficiency of the enterprise by effectively monitoring the process of executing the power consumption plan to utilize the internal resources of the enterprise.
  • Figure 1 shows a graph of the total amount of electricity used by each power system over time during the execution of production activities and during the execution of production activities.
  • FIG. 2 is a schematic structural diagram of a server in an embodiment of the present application.
  • FIG. 3 shows a flow chart of an embodiment of the demand prediction method of the present application in an embodiment.
  • FIG. 4 is a flow chart showing still another embodiment of the demand prediction method of the present application.
  • FIG. 5 is a schematic diagram showing first timing information predicted based on first scheduling information in the power-related information of the present application.
  • FIG. 6 shows a flow chart of a demand prediction method of the present application in still another embodiment.
  • FIG. 7 is a schematic diagram showing the interface of the first time information and the second time information of the present application.
  • FIG. 8 shows an architectural diagram of an demand prediction system of the present application in an embodiment.
  • FIG. 9 is a block diagram showing the structure of a demand control system of the present application in an embodiment.
  • FIG. 10 is a block diagram showing the structure of a computer device in the demand control system of the present application.
  • Figure 11 is a flow chart showing an embodiment of the demand control method of the present application.
  • FIG. 12 is a schematic diagram showing the total amount of electricity used and the expected electricity demand on the time series information of the total electricity consumption predicted by the demand control scheme of the present application.
  • Figure 13 shows an architectural diagram of an demand control system of the present application in an embodiment.
  • first, second, etc. are used herein to describe various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another.
  • the first predetermined threshold may be referred to as a second predetermined threshold, and similarly, the second predetermined threshold may be referred to as a first predetermined threshold without departing from the scope of the various described embodiments.
  • Both the first preset threshold and the preset threshold are describing a threshold, but unless the context is otherwise explicitly indicated, they are not the same preset threshold.
  • a similar situation also includes a first volume and a second volume.
  • the peak of electricity consumption of enterprises is inseparable from the production plans, business hours, and planning activities of enterprises.
  • FIG. 1 it is shown as a graph showing the relationship between the total amount of electricity used by each power system over time during the execution of the production activity and during the execution of the production activity.
  • the enterprise's electricity demand forecast is usually the total amount of electricity required to predict the company's production and operation activities.
  • the total amount of electricity used is the sum of the short-term electricity consumption of each power system of the enterprise (ie, the total energy consumption). It is precisely because of the important influence of production and operation behavior on the electricity consumption of enterprises that the historical power consumption data alone cannot accurately predict the peak value of the total electricity consumption in the next power cycle.
  • the energy prediction method is mainly performed by an energy prediction system.
  • the energy prediction system may be a software system configured on the server, which uses the hardware of the configured server to execute a corresponding program to provide the enterprise with time information of the total amount of power required to execute the corresponding production activity in the next power cycle. Then, according to the predicted timing information, the power demand of the enterprise in the phase of the applied electrical cycle is determined.
  • the power cycle may be the same as the power cycle agreed in the contract, or the power cycle may be set according to the production activity.
  • the predicted electricity demand can be used to help companies declare the contracted electricity demand to more rationally set the electricity cost.
  • the server includes, but is not limited to, a single server, a server cluster, a distributed server group, a cloud server, and the like.
  • the cloud server includes a public cloud server and a private cloud server, where the public or private cloud server includes Software-as-a-Service (software as a service, SaaS). ), Platform-as-a-Service (Platform as a Service, PaaS) and Infrastructure-as-a-Service (Infrastructure as a Service, IaaS).
  • the private cloud server is, for example, an Facebook Cloud computing service platform, an Amazon cloud computing service platform, a Baidu cloud computing platform, a Tencent cloud computing platform, and the like.
  • the server is connected to the power control system of the enterprise, the management system of the production activity, and the like, and can even connect the third-party system with the data, and use the crawler technology to obtain Internet data related to the enterprise power consumption in the Internet.
  • the power control system includes, but is not limited to, a metering device (such as a power meter) installed in an enterprise, an electrical equipment control system, and the like.
  • the management system includes, but is not limited to, a MES (Manufacturing Execution System), an Enterprise Resource Planning (ERP), and the like.
  • the third-party system examples include a self-owned WEB server for storing a historical power data server, for acquiring an enterprise power consumption plan, and the like.
  • Examples of the Internet data include weather forecast data and the like, wherein the weather forecast data may be predicted based on historical weather data acquired from the Internet, or weather forecast data directly obtained from a weather website or other website.
  • the demand forecasting system performs the following steps by using the obtained power-related information of each party to predict an expected power demand of the next power cycle, so as to control the enterprise to use the power based on the expected power demand The total amount of electricity used in the cycle.
  • FIG. 2 is a schematic structural diagram of a server in an embodiment.
  • the server includes an interface unit 11, a storage unit 12, and a processing unit 13.
  • the storage unit 12 includes a non-volatile memory, a storage server, and the like.
  • the non-volatile memory is exemplified by a solid state hard disk or a USB disk.
  • the storage server is configured to store various acquired power related information.
  • the interface unit 11 includes a network interface, a data line interface, and the like.
  • the network interface includes, but is not limited to, an Ethernet network interface device, a network interface device based on a mobile network (3G, 4G, 5G, etc.), a network interface device based on short-range communication (WiFi, Bluetooth, etc.), and the like.
  • the data line interface includes but is not limited to: a USB interface, RS232, and the like.
  • the interface unit is connected to data of various systems, third-party systems, and the Internet of the enterprise.
  • the processing unit 13 is connected to an interface unit and a storage unit, and includes at least one of a CPU or a chip integrated with a CPU, a programmable logic device (FPGA), and a multi-core processor.
  • the processing unit 13 also includes a memory, a register, and the like for temporarily storing data. Please refer to FIG. 3, which is a flowchart of the demand prediction method.
  • the processing unit 13 reads at least one program and power usage related information stored by the storage unit to perform a demand prediction method as described below.
  • the power-related information is obtained by the processing unit 13 in advance from an interface unit (not shown) and stored in the storage unit 12.
  • the power-related information including the power usage plan within a power usage cycle is acquired.
  • the power consumption plan refers to at least one of a production plan, an activity plan, and a business plan that are predicted by the enterprise in the applicable application cycle.
  • the power usage plan includes, but is not limited to, production or activity time limits, electrical equipment used for production or activities, and the like.
  • the power usage related information may further include at least one of the following: scheduling information, personnel information, weather prediction information, equipment maintenance information, and electricity price information.
  • the information about the planning activity is information including an event date, a resource configuration, and the like, which are set to match a corporate celebration, a holiday promotion, and the like.
  • the scheduling information is information including schedule calculation date and time, and resource configuration information determined based on production orders, resources, shifts, holidays, exception shifts, bill of materials, job priority, and the like.
  • the personnel information includes the number of personnel in the office area, the number of personnel in the factory area, the number of shift employees, and the number of expected personnel of the enterprise activities.
  • the electricity price information includes a unit price of electricity price in each period, a price of electricity required for a contract, and the like.
  • the device maintenance information includes the device name and the corresponding power system.
  • the power consumption system includes any power consuming equipment (or a collection of power consuming equipment) or a main power consuming equipment (or a collection of power consuming equipment) used in the enterprise during the execution of the phase application power plan.
  • the powered system can include at least one powered device of the same category.
  • the demand forecasting system treats all lighting equipment in the plant and office areas as one power system.
  • the powered system can include at least one powered device that accesses the same metering device.
  • both the air conditioning equipment and the lighting equipment located in the living office area are connected to the same metering device that treats the plurality of air conditioning equipment and lighting equipment using the same metering device as one power system.
  • the power system includes at least one of the following: a power system for manufacturing and a power system for living in the office.
  • the power system used for manufacturing includes but is not limited to: the power system used in the production line, the separate power system, the air conditioning system and the lighting system in the factory;
  • the power usage systems used include, but are not limited to, assembly lines, preparation lines, test lines, and the like.
  • Separate power systems include, but are not limited to, drive equipment, control equipment, and the like.
  • the power system for living office includes but is not limited to an air conditioning system, a lighting system, an elevator control system, a strong and weak power conversion system, and the like.
  • the demand forecasting system can read the power usage plan and other power-related information associated with the power usage plan by a database shared by the enterprise MES system (or ERP system). For example, according to the needs of the sharable data, the demand forecasting system reads the scheduling information, the personnel information, the maintenance information of the power system, and the execution order of each power system from the database corresponding to the MES system.
  • the demand forecasting system may provide an interface for the enterprise to input power-related information including a power usage plan, and obtain corresponding power-related information by using the interface.
  • the demand forecasting system also obtains electricity-related information that affects the power consumption of the power system from a third party or the Internet. For example, the demand forecasting system acquires weather information and other power consumption information that affects the operation of the air conditioning system.
  • step S120 based on the preset correspondence between the respective operating states of the plurality of power systems and the power consumption, predicting an expectation that each of the power systems performs the power usage plan under the power consumption related information constraint Timing information for the total amount of electricity used.
  • the operating state of the power system includes a combination of operating states of all powered devices in the power system.
  • the operating state of the powered device refers to the operation of the motor, the controller, and the like in the electrical device operating in at least one state and maintaining the corresponding state.
  • the air-conditioning device includes a plurality of air-conditioning devices including a standby mode, a fresh air mode, a cooling mode, a heating mode, and the like, and each type is based on an air volume, a cooling (heat) temperature, and the like.
  • the mode includes at least one operational state, the operational state of the electrical system including a combination of reasonable operational states of each air conditioning device.
  • the above-mentioned power system is only an example, and not every power device must have multiple modes.
  • the lighting device only includes an open state and an off state, and the power system including the lighting device is in an open state for each lighting device and
  • the technician should determine the corresponding operating state based on the actual operating capacity of the powered device.
  • the demand forecasting system further prestores the power consumption of each power system in the corresponding operating state.
  • the power consumption corresponding to each operating state may be determined by pre-simulating the operating states of the powered device, or based on the device parameters of the powered device.
  • the device parameters of the electrical device are used to simulate the power consumption of the electrical device in each operating state, wherein the device parameters include, but are not limited to, electrical parameters such as rated power and maximum power, and physical quantities such as flow rate, pressure, and rotational speed. Parameters, as well as environmental parameters such as temperature.
  • the power usage corresponding to each operating state is determined based on historical historical power usage of each of the power systems. Therefore, in the period before the demand forecast is performed, the actual power consumption or the actual power consumption of each power system before the execution of this prediction is collected to determine the corresponding operation state changes by machine learning.
  • the change of the operating state refers to a change process of the power system from the operating state A1 to the operating state A2, which includes the sequence of operating states A1 and A2.
  • the conveyor device adjusts from the standby state to the transmission state as the operating state change from the standby state to the transmission state.
  • the conveyor device is stopped according to the predetermined conveyor device.
  • the power consumption is zero, and the running state of the power consumption from low to high is the stop state, the standby state, and the transmission state, and the amount of power consumption corresponding to the change of each operating state of the conveyor device is determined.
  • the operating state and the corresponding actual power consumption determine the operating state of the power system and the corresponding power consumption.
  • the manner in which the amount of power consumption change corresponding to each operating state change is determined by the machine learning manner includes but is not limited to the following examples:
  • variable point sequence of the power system and the actual power usage subsequence corresponding to each change point are obtained by accumulating for a period of time; according to the monitored power consumption parameters of each power device in the power system, and each power consumption The electrical characteristics of the main electrical components in the device, and the characteristic analysis of the actual power usage subsequences are performed to obtain the actual amount of power consumption change corresponding to the change of the operating state of each powered device.
  • variable point sequence of the power system and the actual power usage subsequence corresponding to each change point are obtained by accumulating for a period of time; the actual power usage subsequences are clustered and classified, and then the actual use of the same classification is performed.
  • the change of the operating state is matched with the actual amount of power consumption change of each class, so that the actual amount of power consumption corresponding to each power running state change is obtained.
  • the correspondence is stored on a storage server including the database for reading by the demand forecasting system.
  • the power consumption corresponding to the operating state of each power system is not fixed, and is affected by the power system loss, maintenance, etc., and the power consumption corresponding to the operating states of the power systems.
  • the amount will be updated periodically or before the step S120 is performed.
  • FIG. 4 which is shown as a flow chart of the demand prediction method in still another embodiment.
  • the demand prediction method Before performing the step S120, the demand prediction method further includes a step S100 of updating respective operating states of the plurality of power systems based on the historical power consumption of each of the power systems in at least the last power cycle. Correspondence of electricity consumption.
  • the demand prediction system may perform step S100 according to the foregoing corresponding relationship determining manners to obtain the updated corresponding relationship, which will not be described in detail herein.
  • the step S100 may further include: based on historical power consumption of each of the power systems in at least one last power cycle, and a preset loss parameter of each power system, the power consumption related At least one of the information, the step of determining a correspondence between respective operating states of the plurality of power systems and the amount of power used.
  • the power-related information includes inspection and maintenance information, power usage plans, and the like of the electrical equipment in the power system. Among them, the loss parameter of the electrical equipment itself, the maintenance operation performed by the technician, and the duration and operating state changes of the operating states of the power system generated by the power consumption plan, the personnel information participating in the power consumption plan, etc.
  • the demand forecasting system quantizes each of the influencing factors determined based on the power-related information, and serves as a constraint parameter for determining the power consumption corresponding to each of the operating states of the plurality of power systems, and utilizes the latest history.
  • the corresponding relationship is updated by the power consumption to obtain the power consumption corresponding to the respective operating states of the real power system.
  • the quantized data of the loss of each power system itself can be determined based on the loss curve; the quantitative parameter of the inspection and maintenance of each power system can be determined based on the change of power consumption before and after the historical inspection maintenance.
  • the quantization parameter of the power usage plan may be determined based on the change in power usage of a plurality of power usage plans that have been historically executed. According to the design requirements of the actual prediction algorithm, each of the above-mentioned quantization parameters may be used as at least one of a weight, an offset, and a parameter interval threshold for determining each power consumption. Step S120 is performed after determining the power consumption corresponding to the operating state of each power system by using the at least one quantization parameter, that is, using the correspondence relationship to predict the power-related information of each power system. Time-series information for performing the total amount of expected power usage of the power usage plan under constraints.
  • the demand prediction system may predict to be in a power usage cycle according to at least scheduling information (hereinafter referred to as first scheduling information) provided in the power usage plan and the correspondence relationship.
  • First scheduling information for performing a total amount of expected power usage of the power usage plan.
  • the demand forecasting system determines the order of change of the operating state of the phase-applied electrical device according to the execution order of the powered devices in each power-distributing system arranged according to the first scheduling information; a sequence of operating state changes, determining an order of operating state combinations of the electrical devices belonging to the same power system; and predicting, according to the correspondence, a total amount of expected power consumption of the power plan in a power cycle A timing message. Please refer to FIG.
  • the first timing information Pz is the sum of the timing information Pd1 of the expected power consumption of the power system D1 and the timing information Pd2 of the expected power consumption of the power system D2.
  • the manner of predicting the first time series information is only an example, and the power consumption related information may further include usage plan information of each power system as a flexible load, such as a lighting system, an air conditioning system, and an elevator system.
  • the weather forecast information, the maintenance information of the electrical equipment, the personnel information, and other information related to the power consumption, the demand forecasting system also predicts the order of the hard state changes during the operation of each power system including the flexible load based on the above information.
  • the first timing information including the timing information.
  • the weather forecast information includes weather forecast information within a power usage period issued by the meteorological department, or weather forecast information predicted based on historical weather information.
  • the personnel information includes the number of personnel directly involved in scheduling, and the number of personnel indirectly participating in the scheduling (such as the number of office workers).
  • the maintenance information of the powered device includes maintenance information that has been currently maintained, and/or a maintenance plan during the phase of the applied power cycle, and the like.
  • the demand forecasting system may determine timing information of expected power consumption of the power system of the flexible load according to the foregoing information, and superimpose the timing information of all the obtained power systems to obtain the predicted total expected power consumption. First timing information.
  • the information of each scheduling may originate from different scheduling departments, and the lack of horizontal communication between the scheduling departments leads to the concentration of scheduling information. In a short period of time to the power cycle, this causes the predicted first timing information to have an excessive peak maximum value in the corresponding time period, while the total amount of power usage in other time periods is too small.
  • FIG. 6 is a flowchart of the demand forecasting method in still another embodiment. In contrast to FIG. 3 and FIG. 4 , the demand forecasting system performs the total amount of expected power consumption. When forecasting, the following steps are also performed:
  • step S121 at least second scheduling information determined based on the power usage related information is adjusted based on the correspondence.
  • the scheduling information directly provided by the power-related information is first scheduling information, and the demand forecasting system adjusts the first scheduling information according to the power consumption plan to obtain second scheduling information.
  • the demand forecasting system can also derive a usage plan of a power system such as a lighting system or an air-conditioning system.
  • the usage plan information of the lighting system and the air-conditioning system in the production area is determined; the number of personnel in the office area and the weather forecast information are determined according to the information on the electricity-related information. Information on the use of lighting systems and air conditioning systems in the office area.
  • the usage plan information of the elevator system is determined according to the commuting time period of the power-related information, the shift time period, and the like.
  • Step S122 is performed as input information in the order of change in the operating state of the power system corresponding to each of the second scheduling information determined based on the power-related information and the usage plan of each power system.
  • step S122 timing information of the expected total amount of power consumption during the execution of the power usage plan in a power usage period is predicted based on the adjusted at least second scheduling information.
  • the prediction manner is the same as or similar to the foregoing manner of predicting the first timing information, and is not described in detail herein.
  • the steps of adjusting the power-related information and predicting the timing information of the total amount of expected power are repeated until the second timing information superior to the first timing information is obtained.
  • the number of repetitions is determined according to a preset convergence condition or a repetition number threshold.
  • the convergence condition includes, but is not limited to, a minimum of the electricity cost reflected by the time series information of the total expected power consumption, a minimum fluctuation of the expected total power consumption, and a minimum peak value of the expected total power consumption.
  • timing information of the corresponding expected total power consumption is used as the second timing information.
  • the demand forecasting system limits the scheduling information, the usage plan information of the flexible load, and the like according to the power consumption plan, and performs time series information of the expected total power consumption according to the adjusted information to obtain the expected power consumption.
  • Timing information with minimum fluctuation of total amount and/or minimum peak value of expected total electricity consumption, and feedback to relevant personnel of the enterprise, for the relevant personnel to adjust the scheduling information based on the predicted timing information .
  • some regional electricity price standards set a variety of electricity price gradients according to cumulative electricity consumption, power consumption peaks and valleys.
  • the demand forecasting system can obtain electricity price information including electricity price gradients from third-party systems, and then obtain The time limit in the electricity plan is predicted to be the order of change of the operating state corresponding to the start of the power system, gear adjustment, stop, etc., such as the production activity corresponding to the power plan, and is subject to the price information including the electricity price gradient. Selecting one of the operating state change orders of the plurality of sets of candidate power systems to make the lowest timing information as the second timing information; and determining the second scheduling information corresponding to the second timing information , various usage plan information, etc.
  • the enterprise has a self-powered system inside.
  • the self-powered system includes but is not limited to: a photovoltaic power generation system, a heat conversion system, an energy storage system, a triple supply system, a wind power generation system, and the like.
  • the company's self-powered system can also be included in the scope of forecasting electricity demand.
  • the demand prediction method further includes predicting a power supply amount of the self-power supply system based on the power-related information, and compensating the total amount of expected power within the time-series information by using the power supply amount, so as to be based on the compensated
  • the timing information determines the step of the expected power demand.
  • the demand prediction system estimates the peak value of the predicted time series information and the self-power supply system during a period in which the electricity price is high according to the weather forecast information in the power consumption related information.
  • the amount of power supplied, and the estimated amount of power supply is compensated for the expected amount of power used in the corresponding period of time within the timing information to obtain compensated timing information.
  • the demand prediction system estimates a power supply amount converted in a corresponding time period based on a preset thermal conversion rate based on a period in which the electric system discharges thermal energy in the predicted timing information, and The converted power supply amount is compensated to the time period in which the thermal energy is discharged to obtain compensated timing information.
  • the demand prediction system can estimate the electrical energy that can be converted by the thermal conversion system according to the above manner, and store the electrical energy according to the storage loss rate of the energy storage system.
  • the electric energy in the energy storage system; the amount of power supplied from the power supply system during the peak of the predicted timing information and the period when the electricity price is high, and the estimated power supply amount is compensated for the total expected power consumption in the corresponding time period in the time series information.
  • the demand forecasting system should also consider the charging plan information and the discharging plan information of the energy storage system in the self-powered system when predicting the timing information of the expected total power consumption. For this reason, the first timing information and the second timing are predicted.
  • the charging process of the energy storage system can also be regarded as a power consumption process of the power system, and the charging plan information of the energy storage system determined based on the power consumption related information is used to predict the power consumption of the energy storage system. Timing information, and the sum of the predicted timing information of each power usage system is determined as the timing information of the predicted total expected power consumption.
  • the manner of predicting the timing information of the expected total power consumption by using the power supply amount of the self-power supply system is merely an example, and is not a limitation of the present application.
  • the power supply quantity predicted by the demand forecasting system is determined directly or indirectly according to the power-related information.
  • the basis based on the technical idea is extended.
  • the manner in which the power-related information is used to predict the amount of power supplied from the power supply system, and the manner in which the predicted timing information is compensated using the predicted power supply amount is considered as a specific example of the present application.
  • step S130 After predicting the timing information within a power usage cycle, the demand prediction system performs step S130.
  • step S130 an expected power demand of the power cycle is determined based on the timing information of the expected total power consumption.
  • the demand forecasting system may further help the enterprise to set the expected power demand according to the peak maximum value in the timing information, and the enterprise may according to the expectation Report the contractual demand to the power supply company with the electricity demand.
  • the demand forecasting system may provide the predicted at least one timing information and the corresponding scheduling information to the enterprise.
  • FIG. 7 is shown as an interface diagram showing curves of first timing information and second timing information
  • the demand prediction system plots the predicted timing information into a curve, and maximizes the peak value on the curve.
  • the value of the value is displayed on the corresponding display interface.
  • the expected power demand corresponding to the respective timing information may also be displayed on the display interface.
  • the expected power demand may be obtained by a peak maximum value of the corresponding timing information or by amplifying the corresponding peak maximum value according to a preset ratio.
  • the demand forecasting system further selects an expected power demand from the expected power consumption timing information based on the fluctuation condition of the expected power consumption timing information.
  • the fluctuation condition includes, but is not limited to, a deviation between peaks in the expected power consumption timing information, a duration of each peak in the expected power consumption timing information, and the like.
  • the expected power demand may be obtained by a peak maximum value of the corresponding timing information or by amplifying the corresponding peak maximum value according to a preset ratio.
  • the demand forecasting system may also determine an expected power demand based on the fluctuation condition and the electricity price information.
  • the peak maximum value in the predicted expected power consumption timing information is sharper and shorter than other peaks
  • the fluctuation of the electricity price payment and penalty criteria, and the expected power consumption timing information may be selected to be lower than the peak maximum.
  • the value of electricity consumption is used as the expected electricity demand.
  • the enterprise related personnel may adjust corresponding scheduling information according to various information generated by the demand forecasting system according to the electricity cost, and then feed back the power-related information including the adjusted scheduling information to the The demand prediction system is provided for it to perform steps S110-S130 again.
  • the expected power demand for the power cycle is thus predicted.
  • the application also provides a demand forecasting system.
  • the demand forecasting system is a software system configured on the server side. Please refer to FIG. 8, which is shown as an architectural diagram of the demand prediction system in an embodiment.
  • the demand prediction system 2 includes program modules such as an acquisition module 21 and a prediction module 22.
  • the obtaining module 21 is configured to acquire power-related information including a power usage plan within a power usage period.
  • the power consumption plan refers to at least one of a production plan, an activity plan, and a business plan that are predicted by the enterprise in the applicable application cycle.
  • the power usage plan includes, but is not limited to, production or activity time limits, electrical equipment used for production or activities, and the like.
  • the power usage related information may further include at least one of the following: scheduling information, personnel information, weather prediction information, equipment maintenance information, and electricity price information.
  • the scheduling information is information including a schedule calculation date and time, and resource configuration information determined based on a production order, a resource, a shift, a holiday, an exception shift, a bill of materials, a job priority, and the like.
  • the power usage related information may further include at least one of the following: personnel information, weather forecast information, maintenance information of the power system, power price information, and execution order of each power system.
  • the information of the planning activity is information including an event date, a resource configuration, and the like, which are determined to meet the corporate celebration, the holiday promotion, and the like.
  • the power consumption system includes any power consuming equipment (or a collection of power consuming equipment) or a main power consuming equipment (or a collection of power consuming equipment) used in the enterprise during the execution of the phase application power plan.
  • the powered system can include at least one powered device of the same category.
  • the acquisition module 21 treats all lighting devices in the plant and office areas as one power system.
  • the powered system can include at least one powered device that accesses the same metering device.
  • both the air conditioning device and the lighting device located in the living office area are connected to the same metering device, and the obtaining module 21 regards a plurality of air conditioning devices and lighting devices using the same metering device as one power system.
  • the power system includes at least one of the following: a power system for manufacturing and a power system for living in the office.
  • the power system used for manufacturing includes but is not limited to: the power system used in the production line, the separate power system, the air conditioning system and the lighting system in the factory;
  • the power usage systems used include, but are not limited to, assembly lines, preparation lines, test lines, and the like. Separate power systems include, but are not limited to, drive devices, control devices, and the like.
  • the power system for living office includes but is not limited to an air conditioning system, a lighting system, an elevator control system, a strong and weak power conversion system, and the like.
  • the obtaining module 21 can read the power consumption plan and other power-related information associated with the power consumption plan by using a database shared by the enterprise MES system (or ERP system). For example, the acquisition module 21 reads the scheduling information, the personnel information, the maintenance information of the power system, the execution order of each power system, and the like from the database corresponding to the MES system according to the needs of the shareable data. For another example, the obtaining module 21 can provide an interface for the enterprise to input the power-related information including the power usage plan, and obtain corresponding power-related information by using the interface. According to the design requirements, the obtaining module 21 also obtains power-related information that affects the power consumption of the power system from a third party or the Internet. For example, the acquisition module 21 acquires weather information and other power consumption information that affects the operation of the air conditioning system.
  • the prediction module 22 is configured to predict, according to a preset correspondence between respective operating states and power consumptions of the plurality of power systems, that each of the power systems performs the power usage plan under the constraint of the power consumption related information Timing information for the expected total amount of electricity used.
  • the prediction module 22 prestores the operating states of the respective power systems in the enterprise that have at least a large influence on the power consumption.
  • the operating state of the power system includes a combination of operating states of all powered devices in the power system.
  • the operating state of the powered device refers to the operation of the motor, the controller, and the like in the electrical device operating in at least one state and maintaining the corresponding state.
  • the air-conditioning device includes a plurality of air-conditioning devices including a standby mode, a fresh air mode, a cooling mode, a heating mode, and the like, and each type is based on an air volume, a cooling (heat) temperature, and the like.
  • the mode includes at least one operational state, the operational state of the electrical system including a combination of reasonable operational states of each air conditioning device.
  • the above-mentioned power system is only an example, and not every power device must have multiple modes.
  • the lighting device only includes an open state and an off state, and the power system including the lighting device is in an open state for each lighting device and
  • the technician should determine the corresponding operating state based on the actual operating capacity of the powered device.
  • the prediction module 22 also prestores the power consumption of each power system in the corresponding operating state.
  • the power consumption corresponding to each operating state may be determined by pre-simulating the operating states of the powered device, or based on the device parameters of the powered device.
  • the device parameters of the electrical device are used to simulate the power consumption of the electrical device in each operating state, wherein the device parameters include, but are not limited to, electrical parameters such as rated power and maximum power, and physical quantities such as flow rate, pressure, and rotational speed. Parameters, as well as environmental parameters such as temperature.
  • the power usage corresponding to each operating state is determined based on historical historical power usage of each of the power systems. Therefore, in the period before the demand forecast is performed, the actual power consumption or the actual power consumption of each power system before the execution of this prediction is collected to determine the corresponding operation state changes by machine learning.
  • the change of the operating state refers to a change process of the power system from the operating state A1 to the operating state A2, which includes the sequence of operating states A1 and A2.
  • the conveyor device adjusts from the standby state to the transmission state as the operating state change from the standby state to the transmission state.
  • the conveyor device is stopped according to the predetermined conveyor device.
  • the power consumption is zero, and the running state of the power consumption from low to high is the stop state, the standby state, and the transmission state, and the amount of power consumption corresponding to the change of each operating state of the conveyor device is determined.
  • the operating state and the corresponding actual power consumption determine the operating state of the power system and the corresponding power consumption.
  • the manner in which the amount of power consumption change corresponding to each operating state change is determined by the machine learning manner includes but is not limited to the following examples:
  • variable point sequence of the power system and the actual power usage subsequence corresponding to each change point are obtained by accumulating for a period of time; according to the monitored power consumption parameters of each power device in the power system, and each power consumption The electrical characteristics of the main electrical components in the device, and the characteristic analysis of the actual power usage subsequences are performed to obtain the actual amount of power consumption change corresponding to the change of the operating state of each powered device.
  • variable point sequence of the power system and the actual power usage subsequence corresponding to each change point are obtained by accumulating for a period of time; the actual power usage subsequences are clustered and classified, and then the actual use of the same classification is performed.
  • the change of the operating state is matched with the actual amount of power consumption change of each class, so that the actual amount of power consumption corresponding to each power running state change is obtained.
  • the correspondence is stored on the storage server including the database for the prediction module 22 to read.
  • the power consumption corresponding to the operating state of each power system is not fixed, and is affected by the power system loss, maintenance, etc., and the power consumption corresponding to the operating states of the power systems.
  • the amount may be updated periodically or prior to execution of the prediction module 22.
  • the demand forecasting system further includes an update module (not shown), that is, updating the respective operating states of the plurality of power systems based on the historical power consumption of each of the power systems in at least the last power cycle. Correspondence with electricity consumption.
  • the update module may obtain the updated correspondence according to the foregoing corresponding relationship determining manner, and details are not described herein again.
  • the update module is further configured to calculate a historical power consumption of each of the power systems in at least one last power cycle, and a preset loss parameter of each power system, and the power related information. At least one of determining a correspondence between respective operating states of the plurality of power systems and the amount of power used.
  • the power-related information includes inspection and maintenance information, power usage plans, and the like of the electrical equipment in the power system. Among them, the loss parameter of the electrical equipment itself, the maintenance operation performed by the technician, and the duration and operating state changes of the operating states of the power system generated by the power consumption plan, the personnel information participating in the power consumption plan, etc. It will affect the corresponding relationship between the operating status of various power systems and the power consumption including the line equipment and the power equipment with flexible load.
  • the update module quantizes each of the influencing factors determined based on the power-related information, and serves as a constraint parameter for determining the power consumption corresponding to each operating state of the plurality of power systems, and utilizes the latest historical power consumption.
  • the quantity is updated to process the corresponding relationship to obtain a power consumption corresponding to the respective operating states of the real power system.
  • the quantized data of the loss of each power system itself can be determined based on the loss curve; the quantitative parameter of the inspection and maintenance of each power system can be determined based on the change of power consumption before and after the historical inspection maintenance.
  • the quantization parameter of the power usage plan may be determined based on the change in power usage of a plurality of power usage plans that have been historically executed.
  • each of the above-mentioned quantization parameters may be used as at least one of a weight, an offset, and a parameter interval threshold for determining each power consumption.
  • the prediction module 22 is started after determining the power consumption corresponding to the operating state of each power system by using the at least one quantization parameter, that is, using the correspondence relationship to predict the power usage of each of the power systems.
  • the time-series information of the expected total power consumption of the power usage plan is executed under information constraints.
  • the prediction module 22 may predict to perform in a power cycle according to at least scheduling information (hereinafter referred to as first scheduling information) provided in the power usage plan and the correspondence relationship.
  • the first timing information of the expected total amount of power used by the power usage plan.
  • the prediction module 22 determines the order of change of the operating state of the phase-applied electrical device according to the execution order of the powered devices in each power-distributing system arranged according to the first scheduling information; according to the determined operation of the powered device a sequence of state changes, determining an order of operating state combinations belonging to each of the consumers in the same power system; and predicting, according to the correspondence, a first amount of expected power consumption of the power plan in a power cycle Timing information.
  • the first timing information Pz is the sum of the timing information Pd1 of the expected power consumption of the electric device D1 and the timing information Pd2 of the expected power consumption of the electric device D2.
  • the manner of predicting the first time series information is only an example, and the power consumption related information may further include usage plan information of each power system as a flexible load, such as a lighting system, an air conditioning system, and an elevator system.
  • the forecasting module 22 predicts the order of the hard state changes during the operation of each power system including the flexible load based on the information described above, the weather forecast information, the maintenance information of the power equipment, the personnel information, and the like.
  • First timing information including timing information.
  • the weather forecast information includes weather forecast information within a power usage period issued by the meteorological department, or weather forecast information predicted based on historical weather information.
  • the personnel information includes the number of personnel directly involved in scheduling, and the number of personnel indirectly participating in the scheduling (such as the number of office workers).
  • the maintenance information of the powered device includes maintenance information that has been currently maintained, and/or a maintenance plan within a phase of the applied power cycle, and the like.
  • the prediction module 22 may determine timing information of the expected power consumption of the power system of the flexible load according to the foregoing information, and superimpose the timing information of all the obtained types of power systems to obtain the predicted expected power consumption. The first timing information of the quantity.
  • the information of each scheduling may originate from different scheduling departments, and the lack of horizontal communication between the scheduling departments leads to the concentration of scheduling information. In a short period of time to the power cycle, this causes the predicted first timing information to have an excessive peak maximum value in the corresponding time period, while the total amount of power usage in other time periods is too small.
  • the prediction module 22 performs the following steps when performing the prediction of the expected total power consumption:
  • step S121 at least second scheduling information determined based on the power usage related information is adjusted based on the correspondence.
  • the scheduling information directly provided by the power-related information is the first scheduling information, and the prediction module 22 adjusts the first scheduling information according to the power usage plan to obtain the second scheduling information.
  • the prediction module 22 can also derive a usage plan of a power system such as a lighting system or an air-conditioning system.
  • the usage plan information of the lighting system and the air-conditioning system in the production area is determined; the number of personnel in the office area and the weather forecast information are determined according to the information on the electricity-related information. Information on the use of lighting systems and air conditioning systems in the office area.
  • the usage plan information of the elevator system is determined according to the commuting time period of the power-related information, the shift time period, and the like.
  • Step S122 is performed as input information in the order of change in the operating state of the power system corresponding to each of the second scheduling information determined based on the power-related information and the usage plan of each power system.
  • step S122 timing information of the expected total amount of power consumption during the execution of the power usage plan in a power usage period is predicted based on the adjusted at least second scheduling information.
  • the prediction manner is the same as or similar to the foregoing manner of predicting the first timing information, and is not described in detail herein.
  • the steps of adjusting the power-related information and predicting the timing information of the total amount of expected power are repeated until the second timing information superior to the first timing information is obtained.
  • the number of repetitions is determined according to a preset convergence condition or a repetition number threshold.
  • the convergence condition includes, but is not limited to, a minimum of the electricity cost reflected by the time series information of the total expected power consumption, a minimum fluctuation of the expected total power consumption, and a minimum peak value of the expected total power consumption.
  • timing information of the corresponding expected total power consumption is used as the second timing information.
  • the prediction module 22 adjusts the scheduling information, the usage plan information of the flexible load, and the like according to the power consumption plan, and performs time series information of the expected total power consumption according to the adjusted information to obtain the expected power consumption.
  • the timing information of the minimum fluctuation of the quantity and/or the peak value of the expected total amount of power consumption is minimized and fed back to the relevant personnel of the enterprise for the relevant personnel to adjust the scheduling information based on the predicted timing information.
  • the electricity price standard sets a plurality of electricity price gradients according to the accumulated electricity consumption, the power consumption peak and the low valley
  • the prediction module 22 can obtain the electricity price information including the electricity price gradient from the third-party system, and then obtain the electricity price information according to the acquired information.
  • the time limit in the electricity plan is predicted to be the order of change of the operating state corresponding to the start of each power system, gear adjustment, stop, etc., such as the production activities corresponding to the completion of the power plan, and is subject to the price information including the electricity price gradient. Selecting one of the operating state change orders of the plurality of sets of candidate power systems to make the timing information having the lowest power price as the second timing information; and determining the second scheduling information corresponding to the adjusted second timing information, Various usage plan information, etc.
  • the enterprise has a self-powered system inside.
  • the self-powered system includes but is not limited to: a photovoltaic power generation system, a heat conversion system, an energy storage system, a triple supply system, a wind power generation system, and the like.
  • the company's self-powered system can also be included in the scope of forecasting electricity demand.
  • the demand prediction method further includes predicting a power supply amount of the self-power supply system based on the power-related information, and compensating the total amount of expected power within the time-series information by using the power supply amount, so as to be based on the compensated
  • the timing information determines the step of the expected power demand.
  • the prediction module 22 estimates, based on the weather forecast information in the power consumption related information, the peak of the predicted time series information and the time period when the electricity price is high. The amount of power is supplied, and the estimated amount of power supply is compensated for the expected amount of power used in the corresponding period of time within the timing information to obtain compensated timing information.
  • the demand forecasting system should also consider the charging plan information and the discharging plan information of the energy storage system in the self-powered system when predicting the timing information of the expected total power consumption. For this reason, the first timing information and the second timing are predicted.
  • the charging process of the energy storage system can also be regarded as a power consumption process of the power system, and the charging plan information of the energy storage system determined based on the power consumption related information is used to predict the power consumption of the energy storage system. Timing information, and the sum of the predicted timing information of each power usage system is determined as the timing information of the predicted total expected power consumption.
  • the prediction module 22 estimates the amount of power converted in the corresponding time period based on the predicted period of time in which the power system discharges thermal energy, and based on the preset thermal conversion rate, and The converted power supply amount is compensated to the time period during which the thermal energy is discharged to obtain compensated timing information.
  • the prediction module 22 can estimate the electrical energy that the thermal conversion system can convert according to the above manner, and store the electrical energy in the storage according to the storage loss rate estimation of the energy storage system.
  • the amount of power in the system; the amount of power supplied from the power supply system during the peak period of the predicted timing information and the period when the power price is high, and the estimated power supply amount is compensated for the expected amount of power used in the corresponding time period in the time series information, To obtain the compensated timing information.
  • the manner of predicting the timing information of the expected total power consumption by using the power supply amount of the self-power supply system is merely an example, and is not a limitation of the present application.
  • the power supply amount predicted by the prediction module 22 is determined directly or indirectly according to the power-related information.
  • the extended The manner in which the power-related information is used to predict the amount of power supplied from the power supply system, and the manner in which the predicted timing information is compensated using the predicted power supply amount is considered as a specific example of the present application.
  • the prediction module 22 is further configured to determine an expected power demand of the power cycle based on timing information of the total amount of expected power consumption, after predicting timing information in a power usage cycle.
  • the prediction module 22 may further assist the enterprise to set the expected power demand according to the peak maximum value in the timing information, and the enterprise may use the expected The electricity demand reports the contractual demand to the power supply company.
  • the prediction module 22 can provide the predicted at least one timing information and the corresponding scheduling information to the enterprise.
  • FIG. 7, is shown as an interface diagram showing curves of first timing information and second timing information
  • the prediction module 22 plots the predicted timing information into a curve, and peaks on the curve. The value is displayed on the corresponding display interface.
  • the expected power demand corresponding to the respective timing information may also be displayed on the display interface.
  • the expected power demand may be obtained by a peak maximum value of the corresponding timing information or by amplifying the corresponding peak maximum value according to a preset ratio.
  • the demand forecasting system further selects an expected power demand from the expected power consumption timing information based on the fluctuation condition of the expected power consumption timing information.
  • the fluctuation condition includes, but is not limited to, a deviation between peaks in the expected power consumption timing information, a duration of each peak in the expected power consumption timing information, and the like.
  • the expected power demand may be obtained by a peak maximum value of the corresponding timing information or by amplifying the corresponding peak maximum value according to a preset ratio.
  • the prediction module 22 can also determine an expected power demand based on the fluctuation condition and in combination with the electricity price information.
  • the prediction module 22 may combine the power price payment and penalty criteria, and the fluctuation selection of the expected power consumption timing information.
  • the amount of electricity used at the peak maximum is taken as the expected electricity demand.
  • the enterprise related personnel may adjust corresponding scheduling information according to various information generated by the demand forecasting system according to the electricity cost, and then feed back the power-related information including the adjusted scheduling information to the
  • the demand prediction system is used by the acquisition module 21 and the prediction module 22 to again predict the expected power demand of the power cycle to obtain an accurate reference value of the contract demand.
  • the present application also provides a demand control method.
  • the demand control method can be performed by a demand control system.
  • FIG. 9 is a schematic structural diagram of the demand control system.
  • the demand control system may include a computer device 31 for performing the following steps and at least one metering device 32 for providing the cumulative power usage by the computer device 31.
  • the computer device 31 obtains the accumulated power consumption of the power system connected thereto by using each metering device 32, and calculates the instantaneous actual power consumption of each of the monitored power systems by using the accumulated power consumption obtained each time. The amount and / or the actual amount of electricity used.
  • the computer device 31 performs appropriate regulation based on the configured software and hardware to make the total amount of actual power consumption close to or reach the expected power demand, so that the actual power consumption of the power system is lower than the expected power demand. This effectively reduces the cost of electricity used in production activities.
  • the expected power demand may be the contract demand mentioned in the foregoing examples or the expected power demand predicted by the demand forecasting method.
  • the computer device may be a device located in an enterprise's power control room, or a server in the Internet.
  • the server includes but is not limited to a single server, a server cluster, a distributed server farm, a cloud server, and the like.
  • the cloud server includes a public cloud server and a private cloud server, where the public or private cloud server includes Software-as-a-Service (software as a service, SaaS). ), Platform-as-a-Service (Platform as a Service, PaaS) and Infrastructure-as-a-Service (Infrastructure as a Service, IaaS).
  • the private cloud server is, for example, an Facebook Cloud computing service platform, an Amazon cloud computing service platform, a Baidu cloud computing platform, a Tencent cloud computing platform, and the like.
  • the computer equipment is connected with the power control system of the enterprise, the management system of the production activity, and the like, and can even connect the third-party system with the data, and obtain the Internet data related to the power consumption of the enterprise in the Internet by using the crawler technology.
  • the power control system includes, but is not limited to, a metering device (such as a power meter) installed in an enterprise, an electrical equipment control system, and the like.
  • the management system includes, but is not limited to, a MES (Manufacturing Execution System), an Enterprise Resource Planning (ERP), and the like.
  • the third-party system examples include a self-owned WEB server for storing a historical power data server, for acquiring an enterprise power consumption plan, and the like.
  • Examples of the Internet data include weather forecast data and the like, wherein the weather forecast data is obtained from a weather website or other website.
  • FIG. 10 is a schematic structural diagram of the computer device.
  • the computer device includes an interface unit 41, a storage unit 42, and a processing unit 43. Similar to the unit hardware described in the server corresponding to FIG. 10, details are not described herein again. In conjunction with FIG. 9 and FIG.
  • the interface unit 41 in the computer device 31 is connected to each system, third-party system, and Internet data in the enterprise to obtain power-related information corresponding to the currently executed power consumption plan;
  • the unit 43 is connected to the interface unit 41 and the storage unit 42, and the acquired power-related information is stored in the storage unit through the interface unit 41, and at least one program is still stored in the storage unit 42; At least one program is described to coordinate the interface unit 41 and the storage unit 42 to perform the following demand control method.
  • FIG. 11 is a flowchart of the demand control method in an embodiment.
  • the demand control system performs the following steps by using the obtained power-related information of each party to monitor the actual power consumption of each power system in real time, and adjusts various power systems in the enterprise based on the expected power demand. And/or self-powered systems to limit the actual amount of electricity used below the expected electricity demand as much as possible.
  • the computer equipment pre-stores the power-related information including the power consumption plan.
  • the power consumption plan refers to at least one of a production plan, an activity plan, and a business plan that are predicted by the enterprise in the applicable application cycle.
  • the power usage plan includes, but is not limited to, production or activity time limits, electrical equipment used for production or activities, and the like.
  • the power usage related information may further include at least one of the following: scheduling information, personnel information, weather prediction information, equipment maintenance information, and electricity price information.
  • the scheduling information is information including a schedule calculation date and time, and resource configuration information determined based on a production order, a resource, a shift, a holiday, an exception shift, a bill of materials, a job priority, and the like.
  • the power usage related information may further include at least one of the following: personnel information, weather prediction information, maintenance information of the power system, electricity price information, and execution order of each power system.
  • the information of the planning activity is information including an event date, a resource configuration, and the like, which are determined to meet the corporate celebration, the holiday promotion, and the like.
  • electricity-related information In addition to the above-mentioned electricity-related information,
  • step S210 during the execution of the power usage plan, the actual power consumption of the plurality of powered systems being operated is monitored.
  • the enterprise's power consumption system not only includes the power system that directly executes the production activities, but also includes the lighting system that provides lighting and temperature control for the production activities, and provides electricity for the office living area. Power system.
  • the demand control system is connected with the enterprise's metering device (such as a power meter) or the power total control system data, and obtains the total amount of actual power used by all the power systems in the enterprise.
  • the total amount of actual electricity used is the total amount of instantaneous electricity used in the unit sampling period.
  • the power system of each or one zone is connected to the metering device, and the demand control system reads the actual power consumption cumulative value of the metering device at intervals, and accumulates the actual power consumption according to the two readings.
  • the value and the time interval read calculate the actual power consumption of the power system in the unit sampling period, and all the actual power consumption are summed to obtain the total amount of the actual power.
  • all of the power systems are connected to the same metering device, and the demand control system calculates the unit sampling duration of each power system based on the actual power consumption values read twice and the time interval read. The actual amount of electricity used.
  • step S220 based on the acquired power-related information including the power usage plan and the monitored actual power consumption amount, the power consumption total time series information of the power consumption plan that has not been executed is predicted.
  • the demand control system determines, according to the scheduling information in the power-related information, each power system currently used to execute the power consumption plan and its operating state, and predicts according to the executed power plan part.
  • the time series information of the total power consumption refers to the sequence information of the total amount of power used by each power system over time.
  • the demand control system can obtain the following information by analyzing the scheduling information: the start and end dates and specific times of the power plan, the power consumption system of the power plan, and the operation of each power system during the execution period. State change order and change time.
  • the demand control system determines current and subsequent power consumption systems and their operating states by comparing system time and scheduling information; according to preset operating states of respective power systems and power consumption The relationship, the operating state change order, and the change time, and timing information of the total amount of power used during the portion of the power plan to be executed subsequently.
  • the operating state of the power system includes a combination of operating states of all powered devices in the power system.
  • the operating state of the powered device refers to the operation of the motor, the controller, and the like in the electrical device operating in at least one state and maintaining the corresponding state.
  • the air-conditioning device includes a plurality of air-conditioning devices including a standby mode, a fresh air mode, a cooling mode, a heating mode, and the like, and each type is based on an air volume, a cooling (heat) temperature, and the like.
  • the mode includes at least one operational state, the operational state of the electrical system including a combination of reasonable operational states of each air conditioning device.
  • the lighting device only includes an open state and an off state, and the power system including the lighting device is in an open state for each lighting device and A combination of off states. Technicians should determine the appropriate operating status based on the ability of the powered device to maintain operational capability.
  • the power consumption of each power system pre-stored in the demand control system in a corresponding operating state is obtained in advance by simulating each operating state of the power device in some specific examples, or according to the power parameters of the device.
  • the parameters of the electrical equipment are used to simulate the power consumption of the electrical equipment in each operating state, wherein the parameters include, but are not limited to, electrical parameters such as rated power and maximum power, physical parameters such as flow rate, pressure, and rotational speed. And environmental parameters such as temperature.
  • the power usage corresponding to each operating state is determined based on historical historical power usage of each of the power systems.
  • the respective power consumption or the total amount of electricity used by each power system before the execution of the current control is collected to determine the power consumption corresponding to each operating state change through machine learning.
  • the change of the operating state refers to a process of changing the operating state from the operating state A1 to the operating state A2, which includes the sequence of operating states A1 and A2.
  • the conveyor device adjusts from the standby state to the transmission state as the operating state change from the standby state to the transmission state.
  • all the operating states of the powered device are determined in accordance with the order of change in the operating state based on the amount of power used in the stopped state of the powered device.
  • Corresponding power consumption For example, on the basis of determining the amount of change in the amount of power consumption corresponding to the change in the operating state of the conveyor device from the standby state to the transmission state, the power consumption is zero according to the predetermined time when the conveyor device is in the stop state, and the power consumption is low.
  • the high operating state is a stop state, a standby state, and a transfer state, and the respective power consumptions obtained before and after the change of the operating state are determined, and the power consumption corresponding to the conveyor device standby state and the transmission state is determined. .
  • the manner in which the amount of power consumption change corresponding to each operating state change is determined by the machine learning manner includes but is not limited to the following examples:
  • the change point sequence of the power system and the power usage sub-sequence corresponding to each change point are obtained by accumulating for a period of time; according to the monitored power consumption parameters of each power device in the power system, each power device In the electrical characteristics of the main electrical components, the characteristic analysis of each power usage subsequence is performed to obtain the amount of power consumption change corresponding to the change of the operating state of each electrical device.
  • the change point sequence of the power system and the power usage subsequence corresponding to each change point are obtained by accumulating for a period of time; the power usage subsequences are clustered, and then the power consumption subsequence of the same classification is obtained.
  • the operating state of each power device is changed The amount of change in the amount of electricity used in each category is matched, so that the amount of change in the amount of power consumption corresponding to the change in the state of each power operation is obtained.
  • the demand control system needs to estimate the order of change and the change time of the running state of each power system.
  • the demand control system continuously monitors the actual power consumption of each of the power systems; based on the continuously monitored timing information of the actual power usage and the operational state of the preset power system Corresponding relationship of the electric quantity determines the current operating state of the electric power system.
  • the demand control system may detect an operating state change included in the timing information of the actual power consumption that is continuously monitored for a period of time according to the amount of power consumption change corresponding to each operating state change determined in advance; Then, the operating state before and after the change of the operating state of the power system is determined according to the determined correspondence between the operating state of the power system and the power consumption, thereby determining the current operating state of the power system.
  • the power system including the conveyor belt device as an example, on the basis of determining the actual amount of power consumption change corresponding to the change of the operating state of the conveyor belt device from the standby state to the transmission state, the power is determined according to the predetermined conveyor belt device in the stop state.
  • the quantity is zero, and the running state of the power consumption from low to high is the stop state, the standby state, and the transmission state, and the amount of power consumption corresponding to the change of the operating state of each conveyor device, determining that the conveyor device is currently in Standby state and corresponding actual power consumption.
  • the demand control system predicts the power consumption timing information B1 of the power system directly related to the production activity during the subsequent power consumption plan period according to the rough scheduling information in the power consumption related information, according to The weather forecast information, the number of personnel, etc. are predicted to maintain the power consumption time series information B2 of the power system in order to maintain the indoor temperature of the factory area and the office living area; and the plurality of power consumption time series information B1 and B2 are superimposed to obtain the expected power consumption.
  • the total time series information is the power consumption timing information B1 of the power system directly related to the production activity during the subsequent power consumption plan period according to the rough scheduling information in the power consumption related information, according to The weather forecast information, the number of personnel, etc. are predicted to maintain the power consumption time series information B2 of the power system in order to maintain the indoor temperature of the factory area and the office living area; and the plurality of power consumption time series information B1 and B2 are superimposed to obtain the expected power consumption.
  • the total time series information is the power consumption timing information B1 of the power system directly related to
  • the step S220 includes the step of predicting the power consumption total time series information of the at least one unit prediction duration according to the power consumption related information and the monitored actual power consumption total amount.
  • the unit prediction duration refers to the minimum interval of prediction. For example, if the unit prediction duration is five minutes (or any other duration), the demand control system predicts the total amount of power usage timing information within at least one of five minutes.
  • the manner in which the demand control system predicts the power consumption timing information of the at least one unit prediction duration is the same as or similar to the manner of predicting the power consumption total timing information during the non-execution period of the power usage plan, where No longer.
  • the demand control system performs step S230.
  • step S230 when the predicted total power consumption timing information approaches or reaches a preset expected power demand, the power consumption regulation is performed according to the current monitored total power consumption.
  • FIG. 12 is a schematic diagram showing the total amount of electricity used and the expected power demand on the predicted total electricity timing information.
  • the demand control system compares the predicted total electricity consumption and the expected electricity demand one by one; when there is a predicted total electricity consumption time series information, the difference between the total electricity consumption and the expected electricity demand is less than The preset warning deviation threshold, or when the total amount of power consumption is greater than the expected power demand, the demand control system performs power consumption regulation according to the current total amount of electricity used for monitoring.
  • the manner of performing power consumption regulation according to the total amount of actual power usage currently monitored includes at least one of the following examples:
  • the operating state of the at least one power system is adjusted according to the current operating state of each of the powered systems to reduce the actual amount of power used in the corresponding period.
  • the operating state of the lowest priority power system may be adjusted according to a preset priority; the demand control system is monitored until The actual total amount of electricity used, or again predicted that the timing information of the total electricity consumption is not close to the preset expected electricity demand.
  • the demand control system can also re-adjust the operating state of the adjusted power system according to the priority and high to low callback. For example, the demand control system adjusts the air conditioning system with the lowest priority from the heating (or cooling) state to the operating state, the standby state or the power-off state of the power saving mode according to the preset priority, and monitors the actual power consumption again. The total amount, if the difference between the actual total electricity consumption and the expected electricity demand is greater than the preset warning deviation threshold, then re-adjust the air conditioning system to the heating (or cooling) state.
  • the demand control system may also control the self-powered system to compensate power supply lines of each of the power systems to reduce the total power consumption of the corresponding period. the amount.
  • the self-powered system includes but is not limited to: a photovoltaic power generation system, a heat conversion system, an energy storage system, a triple supply system, a wind power generation system, and the like.
  • the demand control system controls the self-powered system to compensate power supply to the power supply lines of each of the power systems to reduce the actual power consumption in the corresponding period.
  • the demand control system determines that the predicted total power consumption timing information approaches, reaches, or exceeds a preset expected power demand, selects at least one self-powered according to the corresponding compensation gap and the power supply amount of the respective power supply system.
  • the system supplies power to the power supply lines of each power system to reduce the actual power consumption in the corresponding period.
  • the respective power supply system can be regarded as a system capable of stably supplying power.
  • the demand control method further includes the step of predicting the amount of power supplied by the self-powered system based on the power-related information. For example, if the self-powered system includes at least one of a photovoltaic power generation system and a wind power generation system, the demand prediction system estimates a peak value and a power price of the predicted time-series information based on weather forecast information in the power-related information.
  • the self-powered system includes at least one of a thermal conversion system and an energy storage system, and the demand prediction system estimates the conversion that the thermal conversion system can convert according to the period in which the thermal system discharges thermal energy in the predicted timing information.
  • the electrical energy, and the electrical energy stored in the energy storage system are estimated according to the storage loss rate of the energy storage system; when the self-powered system is controlled to supply power based on the above prediction, at least one self-powered system is controlled to supply power to the power supply lines of the respective power systems.
  • the total amount of actual electricity monitored by the demand control system further includes the power consumption consumed by the energy storage system during the charging process.
  • the demand control system selects the charging process of the suspended energy storage system according to the charging plan information in the power-related information when determining that the total power consumption timing information approaches or reaches a preset expected power demand. Until the peak maximum value of the re-predicted power consumption total timing information is greater than the preset warning deviation threshold from the expected power demand.
  • the manner in which the power supply compensation is performed by using the power supply amount of the self-power supply system is merely an example, and is not a limitation of the present application.
  • the power supply quantity predicted by the demand control system is determined directly or indirectly according to the power-related information.
  • the basis based on the technical idea is extended.
  • the manner in which the power-related information is used to predict the amount of power supplied from the power supply system and the power supply compensation using the predicted power supply amount is considered as a specific example of the present application.
  • the demand control system may further determine the total power consumption timing based on determining the preset expected power demand in the total power consumption timing information.
  • the total amount of electricity in the information that meets the above conditions is the predicted time interval from the current time; and the power consumption is adjusted based on the predicted duration and the total amount of actual power currently being monitored.
  • the demand control system compares the total amount of power used in the predicted total power consumption time information with the expected power demand, and determines the total amount of power used and the expected power demand.
  • the prediction duration from the current time to the predicted total applied power is determined.
  • the predicted duration is less than a duration threshold, it is generally considered that the expected power demand is quickly reached, and then the self-powered system is controlled to perform power supply compensation; otherwise, it is generally considered that there is a controllable time, and then at least one power is adjusted.
  • the operating state of the system to reduce the actual amount of electricity used in the corresponding period.
  • the above-mentioned manner of adjusting based on the prediction duration is merely an example, and is not a limitation of the present application.
  • the demand control process is performed in any of a variety of ways. Under the guidance of the technical idea, those skilled in the art adopting any of the above-mentioned control modes or the improvement of any of the above-mentioned control modes should be regarded as a specific example of the present application.
  • any of the above methods for regulating the power consumption can directly adjust the phase application power system and the self-power supply system by using the data connection between the demand control system and the enterprise power control system;
  • the power system and the self-powered system are provided to the technical personnel of the enterprise in the form of interface prompts, so that the technical personnel of the enterprise perform the control operation according to the prompts.
  • the correspondence relationship described in the foregoing is various implementations determined based on historically acquired historical power usage of each of the power systems, in order to more accurately obtain actual power usage from one or more metering devices
  • the demand control method further includes The step of updating the correspondence relationship of the historical power consumption of each of the power systems is monitored.
  • the demand control system also stores the monitored actual power consumption and the actual power consumption into a corresponding database.
  • the demand control system uses the preset algorithm for determining the correspondence between the operating states of each power system and the power consumption, and may even include the accumulated actual power consumption and the actual power consumption.
  • the acquired power-related information and the like are input into the algorithm to obtain an updated correspondence.
  • the demand control system may also issue a corresponding update instruction to the system running the algorithm after storing the data for execution of the update operation.
  • the updated correspondence will be retrieved when the demand control system is adjusted in real time, and will not be described in detail here.
  • the application also provides a demand control system.
  • the demand control system is a software system installed in a computer device. Please refer to FIG. 13, which is shown as an architectural diagram of the demand control system in an embodiment.
  • the demand control system 5 includes program modules such as a monitoring module 51, a prediction module 52, and a regulation module 53.
  • the demand control system presets the power-related information including the power consumption plan.
  • the power consumption plan refers to at least one of a production plan, an activity plan, and a business plan that are predicted by the enterprise in the applicable application cycle.
  • the power usage plan includes, but is not limited to, production or activity time limits, electrical equipment used for production or activities, and the like.
  • the power usage related information may further include at least one of the following: scheduling information, personnel information, weather prediction information, equipment maintenance information, and electricity price information.
  • the scheduling information is information including a schedule calculation date and time, and resource configuration information determined based on a production order, a resource, a shift, a holiday, an exception shift, a bill of materials, a job priority, and the like.
  • the power usage related information may further include at least one of the following: personnel information, weather forecast information, maintenance information of the power system, power price information, and execution order of each power system.
  • the information of the planning activity is information including an event date, a resource configuration, and the like, which are determined to meet the corporate celebration, the holiday promotion, and the like.
  • the monitoring module 51 monitors the actual amount of actual power used by the plurality of powered systems being operated during the execution of the power usage plan.
  • the enterprise's power consumption system not only includes the power system that directly executes the production activities, but also includes the lighting system that provides lighting and temperature control for the production activities, and provides electricity for the office living area. Power system.
  • the monitoring module 51 is connected with the metering device of the enterprise (such as a power meter) or the data of the total power control system, and obtains the total amount of actual power used by all the power systems in the enterprise.
  • the total amount of actual electricity used is the total amount of instantaneous electricity used in the unit sampling period.
  • the electrical system of each or one zone is connected to the metering device, and the monitoring module 51 reads the actual power accumulation value of the metering device at intervals and according to the actual power consumption value read twice. And the time interval read is used to calculate the actual power consumption of the power system in unit sampling time, and all the actual power consumption is summed to obtain the total amount of the actual power.
  • all the power systems are connected to the same metering device, and the monitoring module 51 calculates the unit sampling duration of each power system according to the actual power consumption value read twice and the time interval read. The total amount of electricity actually used.
  • the prediction module 52 is configured to predict, according to the acquired power-related information including the power usage plan and the monitored actual power consumption amount, the power consumption total time series information in the power consumption plan that has not been executed during the period.
  • the prediction module 52 determines, according to the scheduling information in the power-related information, each power system currently used to execute the power usage plan and its operating state, and performs partial prediction based on the executed power consumption plan.
  • the electricity consumption plan part of the total electricity consumption timing information.
  • the time series information of the total power consumption refers to the sequence information of the total amount of power used by each power system over time.
  • the prediction module 52 can obtain the following information by analyzing the scheduling information: the start and end dates and specific times of the power plan, the power consumption system of the power plan, and the operating state of each power system during the execution period. Change order and change time.
  • the prediction module 52 determines the current and subsequent power consumption systems and their operating states by comparing the system time and scheduling information; according to the preset correspondence between the operating states of the respective power systems and the power consumption And the running state change order and the change time, and predicting the time series information of the total amount of power used during the part of the power plan to be executed subsequently.
  • the operating state of the power system includes a combination of operating states of all powered devices in the power system.
  • the operating state of the powered device refers to the operation of the motor, the controller, and the like in the electrical device operating in at least one state and maintaining the corresponding state.
  • the air-conditioning device includes a plurality of air-conditioning devices including a standby mode, a fresh air mode, a cooling mode, a heating mode, and the like, and each type is based on an air volume, a cooling (heat) temperature, and the like.
  • the mode includes at least one operational state, the operational state of the electrical system including a combination of reasonable operational states of each air conditioning device.
  • the lighting device only includes an open state and an off state, and the power system including the lighting device is in an open state for each lighting device and A combination of off states. The technician should determine the corresponding operating status based on the electrical equipment's ability to maintain operation.
  • the power consumption of each of the power systems pre-stored in the prediction module 52 in the corresponding operating state is obtained in advance by simulating the operating states of the power device in some specific examples, or is calculated according to the power parameters of the device. And got it.
  • the parameters of the electrical equipment are used to simulate the power consumption of the electrical equipment in each operating state, wherein the parameters include, but are not limited to, electrical parameters such as rated power and maximum power, physical parameters such as flow rate, pressure, and rotational speed. And environmental parameters such as temperature.
  • the power usage corresponding to each operating state is determined based on historical historical power usage of each of the power systems.
  • the respective power consumption or the total amount of electricity used by each power system before the execution of the current control is collected to determine the power consumption corresponding to each operating state change through machine learning.
  • the change of the operating state refers to a process of changing the operating state from the operating state A1 to the operating state A2, which includes the sequence of operating states A1 and A2.
  • the conveyor device adjusts from the standby state to the transmission state as the operating state change from the standby state to the transmission state.
  • all the operating states of the powered device are determined in accordance with the order of change in the operating state based on the amount of power used in the stopped state of the powered device.
  • Corresponding power consumption For example, on the basis of determining the amount of change in the amount of power consumption corresponding to the change in the operating state of the conveyor device from the standby state to the transmission state, the power consumption is zero according to the predetermined time when the conveyor device is in the stop state, and the power consumption is low.
  • the high operating state is a stop state, a standby state, and a transfer state, and the respective power consumptions obtained before and after the change of the operating state are determined, and the power consumption corresponding to the conveyor device standby state and the transmission state is determined. .
  • the manner in which the amount of power consumption change corresponding to each operating state change is determined by the machine learning manner includes but is not limited to the following examples:
  • the change point sequence of the power system and the power usage sub-sequence corresponding to each change point are obtained by accumulating for a period of time; according to the monitored power consumption parameters of each power device in the power system, each power device In the electrical characteristics of the main electrical components, the characteristic analysis of each power usage subsequence is performed to obtain the amount of power consumption change corresponding to the change of the operating state of each electrical device.
  • the change point sequence of the power system and the power usage subsequence corresponding to each change point are obtained by accumulating for a period of time; the power usage subsequences are clustered, and then the power consumption subsequence of the same classification is obtained.
  • the operating state of each power device is changed The amount of change in the amount of electricity used in each category is matched, so that the amount of change in the amount of power consumption corresponding to the change in the state of each power operation is obtained.
  • the prediction module 52 needs to estimate the operating sequence change order and change time of each utility system.
  • the prediction module 52 continuously monitors the actual power consumption of each of the power systems; based on the continuously monitored timing information of the actual power usage and the preset operating state and power consumption of the power system The corresponding relationship between the quantities determines the current operating state of the power system.
  • the prediction module 52 may detect an operating state change included in the timing information of the actual power consumption that is continuously monitored for a period of time according to the amount of power consumption change corresponding to each operating state change determined in advance; According to the determined correspondence between the operating state of the power system and the power consumption, the operating state before and after the change of the operating state of the power system is determined, thereby determining the current operating state of the power system.
  • the power is determined according to the predetermined conveyor belt device in the stop state.
  • the quantity is zero, and the running state of the power consumption from low to high is the stop state, the standby state, and the transmission state, and the amount of power consumption corresponding to the change of the operating state of each conveyor device, determining that the conveyor device is currently in Standby state and corresponding actual power consumption.
  • the prediction module 52 predicts the power consumption timing information B1 of the power system directly related to the production activity during the subsequent power consumption planning period according to the rough scheduling information in the power consumption related information, according to the weather.
  • the forecast information, the number of personnel, etc. are predicted to maintain the power consumption time series information B2 of the power system in order to maintain the indoor temperature of the factory area and the office living area; and the plurality of power consumption time series information B1 and B2 are superimposed to obtain the expected power consumption.
  • a quantity of timing information is predicted to maintain the power consumption time series information B2 of the power system in order to maintain the indoor temperature of the factory area and the office living area.
  • the step S220 includes the step of predicting the power consumption total time series information of the at least one unit prediction duration according to the power consumption related information and the monitored actual power consumption total amount.
  • the unit prediction duration refers to the minimum interval of prediction. For example, if the unit prediction duration is five minutes (or any other duration), the prediction module 52 predicts the total usage timing information for at least one of five minutes.
  • the manner in which the prediction module 52 predicts the total power consumption timing information of the at least one unit prediction duration is the same as or similar to the manner of predicting the power consumption total timing information during the non-execution period of the power usage plan, and is not here. Let me repeat. After predicting the timing information of the total amount of power consumption, the prediction module 52 passes the predicted power consumption total timing information to the regulation module 53.
  • the control module 53 performs power consumption regulation according to the current monitored total power consumption when the predicted total power consumption timing information approaches or reaches a preset expected power demand.
  • FIG. 12 is a schematic diagram showing the total amount of electricity used and the expected power demand on the predicted total electricity timing information.
  • the control module 53 compares the predicted total power consumption and the expected power demand one by one; when there is a predicted total power consumption time series information, the difference between the total power consumption and the expected power demand is less than the pre- When the warning deviation threshold is set, or when the total amount of power usage is greater than the expected power demand, the regulation module 53 performs power consumption regulation according to the current total amount of power usage monitored.
  • the manner of performing power consumption regulation according to the total amount of actual power usage currently monitored includes at least one of the following examples:
  • the operating state of the at least one power system is adjusted according to the current operating state of each of the powered systems to reduce the actual amount of power used in the corresponding period.
  • the operating state of the lowest priority power system may be adjusted according to a preset priority; the control module 53 until the actual monitoring is performed The total amount of electricity used, or re-predicted that the timing information of the total electricity consumption is not close to the preset expected power demand; after the interval is preset, the power system is re-adjusted according to the priority and the callback is adjusted. Operating status.
  • the demand control system adjusts the air conditioning system with the lowest priority from the heating (or cooling) state to the operating state, the standby state or the power-off state of the power saving mode according to the preset priority, and monitors the actual power consumption again.
  • the total amount if the difference between the actual total electricity consumption and the expected electricity demand is greater than the preset warning deviation threshold, then re-adjust the air conditioning system to the heating (or cooling) state.
  • the control module 53 can also control the self-powered system to compensate power supply lines of each of the power systems to reduce the actual power consumption in a corresponding period. .
  • the self-powered system includes but is not limited to: a photovoltaic power generation system, a heat conversion system, an energy storage system, a triple supply system, a wind power generation system, and the like.
  • the control module 53 controls the self-powered system to compensate power supply to the power supply lines of each of the power systems to reduce the actual power consumption in a corresponding period. For example, when the control module 53 determines that the predicted total power consumption timing information approaches, reaches, or exceeds the preset expected power demand, selects and controls at least one self-powered system according to the corresponding compensation gap and the power supply amount of the respective power supply system. Power is supplied to the power supply lines of each power system to reduce the actual power consumption in the corresponding period.
  • the respective power supply system can be regarded as a system capable of stably supplying power.
  • the demand control method further includes the step of predicting the amount of power supplied by the self-powered system based on the power-related information. For example, if the self-powered system includes at least one of a photovoltaic power generation system and a wind power generation system, the demand prediction system estimates a peak value and a power price of the predicted time-series information based on weather forecast information in the power-related information.
  • the self-powered system includes at least one of a thermal conversion system and an energy storage system, and the demand prediction system estimates the conversion that the thermal conversion system can convert according to the period in which the thermal system discharges thermal energy in the predicted timing information.
  • the electrical energy, and the electrical energy stored in the energy storage system are estimated according to the storage loss rate of the energy storage system; when the self-powered system is controlled to supply power based on the above prediction, at least one self-powered system is controlled to supply power to the power supply lines of the respective power systems.
  • the total amount of actual electricity monitored by the demand control system further includes the power consumption consumed by the energy storage system during the charging process.
  • the demand control system selects the charging process of the suspended energy storage system according to the charging plan information in the power-related information when determining that the total power consumption timing information approaches or reaches a preset expected power demand. Until the peak maximum value of the re-predicted power consumption total timing information is greater than the preset warning deviation threshold from the expected power demand.
  • the manner in which the power supply compensation is performed by using the power supply amount of the self-power supply system is merely an example, and is not a limitation of the present application.
  • the power supply amount predicted by the control module 53 is determined directly or indirectly according to the power-related information.
  • the extended The manner in which the power-related information is used to predict the amount of power supplied from the power supply system and the power supply compensation using the predicted power supply amount is considered as a specific example of the present application.
  • control module 53 may further determine the timing information of the total power consumption based on determining the preset expected power demand in the total power consumption timing information.
  • the total amount of electricity in accordance with the above conditions is the predicted time interval from the current time; and the power consumption is adjusted based on the predicted duration and the total amount of actual power currently being monitored.
  • control module 53 compares the total amount of power used in the predicted total power consumption time information with the expected power demand, and determines the total amount of power used and the expected power demand. When the difference is less than the preset warning deviation threshold, the prediction duration from the current time to the predicted total applied power is determined. When the predicted duration is less than a duration threshold, it is generally considered that the expected power demand is quickly reached, and then the self-powered system is controlled to perform power supply compensation; otherwise, it is generally considered that there is a controllable time, and then at least one power is adjusted. The operating state of the system to reduce the actual amount of electricity used in the corresponding period.
  • the above-mentioned manner of adjusting based on the prediction duration is merely an example, and is not a limitation of the present application.
  • the demand control process is performed in any of a variety of ways. Under the guidance of the technical idea, those skilled in the art adopting any of the above-mentioned control modes or the improvement of any of the above-mentioned control modes should be regarded as a specific example of the present application.
  • any of the above methods for regulating the power consumption can directly adjust the phase application power system and the self-power supply system by using the data connection between the regulation module 53 and the enterprise power control system;
  • the power system and the self-powered system are provided to the technical personnel of the enterprise in the form of interface prompts, so that the technical personnel of the enterprise perform the control operation according to the prompts.
  • the correspondence relationship described in the foregoing is various implementations determined based on historically acquired historical power usage of each of the power systems, in order to more accurately obtain actual power usage from one or more metering devices
  • the demand control includes And an update module, configured to update the correspondence relationship based on historical power consumption of each of the powered systems that have been monitored.
  • the monitoring module also stores the monitored actual power consumption and the actual power consumption into a corresponding database.
  • the update module uses the preset algorithm for determining the correspondence between each operating state and the power consumption of each power system, and the accumulated actual power consumption and the actual power consumption may even include the obtained Data such as power-related information is input into the algorithm to obtain an updated correspondence.
  • the update module may also issue a corresponding update instruction to the system running the algorithm after storing the data for execution of the update operation.
  • the updated correspondence will be used when the prediction module is adjusted in real time, and will not be described in detail here.
  • the demand forecasting method provided by the present application can more accurately predict the expected power demand by introducing power-related information in the power cycle, so that the enterprise can more rationally plan power consumption.
  • the present application also provides a demand control method to effectively control the power consumption efficiency of the enterprise by effectively monitoring the process of executing the power consumption plan to utilize the internal resources of the enterprise.

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Abstract

本申请提供一种需量预测方法、需量控制方法及系统。其中，所述需量预测方法包括：获取一用电周期内的包含用电计划的用电相关信息；基于预设的多个用电系统各自的运行状态与用电量的对应关系，预测各所述用电系统在所述用电相关信息约束下执行所述用电计划的预期用电总量的时序信息；基于所述预期用电总量的时序信息确定所述用电周期的预期用电需量。本申请所提供的所述需量预测方法通过引入用电周期内的用电相关信息能够更准确地预测预期用电需量，以供企业更合理规划用电。

Description

需量预测方法、需量控制方法及系统 技术领域
本申请涉及工业控制技术领域，特别是涉及一种需量预测方法、需量控制方法及系统。
背景技术
对于商场、工矿厂区等高用电需求的企业，供电公司会与其签订契约，旨在预估下一用电周期的企业用电需求并限定企业的瞬时用电量不得超出契约中签订的契约需量，对于超额部分，供电公司将会给予惩罚性电费。
对于企业来说，合理预测下一用电周期的累计用电总量和用电总量峰值能够预测用电成本，藉由所预测的用电成本能够约束企业内部用电管理是一种节源方式。然而，目前企业大多依据历史实际用电总量来预测下一用电周期的用电量。然而根据不同的用电计划其用电预测的结果可能千差万别，所预测的契约需量过高会增加用电成本，所预测的契约需量过低会降低企业在供电公司的诚信。
发明内容
鉴于以上所述现有技术的缺点，本申请的目的在于提供一种需量预测方法、需量控制方法及系统，用于解决现有技术中如何合理预测企业的用电需量，以及在所预测的用电需量基础上实现对实际用电的用电管理的问题。
为实现上述目的及其他相关目的，本申请的第一方面提供一种需量预测方法，包括：获取一用电周期内的包含用电计划的用电相关信息；基于预确定的多个用电系统各自的运行状态与用电量的对应关系，预测各所述用电系统在所述用电相关信息约束下执行所述用电计划的预期用电总量的时序信息；基于所述预期用电总量的时序信息确定所述用电周期的预期用电需量。
在本申请的第一方面的某些实施方式中，所述基于预设的多个用电系统各自的运行状态与用电量的对应关系，预测各所述用电系统在所述用电相关信息约束下执行用电计划期间的预期用电总量的时序信息的步骤包括：基于所述对应关系和所述用电相关信息中的至少第一排产信息，预测在一用电周期内执行所述用电计划的预期用电总量的第一时序信息。
在本申请的第一方面的某些实施方式中，所述基于预设的多个用电系统各自的运行状态与用电量的对应关系，预测各所述用电系统在所述用电相关信息约束下执行用电计划期间的预期用电总量的时序信息的步骤包括：基于所述对应关系调整基于所述用电相关信息而确定 的至少第二排产信息；基于调整后的至少第二排产信息预测在一用电周期内执行所述用电计划的期间的预期用电总量的时序信息；重复上述调整用电相关信息和预测预期用电总量的时序信息的步骤直至得到优于所述第一时序信息的第二时序信息。
在本申请的第一方面的某些实施方式中，所述得到优于第一时序信息的第二时序信息的步骤包括：基于所述对应关系和所述用电相关信息中的至少用电计划和电价信息，按照所述用电相关信息中的至少用电计划，选取用电费用最低、预期用电总量的波动最小、和预期用电总量的峰值最大值最小中至少一种的时序信息作为第二时序信息。
在本申请的第一方面的某些实施方式中，所述需量预测方法还包括：基于所述用电相关信息预测自供电系统的供电量；以及利用所述供电量补偿所述时序信息内的预期用电总量，以便基于补偿后的所述时序信息确定所述预期用电需量。
在本申请的第一方面的某些实施方式中，所述基于预期用电总量的时序信息确定所述用电周期的预期用电需量的步骤包括以下至少一种：将所述预期用电时序信息中的峰值最大值作为所述预期用电需量；按照预设比例放大所述预期用电时序信息中的峰值最大值以得到所述预期用电需量；基于所述预期用电时序信息的波动情况自所述预期用电时序信息中选择预期用电需量。
在本申请的第一方面的某些实施方式中，所述需量预测方法还包括基于至少上一个用电周期内各所述用电系统的历史用电量更新多个用电系统各自的运行状态与用电量的对应关系的步骤。
在本申请的第一方面的某些实施方式中，所述用电相关信息还包括以下至少一种：人员信息、天气预测信息、排产信息、各用电系统的维护信息和电价信息。
在本申请的第一方面的某些实施方式中，所述用电系统包括以下至少一种：用于生产制造的用电系统和用于生活办公的用电系统。
本申请第二方面提供一种需量控制方法，包括：在执行用电计划期间，监测所运行的多个用电系统的实际用电总量；基于所获取的包含用电计划的用电相关信息和所监测的实际用电总量，预测所述用电计划中尚未执行期间的用电总量时序信息；当所述用电总量时序信息接近或达到预设的预期用电需量时，根据当前所监测的实际用电总量进行用电量调控。
在本申请的第二方面的某些实施方式中，所述基于所获取的包含用电计划的用电相关信息和所监测的实际用电总量，预测所述用电计划中尚未执行期间的用电总量时序信息的步骤包括：根据所述用电相关信息和所监测的实际用电总量，预测后续至少一个单位预测时长的用电总量时序信息。
在本申请的第二方面的某些实施方式中，所述当用电总量时序信息接近或达到预设的预 期用电需量时，根据当前所监测的实际用电总量进行用电量调控的步骤包括：确定所述用电总量时序信息中接近或达到预设的预期用电需量的用电总量相距当前时刻的预测时长；基于所述预测时长和当前所监测的实际用电总量进行用电量调控。
在本申请的第二方面的某些实施方式中，还包括持续监测各所述用电系统的实际用电量；以及基于所持续监测的实际用电量的时序信息和预设的用电系统的运行状态与用电量的对应关系，确定所述用电系统当前的运行状态的步骤。
在本申请的第二方面的某些实施方式中，所述根据当前所监测的实际用电总量进行用电量调控的步骤包括以下步骤：当所述用电总量时序信息接近或达到预设的预期用电需量时，根据各所述用电系统当前的运行状态调整至少一种用电系统的运行状态以降低相应时期的实际用电总量。
在本申请的第二方面的某些实施方式中，所述需量预测方法还包括基于已监测的各所述用电系统的历史用电量更新所述对应关系的步骤。
在本申请的第二方面的某些实施方式中，所述根据当前所监测的实际用电总量进行用电量调控的步骤包括：当所述用电总量时序信息接近或达到预设的预期用电需量时，控制自供电系统向各所述用电系统的供电线路补偿供电以降低相应时期的实际用电总量。
在本申请的第二方面的某些实施方式中，所述需量预测方法还包括基于所述用电相关信息预测所述自供电系统的供电量的步骤。
本申请第三方面提供一种需量预测系统，包括：获取模块，用于获取一用电周期内的包含用电计划的用电相关信息；预测模块，用于基于预确定的多个用电系统各自的运行状态与用电量的对应关系，预测各所述用电系统在所述用电相关信息约束下执行所述用电计划的预期用电总量的时序信息；以及用于基于所述预期用电总量的时序信息确定所述用电周期的预期用电需量，以便基于所述预期用电需量控制在所述用电周期内的用电量。
在本申请的第三方面的某些实施方式中，所述预测模块基于所述对应关系和所述用电相关信息中的至少第一排产信息，预测在一用电周期内执行所述用电计划的预期用电总量的第一时序信息。
在本申请的第三方面的某些实施方式中，所述预测模块还基于所述对应关系调整基于所述用电相关信息而确定的至少第二排产信息；基于调整后的至少第二排产信息预测在一用电周期内执行所述用电计划的期间的预期用电总量的时序信息；以及重复上述调整用电相关信息和预测预期用电总量的时序信息的步骤直至得到优于所述第一时序信息的第二时序信息。
在本申请的第三方面的某些实施方式中，所述预测模块得到优于第一时序信息的第二时序信息的方式包括：按照所述用电相关信息中的至少用电计划，选取用电费用最低、预期用 电总量的波动最小、和预期用电总量的峰值最大值最小中至少一种的时序信息作为第二时序信息。
在本申请的第三方面的某些实施方式中，所述预测模块还用于基于所述用电相关信息预测自供电系统的供电量；以及用于利用所述供电量补偿所述时序信息内的预期用电总量，以便基于补偿后的所述时序信息确定所述预期用电需量。
在本申请的第三方面的某些实施方式中，所述预测模块基于预期用电总量的时序信息确定所述用电周期的预期用电需量的方式包括以下至少一种：将所述预期用电时序信息中的峰值最大值作为所述预期用电需量；或者按照预设比例放大所述预期用电时序信息中的峰值最大值以得到所述预期用电需量；基于所述预期用电时序信息的波动情况自所述预期用电时序信息中选择预期用电需量。
在本申请的第三方面的某些实施方式中，所述需量预测系统还包括更新模块，用于基于至少上一个用电周期内各所述用电系统的用电量更新多个用电系统各自的运行状态与用电量的对应关系。
在本申请的第三方面的某些实施方式中，所述用电相关信息包括：运行用电计划的用电系统信息、用电计划信息，以及以下至少一种：人员信息、天气预测信息、用电系统的维护信息、排产信息和电价信息。
在本申请的第三方面的某些实施方式中，所述用电系统包括以下至少一种：用于生产制造的用电系统和用于生活办公的用电系统。
本申请第四方面提供一种服务端，包括：接口单元，用于获取一用电周期内的包含用电计划的用电相关信息；存储单元，用于存储至少一个程序；处理单元，用于调用所述至少一个程序以协调所述接口单元和存储单元执行如第一方面所述的需量预测方法。
本申请第五方面提供一种需量控制系统，包括：监测模块，用于在执行用电计划期间，监测所运行的多个用电系统的实际用电总量；预测模块，用于基于所获取的包含用电计划的用电相关信息和所监测的实际用电总量，预测所述用电计划未执行期间的用电总量时序信息；调控模块，用于当所述用电总量时序信息接近或达到预设的预期用电需量时，根据当前所监测的实际用电总量进行用电量调控。
在本申请的第五方面的某些实施方式中，所述预测模块根据所述用电相关信息和所监测的实际用电总量，预测后续至少一个单位预测时长的用电总量时序信息。
在本申请的第五方面的某些实施方式中，当所述用电总量时序信息接近或达到预设的预期用电需量时，所述调控模块根据当前所监测的实际用电总量进行用电量调控的方式包括：确定所述用电总量时序信息中接近或达到预设的预期用电需量的用电总量相距当前时刻的预 测时长；基于所述预测时长和当前所监测的实际用电总量进行用电量调控。
在本申请的第五方面的某些实施方式中，所述监测模块还用于持续监测各所述用电系统的实际用电量的时序信息和预设的用电系统的运行状态与用电量的对应关系，确定所述用电系统当前的运行状态。
在本申请的第五方面的某些实施方式中，当所述用电总量时序信息接近或达到预设的预期用电需量时，所述调控模块根据各所述用电系统当前的运行状态调整至少一种用电系统的运行状态以降低相应时期的实际用电总量。
在本申请的第五方面的某些实施方式中，所述需量控制系统还包括更新模块，用于基于已监测的各所述用电系统的历史用电量更新所述对应关系。
在本申请的第五方面的某些实施方式中，所述调控模块根据当前所监测的实际用电总量进行用电量调控的方式还包括：当所述用电总量时序信息接近或达到预设的预期用电需量时，控制自供电系统向各所述用电系统的供电线路补偿供电以降低相应时期的实际用电总量。
在本申请的第五方面的某些实施方式中，所述预测模块还用于基于所述用电相关信息预测所述自供电系统的供电量。
本申请第六方面提供一种计算机设备，包括：接口单元，用于获取当前所执行的用电计划所对应的用电相关信息，以及；存储单元，用于存储至少一个程序；处理单元，用于调用所述至少一个程序以协调所述接口单元和存储单元执行如第二方面所述的需量预测方法。
本申请第七方面提供一种需量控制系统，包括：至少一个计量装置，用于计量所连接的用电系统的累积用电量；计算机设备，与各所述计量装置通信连接且具有接口单元，用于从所述计量装置获取各所述计量装置的累积用电量，从所述接口单元获取包含用电计划的用电相关信息，以及执行如第二方面所述的需量预测方法。
如上所述，本申请的需量预测方法、需量控制方法及系统，具有以下有益效果：本申请所提供的所述需量预测方法通过引入用电周期内的用电相关信息能够更准确地预测预期用电需量，以供企业更合理规划用电。另外，本申请还提供需量控制方法通过对执行用电计划的过程进行有效监控以利用企业内部资源予以及时调控使得企业的用电效率大幅提高。
附图说明
图1显示为在执行生产活动期间和未执行生产活动期间各用电系统的用电总量随时间的变化曲线示意图。
图2显示为本申请的服务端在一实施方式中的结构示意图。
图3显示为本申请的需量预测方法在一实施方式中的流程图。
图4显示为本申请的需量预测方法在又一实施方式中的流程图。
图5显示为本申请的基于用电相关信息中第一排产信息所预测的第一时序信息的示意图。
图6显示为本申请的需量预测方法在再一实施方式中的流程图。
图7显示为本申请的第一时序信息和第二时序信息的曲线的界面示意图。
图8显示为本申请的需量预测系统在一实施方式中的架构图。
图9显示为本申请的需量控制系统在一实施方式中的结构示意图。
图10显示为本申请的需量控制系统中计算机设备的结构示意图。
图11显示为本申请的需量控制方法在一实施方式中的流程图。
图12显示为利用本申请的需量控制方案所预测的用电总量时序信息上各用电总量与预期用电需量的示意图。
图13显示为本申请的需量控制系统在一实施方式中的架构图。
具体实施方式
以下由特定的具体实施例说明本申请的实施方式，熟悉此技术的人士可由本说明书所揭露的内容轻易地了解本申请的其他优点及功效。
虽然在一些实例中术语第一、第二等在本文中用来描述各种元件，但是这些元件不应当被这些术语限制。这些术语仅用来将一个元件与另一个元件进行区分。例如，第一预设阈值可以被称作第二预设阈值，并且类似地，第二预设阈值可以被称作第一预设阈值，而不脱离各种所描述的实施例的范围。第一预设阈值和预设阈值均是在描述一个阈值，但是除非上下文以其他方式明确指出，否则它们不是同一个预设阈值。相似的情况还包括第一音量与第二音量。
再者，如同在本文中所使用的，单数形式“一”、“一个”和“该”旨在也包括复数形式，除非上下文中有相反的指示.应当进一步理解，术语“包含”、“包括”表明存在所述的特征、步骤、操作、元件、组件、项目、种类、和/或组，但不排除一个或多个其他特征、步骤、操作、元件、组件、项目、种类、和/或组的存在、出现或添加.此处使用的术语“或”和“和/或”被解释为包括性的，或意味着任一个或任何组合.因此，“A、B或C”或者“A、B和/或C”意味着“以下任一个：A；B；C；A和B；A和C；B和C；A、B和C”。仅当元件、功能、步骤或操作的组合在某些方式下内在地互相排斥时，才会出现该定义的例外。
企业的用电量高峰与企业的生产计划、营业时长、企划活动等行为密不可分。请参阅图1所示，其显示为在执行生产活动期间和未执行生产活动期间各用电系统的用电总量随时间的变化曲线示意图。由图1中可见，企业的用电需量预测通常是预测企业执行生产经营行为期间所需的用电总量。该用电总量为企业的各用电系统短时用电量的总和(即能耗总量)。正是由于生产经营行为对企业用电量的重要影响，单依靠历史用电数据不能准确预测下一用电周期中的用电总量峰值。
为此，本申请提供一种需量预测方法。所述能量预测方法主要由能量预测系统来执行。其中，所述能量预测系统可以是配置在服务端的软件系统，其利用所配置服务端的硬件执行相应程序以为企业提供下一用电周期内执行相应生产活动所需的用电总量的时序信息，进而根据所预测的时序信息确定企业在相应用电周期的用电需量。其中，所述用电周期可与契约中所约定的用电周期相同，也可以根据生产活动来设置用电周期。所预测的用电需量可用来帮助企业申报契约用电需量以更加合理地设置用电成本。
在此，所述服务端包括但不限于单台服务器、服务器集群、分布式服务器群、云服务端等。其中，所述云服务端包括公共云(Public Cloud)服务端与私有云(Private Cloud)服务端，其中，所述公共或私有云服务端包括Software-as-a-Service(软件即服务,SaaS)、Platform-as-a-Service(平台即服务,PaaS)及Infrastructure-as-a-Service(基础设施即服务,IaaS)等。所述私有云服务端例如阿里云计算服务平台、亚马逊(Amazon)云计算服务平台、百度云计算平台、腾讯云计算平台等等。
在此，所述服务端与企业的用电控制系统、生产活动的管理系统等通信连接，甚至还可以数据连接第三方系统，以及利用爬虫技术获取互联网中与企业用电相关的互联网数据等。其中，所述用电控制系统包括但不限于：安装在企业内的计量装置(如电度表)、电气设备控制系统等。所述管理系统包括但不限于：生产过程执行系统(MES，Manufacturing Execution System)、企业资源计划系统(ERP，Enterprise Resource Planning)等。所述第三方系统举例包括自有的用于存储历史用电数据服务器、用于获取企业用电计划的WEB服务器等。所述互联网数据举例包括天气预报数据等，其中，所述天气预报数据可以是基于从互联网获取的历史同期的天气数据预测而得的，或者从气象网站或其他网站直接获取的天气预报数据。所述需量预测系统利用所获取的各方用电相关信息执行以下各步骤以预测下一用电周期的预期用电需量，以便基于所述预期用电需量控制企业在所述用电周期内的用电总量。
另外，请参阅图2，其显示为服务端在一实施方式中的结构示意图。所述服务端包含接口单元11、存储单元12、和处理单元13。其中，所述存储单元12包含非易失性存储器、存储服务器等。其中，所述非易失性存储器举例为固态硬盘或U盘等。所述存储服务器用于存 储所获取的各种用电相关信息。所述接口单元11包括网络接口、数据线接口等。其中所述网络接口包括但不限于：以太网的网络接口装置、基于移动网络(3G、4G、5G等)的网络接口装置、基于近距离通信(WiFi、蓝牙等)的网络接口装置等。所述数据线接口包括但不限于：USB接口、RS232等。所述接口单元与企业的各系统、第三方系统、互联网等数据连接。所述处理单元13连接接口单元和存储单元，其包含：CPU或集成有CPU的芯片、可编程逻辑器件(FPGA)和多核处理器中的至少一种。所述处理单元13还包括内存、寄存器等用于临时存储数据的存储器。请参阅图3，其显示为所述需量预测方法的流程图。所述处理单元13读取存储单元所存储的至少一个程序和用电相关信息以执行如下所述的需量预测方法。其中，所述用电相关信息是处理单元13预先自接口单元(未予图示)获取并保存在存储单元12中的。
在步骤S110中，获取一用电周期内的包含用电计划的用电相关信息。其中，所述用电计划是指企业在相应用电周期内预知的生产计划、活动计划、经营计划中的至少一个。所述用电计划包括但不限于生产或活动时限、生产或活动所使用的用电设备等。除了上述用电计划之外，所述用电相关信息中还可以包含以下至少一种：排产信息、人员信息、天气预测信息、设备维护信息和电价信息等。
其中，所述企划活动信息是为配合企业庆典、节假日促销等而设定的包含活动日期、资源配置等信息。所述排产信息是基于生产订单、资源、班次、假日、例外班次、物料清单、作业优先级等而确定的包含排程计算日期和时间、和资源配置信息的信息。所述人员信息包括办公区域的人员数量、厂区人员数量、换班人员数量、企业活动预计人员数量等。所述电价信息包括各时段电价单价、契约需量的电价等。设备维护信息包括设备名称及所对应的用电系统等。
其中，所述用电系统包含在执行相应用电计划期间企业中所使用的任何耗电设备(或耗电设备的集合)、或者主要的耗电设备(或耗电设备的集合)。在一些具体示例中，所述用电系统可包含相同类别的至少一个用电设备。例如，所述需量预测系统将厂区和办公区的所有照明设备视为一个用电系统。在另一些具体示例中，所述用电系统可包含接入同一计量装置的至少一个用电设备。例如，位于生活办公区域的空调设备和照明设备均接入同一个计量装置，所述需量预测系统将使用同一计量装置的多个空调设备和照明设备视为一个用电系统。所述用电系统包括以下至少一种：用于生产制造的用电系统和用于生活办公的用电系统。根据企业的经营范围，用于生产制造的用电系统包括但不限于：产线上所使用的用电系统、单独的用电系统、厂房内的空调系统和照明系统等；其中，产线上所使用的用电系统包括但不限于：装配流水线、制备流水线、测试流水线等。单独的用电系统包括但不限于：驱 动设备、控制设备等。用于生活办公的用电系统包括但不限于空调系统、照明系统、电梯控制系统、强弱电转换系统等。
在此，所述需量预测系统可藉由企业的MES系统(或ERP系统)所分享的数据库读取用电计划及与所述用电计划关联的其他用电相关信息。例如，根据可共享数据的需要，所述需量预测系统从MES系统所对应的数据库读取排产信息、人员信息、用电系统的维护信息和各用电系统执行顺序等。又如，所述需量预测系统可向企业提供可输入包含用电计划的用电相关信息的界面，并藉由所述界面获取相应的用电相关信息。根据设计需要，所述需量预测系统还从第三方或互联网上获取影响用电系统耗电的用电相关信息。例如所述需量预测系统获取天气预报信息等影响空调系统运行的用电信息。
在步骤S120中，基于预设的多个用电系统各自的运行状态与用电量的对应关系，预测各所述用电系统在所述用电相关信息约束下执行所述用电计划的预期用电总量的时序信息。
在此，所述需量预测系统中预存有企业内至少对用电量影响大的各用电系统各自的运行状态。其中，所述用电系统的运行状态中包含用电系统中所有用电设备的运行状态组合。所述用电设备的运行状态是指用电设备中的电机、控制器等在至少一种状态下运行并维持相应状态的运行。以所述用电系统中包含多个空调设备为例，所述空调设备包含待机模式、新风模式、制冷模式、制热模式等多种模式，根据出风量、制冷(热)温度等，每种模式包括至少一种运行状态，该用电系统的运行状态包含每个空调设备的合理的运行状态的组合。需要说明的是，上述用电系统仅为举例，并非每个用电设备一定具备多种模式，例如照明设备仅包含开状态和关状态，包含照明设备的用电系统为各照明设备开状态和关状态的组合，技术人员应根据用电设备实际维持运行能力而确定相应的运行状态。
另外，所述需量预测系统中还预存有各用电系统在相应运行状态下的用电量。在此，在一些实施方式中，各运行状态所对应的用电量可通过预先模拟用电设备各运行状态而确定的，或者根据用电设备的设备参数计算而得的。例如，利用用电设备的设备参数模拟用电设备在各运行状态下的用电量，其中，所述设备参数包括但不限于：额定功率、最大功率等电气参数，流量、压力、转速等物理参数，以及温度等环境参数。
在另一些实施方式中，各运行状态所对应的用电量是基于历史获取的各所述用电系统的历史用电量而确定的。为此，在进行需量预测前的一段时期，收集执行本次预测之前各用电系统各自的实际用电量或者实际用电总量，以通过机器学习方式确定各运行状态变化所对应的用电量变化量。其中，所述运行状态变化是指用电系统从运行状态A1调整为运行状态A2的变化过程，其包含运行状态A1和A2的先后顺序。例如，传送带设备从待机状态调整为传送状态为从待机状态至传送状态的运行状态变化。接着，在确定了运行状态变化所对应的用 电量变化量的基础上，以用电设备停止状态下或运行极限状态所对应的实际用电量为基准，按照所述运行状态变化顺序确定用电设备的所有运行状态所对应的实际用电量。以用电系统包含两台传送带设备为例，在确定了每台传送带设备从待机状态至传送状态的运行状态变化所对应的实际用电量变化量的基础上，根据预先确定的传送带设备在停止状态时用电量为零，用电量从低到高的运行状态依次为停止状态、待机状态和传送状态，以及在发生传送带设备的各运行状态变化时所对应的用电量变化量，确定传送带设备待机状态和传送状态各自所对应的实际用电量；相应的用电系统的运行状态包含两台传送带设备各运行状态的组合，所述需量预测系统可根据所得到的每台传送带设备的运行状态及所对应的实际用电量确定所述用电系统的运行状态及所对应的用电量。
其中，所述通过机器学习方式确定各运行状态变化所对应的用电量变化量的方式包括但不限于以下示例：
在一些具体示例中，通过一段时间积累得到用电系统的变点序列以及对应各变点的实际用电量子序列；根据所监控的用电系统中各用电设备的用电参数、各用电设备中主要电器件的电特性等，对各实际用电量子序列进行特征分析，得到每个用电设备运行状态变化所对应的实际用电量变化量。
在另一些具体示例中，通过一段时间积累得到用电系统的变点序列以及对应各变点的实际用电量子序列；将各实际用电量子序列进行聚类分类，再对同一分类的实际用电量子序列进行特征分析得到实际用电量变化量；根据所监控的用电系统中各用电设备的用电参数、各用电设备中主要电器件的电特性等，将每个用电设备运行状态变化与各分类的实际用电量变化量进行匹配，如此得到每个用电运行状态变化所对应的实际用电量变化量。
在确定了各用电设备运行状态与实际用电量的对应关系后，所述对应关系被存储在包含数据库的存储服务器上，以供所述需量预测系统读取。在一些实施方式中，各用电系统的运行状态所对应的用电量并非为固定不变的，受用电系统损耗、维护等影响，所述各用电系统的运行状态所对应的用电量会被定期或在执行所述步骤S120之前被更新。为此，请参阅图4，其显示为所述需量预测方法在又一实施方式中的流程图。在执行所述步骤S120之前，所述需量预测方法还包括步骤S100，即基于至少上一个用电周期内各所述用电系统的历史用电量更新多个用电系统各自的运行状态与用电量的对应关系。在此，所述需量预测系统可按照前述各对应关系确定方式执行步骤S100以得到更新后的所述对应关系，在此不再详述。
在一些实施方式中，所述步骤S100可进一步包括基于至少上一个用电周期内各所述用电系统的历史用电量，以及预设的各用电系统的损耗参数、所述用电相关信息中的至少一种，确定多个用电系统各自的运行状态与用电量的对应关系的步骤。其中，所述用电相关信息中 包含用电系统中用电设备的巡检维修保养信息、用电计划等。其中，用电设备自身的损耗参数，技术人员对其进行的维修操作，以及因用电计划而产生的用电系统各运行状态的持续时长和运行状态变化，参与用电计划的人员信息等均会影响包含产线设备和具有柔性负荷的用电设备在内多种用电系统各自的运行状态与用电量的对应关系。因此，所述需量预测系统将基于用电相关信息而确定的各中影响因素量化，并作为确定多个用电系统各自的运行状态所对应的用电量的约束参数，并利用最新的历史用电量对所述对应关系进行更新处理，以得到更贴近真实用电系统各自的运行状态所对应的用电量。其中各用电系统自身的损耗的量化数据可以基于损耗变化曲线而确定；各用电系统的巡检维修保养的量化参数可以是基于历史巡检维修保养之前和之后的用电量的变化而确定的；用电计划的量化参数可以是基于历史执行过的多个用电计划时用电量的变化而确定的。根据实际预测算法的设计需要，上述各量化参数可作为用于确定各用电量的权重、偏移量、参数区间门限中的至少一种。经上述至少一种量化参数的约束，在确定各用电系统的运行状态所对应的用电量后执行步骤S120，即利用所述对应关系预测各所述用电系统在所述用电相关信息约束下执行所述用电计划的预期用电总量的时序信信息。
在一些实施方式中，所述需量预测系统可按照所述用电计划中所提供的至少排产信息(以下称为第一排产信息)和所述对应关系，预测在一用电周期内执行所述用电计划的预期用电总量的第一时序信息。在此，所述需量预测系统按照所述第一排产信息所安排的各用电系统中用电设备的执行顺序确定相应用电设备的运行状态变化顺序；依据所确定的用电设备的运行状态变化顺序，确定属于同一用电系统中各用电设备的运行状态组合的顺序；再依据所述对应关系预测在一用电周期内执行所述用电计划的预期用电总量的第一时序信息。请参阅图5，其显示为基于用电相关信息中第一排产信息所预测的第一时序信息的示意图，所述示意图仅示意性地描述第一时序信息为所预测的各用电系统的预期用电量的总和，而非经准确测量得到的。其中，所述第一时序信息Pz是用电系统D1的预期用电量的时序信息Pd1和用电系统D2的预期用电量的时序信息Pd2的总和。
需要说明的是，上述预测第一时序信息的方式仅为举例，由于所述用电相关信息中还可以包含如照明系统、空调系统、电梯系统等作为柔性负荷的各用电系统的使用计划信息、天气预报信息、用电设备的维护信息、人员信息等与用电相关的其他信息，所述需量预测系统还基于上述各信息预测包含柔性负荷的各用电系统运行期间各硬性状态变化顺序的时序信息在内的第一时序信息。其中，所述天气预报信息包括气象部门发布的用电周期内的天气预报信息，或基于历史同时期的天气信息而预测的天气预报信息。所述人员信息包括直接参与排产的人员数量、和间接参与排产的人员数量(如办公人员数量)等。所述用电设备的维护信 息包括当前已维护的维护信息、和/或在相应用电周期内的维护计划等。所述需量预测系统可根据上述各信息确定柔性负荷的用电系统的预期用电量的时序信息等，通过将所有得到的用电系统的时序信息叠加得到所预测的预期用电总量的第一时序信息。
在一些实施方式中，对于具有生产工序复杂、或生产产品多样的企业来说，各排产信息可能源自不同排产部门，而排产部门之间由于缺乏横向沟通，导致排产信息会集中到用电周期的一短期时段内，这使得所预测的第一时序信息在相应时段内具有过高的峰值最大值，而其他时段的用电总量过少。为此，请参阅图6，其显示为所述需量预测方法在又一实施方式中的流程图，与图3和图4不同的是，所述需量预测系统在进行预期用电总量预测时，还执行以下步骤：
在步骤S121中，基于所述对应关系调整基于所述用电相关信息而确定的至少第二排产信息。其中，所述用电相关信息所直接提供的排产信息为第一排产信息，所述需量预测系统根据用电计划而调整所述第一排产信息以得到第二排产信息。根据用电相关信息所提供的如人员信息、天气预报信息等其他信息，所述需量预测系统也可以推得如照明系统、空调系统等柔性负荷的用电系统的使用计划等。例如，根据用电相关信息中参与生产计划的人员数量、天气预报信息等制定生产区域的照明系统和空调系统的使用计划信息；根据用电相关信息中办公区域的人员数量、天气预报信息等制定办公区域的照明系统和空调系统的使用计划信息。又如，根据用电相关信息中上下班时间段、换班时间段等制定电梯系统的使用计划信息。
以基于所述用电相关信息而确定的第二排产信息、各用电系统的使用计划各自所对应的用电系统的运行状态变化顺序为输入信息执行步骤S122。
在步骤S122中，基于调整后的至少第二排产信息预测在一用电周期内执行所述用电计划的期间的预期用电总量的时序信息。在此，所述预测方式与前述预测第一时序信息的方式相同或相似，在此不再详述。
重复上述调整用电相关信息和预测预期用电总量的时序信息的步骤直至得到优于所述第一时序信息的第二时序信息。在此，所重复的次数依据预设的收敛条件或重复次数阈值而确定。其中，所述收敛条件包括但不限于所得到预期用电总量的时序信息所反映的用电费用最低、预期用电总量的波动最小、和预期用电总量的峰值最大值最小中至少一种。当将所得到的各预期用电总量的时序信息满足基于所述收敛条件时，将相应的预期用电总量的时序信息作为第二时序信息。或者当在所述重复次数达到所述重复次数阈值时，从所得到的所有预期用电总量的时序信息中选取最符合所述收敛条件的预期用电总量的时序信息，并将其作为第二时序信息。
例如，所述需量预测系统以所述用电计划为约束，调整排产信息、柔性负荷的使用计划 信息等并按照调整后的各信息进行预期用电总量的时序信息，得到预期用电总量的波动最小和/或预期用电总量的峰值最大值最小的时序信息，并将其反馈给企业的相关人员，以供所述相关人员基于所预测的时序信息对排产信息进行调整。
又如，一些地域的电价标准按照累积用电量、用电高峰和低谷设置了多种电价梯度，所述需量预测系统可从第三方系统获取包含电价梯度的电价信息，再根据所获取的用电计划中的期限，预测为完成用电计划所对应的生产活动等各用电系统启动、档位调整、停止等所对应的运行状态变化顺序，并以包含电价梯度的电价信息为约束，从所预测的多组候选的各用电系统的运行状态变化顺序中选择其中一种使得电价最低的时序信息作为第二时序信息；同时还确定第二时序信息所对应调整的第二排产信息、各种使用计划信息等。
在上述各示例的基础上，本领域技术人员可将其中一种与其他预测方案相结合，以便在预期用电总量波动最小、降低预期用电总量的时序信息中峰值最大值和电价最低的基础上增加更多与实际生产活动相关的约束来预测实际用电总量的时序信息，并得到相应的排产信息和各柔性负载的使用计划信息的方式应视为本申请的具体示例在此不一一详述。
在又一些实施方式中，为防止供电意外，或为了减少能量浪费，企业内部具备自供电系统。所述自供电系统包括但不限于：光伏发电系统、热转换系统、储能系统、三联供系统、风能发电系统等。在进行用电预测时，还可以将企业的自供电系统纳入预测用电需量的考量范围。为此，所述需量预测方法还包括基于所述用电相关信息预测自供电系统的供电量，以及利用所述供电量补偿所述时序信息内的预期用电总量，以便基于补偿后的所述时序信息确定所述预期用电需量的步骤。
对于具有光伏发电系统和风能发电系统的企业来说，所述需量预测系统根据所述用电相关信息中的天气预报信息估计在已预测的时序信息的峰值及电价较高的时段自供电系统的供电量，并将所估计的供电量补偿在所述时序信息内的相应时段的预期用电总量，以得到补偿后的时序信息。
对于具有热转换系统的企业来说，所述需量预测系统根据已预测时序信息中用电系统排放热能的时段，并基于预设的热转换率估计在相应时段内所转换的供电量，并将所转换的供电量补偿到所述排放热能的时段内，以得到补偿后的时序信息。
对于具有热转换系统和储能系统的企业来说，所述需量预测系统可按照上述方式估计热转换系统所能转换的电能，并按照储能系统的存储损失率估计将所述电能存储在储能系统中电能；在已预测的时序信息的峰值及电价较高的时段自供电系统的供电量，并将所估计的供电量补偿在所述时序信息内的相应时段的预期用电总量，以得到补偿后的时序信息。另外，所述需量预测系统在预测预期用电总量的时序信息时还应考虑自供电系统中储能系统充电计 划信息和放电计划信息，为此，在预测第一时序信息和第二时序信息时，还可以将储能系统的充电过程视为一用电系统的用电过程，并基于所述用电相关信息而确定的储能系统的充电计划信息，预测储能系统的用电量的时序信息，并将所预测的所有用电系统各用电量的时序信息的总和确定为所预测的预期用电总量的时序信息。
需要说明的是，上述各利用自供电系统的供电量预测预期用电总量的时序信息的方式仅为举例，而非对本申请的限制。事实上根据实际自供电系统的供电方式，所述需量预测系统所预测的供电量是直接或间接依据用电相关信息而确定的，在此技术思想的指导下，由此延伸出的基于所述用电相关信息预测自供电系统的供电量，以及利用所预测的供电量对已预测的时序信息进行补偿处理的方式应视为本申请的一个具体示例。
当预测了一用电周期内的时序信息后，所述需量预测系统执行步骤S130。
在步骤S130中，基于所述预期用电总量的时序信息确定所述用电周期的预期用电需量。
在此，当预测了一用电周期内的时序信息后，所述需量预测系统可进一步根据所述时序信息中的峰值最大值来帮助企业设定预期用电需量，企业可根据该预期用电需量向供电公司上报契约需量。为此，所述需量预测系统可将所预测的至少一个时序信息及各自所对应的排产信息提供给企业。例如，请参阅图7，其显示为显示第一时序信息和第二时序信息的曲线的界面示意图，所述需量预测系统将所预测的各时序信息绘制成曲线，以及将曲线上的峰值最大值的数值显示在相应的显示界面上。在所述显示界面上还可以显示按照各自时序信息所对应的预期用电需量。在此，所述预期用电需量可以为相应时序信息的峰值最大值或按照预设比例放大相应峰值最大值所得到的。
在另一具体示例中，所述需量预测系统还基于所述预期用电时序信息的波动情况自所述预期用电时序信息中选择预期用电需量。其中，所述波动情况包括但不限于：所述预期用电时序信息中各峰值之间的偏差情况、所述预期用电时序信息中各波峰所持续的时长等。在此，所述预期用电需量可以为相应时序信息的峰值最大值或按照预设比例放大相应峰值最大值所得到的。所述需量预测系统还可根据波动情况并结合电价信息确定预期用电需量。例如，在所预测的预期用电时序信息中的峰值最大值相比其他峰值更尖锐、耗时更短，则可结合电价支付和惩罚标准、和预期用电时序信息的波动选择低于峰值最大值的用电量作为预期用电需量。
企业相关人员可根据所述需量预测系统所提供的依据用电成本而生成的各种信息调整相应的排产信息，并再将包含调整后的排产信息的用电相关信息反馈给所述需量预测系统，以供其再次执行步骤S110-S130。由此预测所述用电周期的预期用电需量。
本申请还提供一种需量预测系统。所述需量预测系统为配置在服务端的软件系统。请参阅图8，其显示为所述需量预测系统在一实施方式中的架构图。所述需量预测系统2包含获取模块21和预测模块22等程序模块。
所述获取模块21用于获取一用电周期内的包含用电计划的用电相关信息。其中，所述用电计划是指企业在相应用电周期内预知的生产计划、活动计划、经营计划中的至少一个。所述用电计划包括但不限于生产或活动时限、生产或活动所使用的用电设备等。除了上述用电计划之外，所述用电相关信息中还可以包含以下至少一种：排产信息、人员信息、天气预测信息、设备维护信息和电价信息等。
其中，所述排产信息是基于生产订单、资源、班次、假日、例外班次、物料清单、作业优先级等而确定的包含排程计算日期和时间、和资源配置信息的信息。除了用电计划之外，所述用电相关信息中还可以包含以下至少一种：人员信息、天气预测信息、用电系统的维护信息、电价信息和各用电系统执行顺序。其中，所述企划活动信息是为配合企业庆典、节假日促销等确定的包含活动日期、资源配置等信息。其中，所述用电系统包含在执行相应用电计划期间企业中所使用的任何耗电设备(或耗电设备的集合)、或者主要的耗电设备(或耗电设备的集合)。在一些具体示例中，所述用电系统可包含相同类别的至少一个用电设备。例如，所述获取模块21将厂区和办公区的所有照明设备视为一个用电系统。在另一些具体示例中，所述用电系统可包含接入同一计量装置的至少一个用电设备。例如，位于生活办公区域的空调设备和照明设备均接入同一个计量装置，所述获取模块21将使用同一计量装置的多个空调设备和照明设备视为一个用电系统。所述用电系统包括以下至少一种：用于生产制造的用电系统和用于生活办公的用电系统。根据企业的经营范围，用于生产制造的用电系统包括但不限于：产线上所使用的用电系统、单独的用电系统、厂房内的空调系统和照明系统等；其中，产线上所使用的用电系统包括但不限于：装配流水线、制备流水线、测试流水线等。单独的用电系统包括但不限于：驱动设备、控制设备等。用于生活办公的用电系统包括但不限于空调系统、照明系统、电梯控制系统、强弱电转换系统等。
在此，所述获取模块21可藉由企业的MES系统(或ERP系统)所分享的数据库读取用电计划及与所述用电计划关联的其他用电相关信息。例如，根据可共享数据的需要，所述获取模块21从MES系统所对应的数据库读取排产信息、人员信息、用电系统的维护信息和各用电系统执行顺序等。又如，所述获取模块21可向企业提供可输入包含用电计划的用电相关信息的界面，并藉由所述界面获取相应的用电相关信息。根据设计需要，所述获取模块21还从第三方或互联网上获取影响用电系统耗电的用电相关信息。例如所述获取模块21获取天气预报信息等影响空调系统运行的用电信息。
所述预测模块22用于基于预设的多个用电系统各自的运行状态与用电量的对应关系，预测各所述用电系统在所述用电相关信息约束下执行所述用电计划的预期用电总量的时序信息。
在此，所述预测模块22中预存有企业内至少对用电量影响大的各用电系统各自的运行状态。其中，所述用电系统的运行状态中包含用电系统中所有用电设备的运行状态组合。所述用电设备的运行状态是指用电设备中的电机、控制器等在至少一种状态下运行并维持相应状态的运行。以所述用电系统中包含多个空调设备为例，所述空调设备包含待机模式、新风模式、制冷模式、制热模式等多种模式，根据出风量、制冷(热)温度等，每种模式包括至少一种运行状态，该用电系统的运行状态包含每个空调设备的合理的运行状态的组合。需要说明的是，上述用电系统仅为举例，并非每个用电设备一定具备多种模式，例如照明设备仅包含开状态和关状态，包含照明设备的用电系统为各照明设备开状态和关状态的组合，技术人员应根据用电设备实际维持运行能力而确定相应的运行状态。
另外，所述预测模块22中还预存有各用电系统在相应运行状态下的用电量。在此，在一些实施方式中，各运行状态所对应的用电量可通过预先模拟用电设备各运行状态而确定的，或者根据用电设备的设备参数计算而得的。例如，利用用电设备的设备参数模拟用电设备在各运行状态下的用电量，其中，所述设备参数包括但不限于：额定功率、最大功率等电气参数，流量、压力、转速等物理参数，以及温度等环境参数。
在另一些实施方式中，各运行状态所对应的用电量是基于历史获取的各所述用电系统的历史用电量而确定的。为此，在进行需量预测前的一段时期，收集执行本次预测之前各用电系统各自的实际用电量或者实际用电总量，以通过机器学习方式确定各运行状态变化所对应的用电量变化量。其中，所述运行状态变化是指用电系统从运行状态A1调整为运行状态A2的变化过程，其包含运行状态A1和A2的先后顺序。例如，传送带设备从待机状态调整为传送状态为从待机状态至传送状态的运行状态变化。接着，在确定了运行状态变化所对应的用电量变化量的基础上，以用电设备停止状态下或运行极限状态所对应的实际用电量为基准，按照所述运行状态变化顺序确定用电设备的所有运行状态所对应的实际用电量。以用电系统包含两台传送带设备为例，在确定了每台传送带设备从待机状态至传送状态的运行状态变化所对应的实际用电量变化量的基础上，根据预先确定的传送带设备在停止状态时用电量为零，用电量从低到高的运行状态依次为停止状态、待机状态和传送状态，以及在发生传送带设备的各运行状态变化时所对应的用电量变化量，确定传送带设备待机状态传送状态各自所对应的实际用电量；相应的用电系统的运行状态包含两台传送带设备各运行状态的组合，所述需量预测系统可根据所得到的每台传送带设备的运行状态及所对应的实际用电量确定所述 用电系统的运行状态及所对应的用电量。
其中，所述通过机器学习方式确定各运行状态变化所对应的用电量变化量的方式包括但不限于以下示例：
在一些具体示例中，通过一段时间积累得到用电系统的变点序列以及对应各变点的实际用电量子序列；根据所监控的用电系统中各用电设备的用电参数、各用电设备中主要电器件的电特性等，对各实际用电量子序列进行特征分析，得到每个用电设备运行状态变化所对应的实际用电量变化量。
在另一些具体示例中，通过一段时间积累得到用电系统的变点序列以及对应各变点的实际用电量子序列；将各实际用电量子序列进行聚类分类，再对同一分类的实际用电量子序列进行特征分析得到实际用电量变化量；根据所监控的用电系统中各用电设备的用电参数、各用电设备中主要电器件的电特性等，将每个用电设备运行状态变化与各分类的实际用电量变化量进行匹配，如此得到每个用电运行状态变化所对应的实际用电量变化量。
在确定了各用电设备运行状态与实际用电量的对应关系后，所述对应关系被存储在包含数据库的存储服务器上，以供所述预测模块22读取。在一些实施方式中，各用电系统的运行状态所对应的用电量并非为固定不变的，受用电系统损耗、维护等影响，所述各用电系统的运行状态所对应的用电量会被定期或在执行所述预测模块22之前被更新。为此，所述需量预测系统还包括更新模块(未予图示)，即基于至少上一个用电周期内各所述用电系统的历史用电量更新多个用电系统各自的运行状态与用电量的对应关系。在此，所述更新模块可按照前述各对应关系确定方式得到更新后的所述对应关系，在此不再详述。
在一些实施方式中，所述更新模块还基于至少上一个用电周期内各所述用电系统的历史用电量，以及预设的各用电系统的损耗参数、所述用电相关信息中的至少一种，确定多个用电系统各自的运行状态与用电量的对应关系。其中，所述用电相关信息中包含用电系统中用电设备的巡检维修保养信息、用电计划等。其中，用电设备自身的损耗参数，技术人员对其进行的维修操作，以及因用电计划而产生的用电系统各运行状态的持续时长和运行状态变化，参与用电计划的人员信息等均会影响包含产线设备和具有柔性负荷的用电设备在内多种用电系统各自的运行状态与用电量的对应关系。因此，所述更新模块将基于用电相关信息而确定的各中影响因素量化，并作为确定多个用电系统各自的运行状态所对应的用电量的约束参数，并利用最新的历史用电量对所述对应关系进行更新处理，以得到更贴近真实用电系统各自的运行状态所对应的用电量。其中各用电系统自身的损耗的量化数据可以基于损耗变化曲线而确定；各用电系统的巡检维修保养的量化参数可以是基于历史巡检维修保养之前和之后的用电量的变化而确定的；用电计划的量化参数可以是基于历史执行过的多个用电计划时用电量 的变化而确定的。根据实际预测算法的设计需要，上述各量化参数可作为用于确定各用电量的权重、偏移量、参数区间门限中的至少一种。经上述至少一种量化参数的约束，在确定各用电系统的运行状态所对应的用电量后启动预测模块22，即利用所述对应关系预测各所述用电系统在所述用电相关信息约束下执行所述用电计划的预期用电总量的时序信信息。
在一些实施方式中，所述预测模块22可按照所述用电计划中所提供的至少排产信息(以下称为第一排产信息)和所述对应关系，预测在一用电周期内执行所述用电计划的预期用电总量的第一时序信息。在此，所述预测模块22按照所述第一排产信息所安排的各用电系统中用电设备的执行顺序确定相应用电设备的运行状态变化顺序；依据所确定的用电设备的运行状态变化顺序，确定属于同一用电系统中各用电设备的运行状态组合的顺序；再依据所述对应关系预测在一用电周期内执行所述用电计划的预期用电总量的第一时序信息。如图4所示，所述第一时序信息Pz是用电设备D1的预期用电量的时序信息Pd1和用电设备D2的预期用电量的时序信息Pd2的总和。
需要说明的是，上述预测第一时序信息的方式仅为举例，由于所述用电相关信息中还可以包含如照明系统、空调系统、电梯系统等作为柔性负荷的各用电系统的使用计划信息、天气预报信息、用电设备的维护信息、人员信息等与用电相关的其他信息，所述预测模块22还基于上述各信息预测包含柔性负荷的各用电系统运行期间各硬性状态变化顺序的时序信息在内的第一时序信息。其中，所述天气预报信息包括气象部门发布的用电周期内的天气预报信息，或基于历史同时期的天气信息而预测的天气预报信息。所述人员信息包括直接参与排产的人员数量、和间接参与排产的人员数量(如办公人员数量)等。所述用电设备的维护信息包括当前已维护的维护信息、和/或在相应用电周期内的维护计划等。所述预测模块22可根据上述各信息确定柔性负荷的用电系统的预期用电量的时序信息等，通过将所有得到的所有类型的用电系统的时序信息叠加得到所预测的预期用电总量的第一时序信息。
在一些实施方式中，对于具有生产工序复杂、或生产产品多样的企业来说，各排产信息可能源自不同排产部门，而排产部门之间由于缺乏横向沟通，导致排产信息会集中到用电周期的一短期时段内，这使得所预测的第一时序信息在相应时段内具有过高的峰值最大值，而其他时段的用电总量过少。为此，所述预测模块22在进行预期用电总量预测时，还执行以下步骤：
在步骤S121中，基于所述对应关系调整基于所述用电相关信息而确定的至少第二排产信息。其中，所述用电相关信息所直接提供的排产信息为第一排产信息，所述预测模块22根据用电计划而调整所述第一排产信息以得到第二排产信息。根据用电相关信息所提供的如人员信息、天气预报信息等其他信息，所述预测模块22也可以推得如照明系统、空调系统等柔性 负荷的用电系统的使用计划等。例如，根据用电相关信息中参与生产计划的人员数量、天气预报信息等制定生产区域的照明系统和空调系统的使用计划信息；根据用电相关信息中办公区域的人员数量、天气预报信息等制定办公区域的照明系统和空调系统的使用计划信息。又如，根据用电相关信息中上下班时间段、换班时间段等制定电梯系统的使用计划信息。
以基于所述用电相关信息而确定的第二排产信息、各用电系统的使用计划各自所对应的用电系统的运行状态变化顺序为输入信息执行步骤S122。
在步骤S122中，基于调整后的至少第二排产信息预测在一用电周期内执行所述用电计划的期间的预期用电总量的时序信息。在此，所述预测方式与前述预测第一时序信息的方式相同或相似，在此不再详述。
重复上述调整用电相关信息和预测预期用电总量的时序信息的步骤直至得到优于所述第一时序信息的第二时序信息。在此，所重复的次数依据预设的收敛条件或重复次数阈值而确定。其中，所述收敛条件包括但不限于所得到预期用电总量的时序信息所反映的用电费用最低、预期用电总量的波动最小、和预期用电总量的峰值最大值最小中至少一种。当将所得到的各预期用电总量的时序信息满足基于所述收敛条件时，将相应的预期用电总量的时序信息作为第二时序信息。或者当在所述重复次数达到所述重复次数阈值时，从所得到的所有预期用电总量的时序信息中选取最符合所述收敛条件的预期用电总量的时序信息，并将其作为第二时序信息。
例如，所述预测模块22以所述用电计划为约束，调整排产信息、柔性负荷的使用计划信息等并按照调整后的各信息进行预期用电总量的时序信息，得到预期用电总量的波动最小和/或预期用电总量的峰值最大值最小的时序信息，并将其反馈给企业的相关人员，以供所述相关人员基于所预测的时序信息对排产信息进行调整。
又如，一些地域的电价标准按照累积用电量、用电高峰和低谷设置了多种电价梯度，所述预测模块22可从第三方系统获取包含电价梯度的电价信息，再根据所获取的用电计划中的期限，预测为完成用电计划所对应的生产活动等各用电系统启动、档位调整、停止等所对应的运行状态变化顺序，并以包含电价梯度的电价信息为约束，从所预测的多组候选的各用电系统的运行状态变化顺序中选择其中一种使得电价最低的时序信息作为第二时序信息；同时还确定第二时序信息所对应调整的第二排产信息、各种使用计划信息等。
在上述各示例的基础上，本领域技术人员可将其中一种与其他预测方案相结合，以便在预期用电总量波动最小、降低预期用电总量的时序信息中峰值最大值和电价最低的基础上增加更多与实际生产活动相关的约束来预测实际用电总量的时序信息，并得到相应的排产信息和各柔性负载的使用计划信息的方式应视为本申请的具体示例在此不一一详述。
在又一些实施方式中，为防止供电意外，或为了减少能量浪费，企业内部具备自供电系统。所述自供电系统包括但不限于：光伏发电系统、热转换系统、储能系统、三联供系统、风能发电系统等。在进行用电预测时，还可以将企业的自供电系统纳入预测用电需量的考量范围。为此，所述需量预测方法还包括基于所述用电相关信息预测自供电系统的供电量，以及利用所述供电量补偿所述时序信息内的预期用电总量，以便基于补偿后的所述时序信息确定所述预期用电需量的步骤。
对于具有光伏发电系统和风能发电系统的企业来说，所述预测模块22根据所述用电相关信息中的天气预报信息估计在已预测的时序信息的峰值及电价较高的时段自供电系统的供电量，并将所估计的供电量补偿在所述时序信息内的相应时段的预期用电总量，以得到补偿后的时序信息。另外，所述需量预测系统在预测预期用电总量的时序信息时还应考虑自供电系统中储能系统充电计划信息和放电计划信息，为此，在预测第一时序信息和第二时序信息时，还可以将储能系统的充电过程视为一用电系统的用电过程，并基于所述用电相关信息而确定的储能系统的充电计划信息，预测储能系统的用电量的时序信息，并将所预测的所有用电系统各用电量的时序信息的总和确定为所预测的预期用电总量的时序信息。
对于具有热转换系统的企业来说，所述预测模块22根据已预测时序信息中用电系统排放热能的时段，并基于预设的热转换率估计在相应时段内所转换的供电量，并将所转换的供电量补偿到所述排放热能的时段内，以得到补偿后的时序信息。
对于具有热转换系统和储能系统的企业来说，所述预测模块22可按照上述方式估计热转换系统所能转换的电能，并按照储能系统的存储损失率估计将所述电能存储在储能系统中电能；在已预测的时序信息的峰值及电价较高的时段自供电系统的供电量，并将所估计的供电量补偿在所述时序信息内的相应时段的预期用电总量，以得到补偿后的时序信息。
需要说明的是，上述各利用自供电系统的供电量预测预期用电总量的时序信息的方式仅为举例，而非对本申请的限制。事实上根据实际自供电系统的供电方式，所述预测模块22所预测的供电量是直接或间接依据用电相关信息而确定的，在此技术思想的指导下，由此延伸出的基于所述用电相关信息预测自供电系统的供电量，以及利用所预测的供电量对已预测的时序信息进行补偿处理的方式应视为本申请的一个具体示例。
当预测了一用电周期内的时序信息后，所述预测模块22还用于基于所述预期用电总量的时序信息确定所述用电周期的预期用电需量。
在此，当预测了一用电周期内的时序信息后，所述预测模块22可进一步根据所述时序信息中的峰值最大值来帮助企业设定预期用电需量，企业可根据该预期用电需量向供电公司上报契约需量。为此，所述预测模块22可将所预测的至少一个时序信息及各自所对应的排 产信息提供给企业。例如，请参阅图7，其显示为显示第一时序信息和第二时序信息的曲线的界面示意图，所述预测模块22将所预测的各时序信息绘制成曲线，以及将曲线上的峰值最大值的数值显示在相应的显示界面上。在所述显示界面上还可以显示按照各自时序信息所对应的预期用电需量。在此，所述预期用电需量可以为相应时序信息的峰值最大值或按照预设比例放大相应峰值最大值所得到的。
在另一具体示例中，所述需量预测系统还基于所述预期用电时序信息的波动情况自所述预期用电时序信息中选择预期用电需量。其中，所述波动情况包括但不限于：所述预期用电时序信息中各峰值之间的偏差情况、所述预期用电时序信息中各波峰所持续的时长等。在此，所述预期用电需量可以为相应时序信息的峰值最大值或按照预设比例放大相应峰值最大值所得到的。所述预测模块22还可根据波动情况并结合电价信息确定预期用电需量。例如，在所预测的预期用电时序信息中的峰值最大值相比其他峰值更尖锐、耗时更短，则预测模块22可结合电价支付和惩罚标准、和预期用电时序信息的波动选择低于峰值最大值的用电量作为预期用电需量。
企业相关人员可根据所述需量预测系统所提供的依据用电成本而生成的各种信息调整相应的排产信息，并再将包含调整后的排产信息的用电相关信息反馈给所述需量预测系统，以供获取模块21和预测模块22再次预测所述用电周期的预期用电需量以获取准确的契约需量的参考值。
对于企业来说，准确预测预期用电需量能够避免过高或过低估计契约需量，进而节约用电成本。然而在实际生产中，受临时加产、天气、人员操作等多种不确定因素的影响，还需要对执行用电计划的过程进行有效监控，以便在不确定因素影响下尽可能确保用电需量不高于契约需量。为此，本申请还提供一种需量控制方法。所述需量控制方法可由需量控制系统来执行。其中，请参阅图9，其显示为所述需量控制系统的结构示意图。所述需量控制系统可以包含用于执行以下各步骤的计算机设备31和用以项所述计算机设备31提供累积用电量的至少一个计量装置32。所述计算机设备31利用各计量装置32获取与之连接的用电系统的累积用电量，并通过每次所获取的累积用电量计算所监控的所有用电系统各自的瞬时的实际用电量和/或实际用电总量。所述计算机设备31基于所配置的软件和硬件而执行在实际用电总量接近或达到预期用电需量时，给予适当调控使得用电系统的实际用电总量低于预期用电需量，如此有效降低企业生产活动时的用电成本。其中，所述预期用电需量可为前述各示例中所提及的契约需量或所述需量预测方法所预测的预期用电需量。
其中，所述计算机设备可以是位于企业的用电调控机房的设备，或为互联网中一服务端。所述服务端包括但不限于单台服务器、服务器集群、分布式服务器群、云服务端等。其 中，所述云服务端包括公共云(Public Cloud)服务端与私有云(Private Cloud)服务端，其中，所述公共或私有云服务端包括Software-as-a-Service(软件即服务,SaaS)、Platform-as-a-Service(平台即服务,PaaS)及Infrastructure-as-a-Service(基础设施即服务,IaaS)等。所述私有云服务端例如阿里云计算服务平台、亚马逊(Amazon)云计算服务平台、百度云计算平台、腾讯云计算平台等等。
所述计算机设备与企业的用电控制系统、生产活动的管理系统等通信连接，甚至还可以数据连接第三方系统，以及利用爬虫技术获取互联网中与企业用电相关的互联网数据等。其中，所述用电控制系统包括但不限于：安装在企业内的计量装置(如电度表)、电气设备控制系统等。所述管理系统包括但不限于：生产过程执行系统(MES，Manufacturing Execution System)、企业资源计划系统(ERP，Enterprise Resource Planning)等。所述第三方系统举例包括自有的用于存储历史用电数据服务器、用于获取企业用电计划的WEB服务器等。所述互联网数据举例包括天气预报数据等，其中，所述天气预报数据从气象网站或其他网站获取的。具体地，请参阅图10，其显示为所述计算机设备的结构示意图。所述计算机设备包括接口单元41、存储单元42和处理单元43。与图10所对应的服务端中所描述的各单元硬件类似，在此不再赘述。其中，结合图9和图10，所述计算机设备31中的接口单元41与企业中各系统、第三方系统和互联网数据连接以获取当前所执行的用电计划所对应的用电相关信息；处理单元43连接接口单元41和存储单元42，通过接口单元41将所获取的用电相关信息存储在存储单元中，并且所述存储单元42中还存有至少一个程序；所述处理单元43调用所述至少一个程序以协调所述接口单元41和存储单元42执行如下需量控制方法。
请参阅图11，其显示为所述需量控制方法在一实施方式中的流程图。所述需量控制系统利用所获取的各方用电相关信息执行以下各步骤以实时监测各用电系统的实际用电总量，并基于所述预期用电需量调控企业内各用电系统和/或自供电系统以尽可能地将实际用电总量限制在预期用电需量以下。
其中，为准确地确定企业中所能调控的用电系统和自供电系统，所述计算机设备中预存储有包含用电计划的用电相关信息。其中，所述用电计划是指企业在相应用电周期内预知的生产计划、活动计划、经营计划中的至少一个。所述用电计划包括但不限于生产或活动时限、生产或活动所使用的用电设备等。除了上述用电计划之外，所述用电相关信息中还可以包含以下至少一种：排产信息、人员信息、天气预测信息、设备维护信息和电价信息等。其中，所述排产信息是基于生产订单、资源、班次、假日、例外班次、物料清单、作业优先级等而确定的包含排程计算日期和时间、和资源配置信息的信息。除了用电计划之外，所述用电相关信息中还可以包含以下至少一种：人员信息、天气预测信息、用电系统的维护信息、 电价信息和各用电系统执行顺序。其中，所述企划活动信息是为配合企业庆典、节假日促销等确定的包含活动日期、资源配置等信息。除了上述用电相关信息之外，
在步骤S210中，在执行用电计划期间，监测所运行的多个用电系统的实际用电总量。其中，在执行用电计划期间，企业的用电系统不仅包含直接执行生产活动的用电系统，还包括为生产活动提供照明、温控的用电系统，以及为企业办公生活区域提供用电的用电系统。
在此，所述需量控制系统与企业的计量装置(如电度表)、或者用电总控制系统数据连接，并获取企业内所有用电系统的实际用电总量。其中所述实际用电总量为单位采样时长内的瞬时用电总量。在一些具体示例中，每一个或一个区域的用电系统连接计量装置，所述需量控制系统通过间隔地读取计量装置的实际用电累计值，并根据两次读取的实际用电累计值及所读取的时间间隔计算用电系统在单位采样时长的实际用电量，将所有实际用电量取和得到所述实际用电总量。在又一些具体示例中，所有用电系统连接同一个计量装置，所述需量控制系统根据两次读取的实际用电累计值及所读取的时间间隔计算各用电系统在单位采样时长的实际用电总量。
在步骤S220中，基于所获取的包含用电计划的用电相关信息和所监测的实际用电总量，预测所述用电计划中尚未执行期间的用电总量时序信息。
在此，所述需量控制系统根据所述用电相关信息中的排产信息确定当前用于执行用电计划的各用电系统及其运行状态，并依据已执行的用电计划部分预测未执行的用电计划部分的用电总量时序信息。其中，所述用电总量时序信息是指各用电系统的用电总量随时间的序列信息。
在此，所述需量控制系统可通过解析排产信息得到以下信息：用电计划的起始和结束的日期和具体时间，执行用电计划的用电系统、执行期间各用电系统的运行状态变化顺序和变化时间。在持续监测期间，所述需量控制系统通过比对系统时间与排产信息确定当前和后续各用电系统及其运行状态；根据预设的各用电系统的运行状态与用电量的对应关系、所述运行状态变化顺序和变化时间，预测后续待执行的用电计划部分期间的用电总量的时序信息。
其中，所述用电系统的运行状态中包含用电系统中所有用电设备的运行状态组合。所述用电设备的运行状态是指用电设备中的电机、控制器等在至少一种状态下运行并维持相应状态的运行。以所述用电系统中包含多个空调设备为例，所述空调设备包含待机模式、新风模式、制冷模式、制热模式等多种模式，根据出风量、制冷(热)温度等，每种模式包括至少一种运行状态，该用电系统的运行状态包含每个空调设备的合理的运行状态的组合。需要说明的是，上述用电系统仅为举例，并非每个用电设备一定具备多种模式，例如照明设备仅包含开状态和关状态，包含照明设备的用电系统为各照明设备开状态和关状态的组合。技术人 员应根据用电设备维持运行能力而确定相应的运行状态。
另外，所述需量控制系统中预存的各用电系统在相应运行状态下的用电量，在一些具体示例中是预先通过模拟用电设备各运行状态而得到的，或者根据设备用电参数计算而得的。例如，利用用电设备的参数模拟用电设备在各运行状态下的用电量，其中，所述参数包括但不限于：额定功率、最大功率等电气参数，流量、压力、转速等物理参数，以及温度等环境参数。
在另一些具体示例中，各运行状态所对应的用电量是基于历史获取的各所述用电系统的历史用电量而确定的。为此，在进行需量控制前的一段时期，收集执行本次控制之前各用电系统各自的用电量或者用电总量，以通过机器学习方式确定各运行状态变化所对应的用电量变化量。其中，所述运行状态变化是指用电设备从运行状态A1调整为运行状态A2的变化过程，其包含运行状态A1和A2的先后顺序。例如，传送带设备从待机状态调整为传送状态为从待机状态至传送状态的运行状态变化。接着，在确定了运行状态变化所对应的用电量变化量的基础上，以用电设备停止状态下所对应的用电量为基准，按照运行状态变化顺序确定用电设备的所有运行状态所对应的用电量。例如，在确定了传送带设备从待机状态至传送状态的运行状态变化所对应的用电量变化量的基础上，根据预先确定的传送带设备在停止状态时用电量为零，用电量从低到高的运行状态依次为停止状态、待机状态和传送状态，以及在发生所述运行状态变化之前及之后所获取的各自用电量确定传送带设备待机状态和传送状态给子所对应的用电量。
其中，所述通过机器学习方式确定各运行状态变化所对应的用电量变化量的方式包括但不限于以下示例：
在一些具体示例中，通过一段时间积累得到用电系统的变点序列以及对应各变点的用电量子序列；根据所监控的用电系统中各用电设备的用电参数、各用电设备中主要电器件的电特性等，对各用电量子序列进行特征分析，得到每个用电设备运行状态变化所对应的用电量变化量。
在另一些具体示例中，通过一段时间积累得到用电系统的变点序列以及对应各变点的用电量子序列；将各用电量子序列进行聚类分类，再对同一分类的用电量子序列进行特征分析得到用电量变化量；根据所监控的用电系统中各用电设备的用电参数、各用电设备中主要电器件的电特性等，将每个用电设备运行状态变化与各分类的用电量变化量进行匹配，如此得到每个用电运行状态变化所对应的用电量变化量。
在实际应用中企业并非一定会提供详细的排产信息，为此所述需量控制系统需要估计各用电系统的运行状运行状态变化顺序和变化时间。在一具体示例中，所述需量控制系统持续 监测各所述用电系统的实际用电量；基于所持续监测的实际用电量的时序信息和预设的用电系统的运行状态与用电量的对应关系，确定所述用电系统当前的运行状态。
在此，所述需量控制系统可依据预先所确定的各运行状态变化所对应的用电量变化量，检测经持续监测一段时间的实际用电量的时序信息中所包含的运行状态变化；接着，按照所确定的用电系统的运行状态与用电量的对应关系确定用电系统的运行状态变化之前和之后的运行状态，由此确定用电系统当前的运行状态。以用电系统包含传送带设备为例，在确定了传送带设备从待机状态至传送状态的运行状态变化所对应的实际用电量变化量的基础上，根据预先确定的传送带设备在停止状态时用电量为零，用电量从低到高的运行状态依次为停止状态、待机状态和传送状态，以及在发生各传送带设备的运行状态变化时所对应的用电量变化量，确定传送带设备当前处于待机状态和所对应的实际用电量。
接着，按照所预获取的各用电系统的运行状态与用电量的对应关系，以及所获取的用电相关信息，预测后续待执行的用电计划期间的用电总量的时序信息。在此，所述需量控制系统根据所述用电相关信息中的粗略的排产信息预测后续待执行的用电计划期间与生产活动直接相关的用电系统的用电量时序信息B1，根据天气预报信息、人员数量等预测为了维持厂区和办公生活区的室内温度而对应的用电系统的用电量时序信息B2等；将多个用电量时序信息B1和B2等叠加得到预期用电总量的时序信信息。
在一些实施方式中，为了提高实时性，所述步骤S220包括根据所述用电相关信息和所监测的实际用电总量，预测后续至少一个单位预测时长的用电总量时序信息的步骤。其中，所述单位预测时长是指预测的最小间隔。例如，所述单位预测时长为五分钟(或其他任意时长)，则所述需量控制系统预测至少一个五分钟之内的用电总量时序信息。
在此，所述需量控制系统预测至少一个单位预测时长的用电总量时序信息的方式与前述预测所述用电计划未执行期间的用电总量时序信息的方式相通或相似，在此不再赘述。当预测了用电总量的时序信息后，所述需量控制系统执行步骤S230。
在步骤S230中，当所预测的用电总量时序信息接近或达到预设的预期用电需量时，根据当前所监测的实际用电总量进行用电量调控。
请参阅图12，其显示为所预测的用电总量时序信息上各用电总量与预期用电需量的示意图。所述需量控制系统逐个比较所预测的各用电总量和预期用电需量；当所预测的用电总量时序信息中存在一用电总量与预期用电需量之间的差距小于预设警戒偏差阈值，或者存在一用电总量大于预期用电需量时，所述需量控制系统根据当前所监测的实际用电总量进行用电量调控。
在此，所述根据当前所监测的所述实际用电总量进行用电量调控的方式包括以下至少一 种示例：
在一些具体示例中，根据各所述用电系统当前的运行状态调整至少一种用电系统的运行状态以降低相应时期的实际用电总量。当所预测的用电总量时序信息接近或达到预设的预期用电需量时可按照预设的优先级，调整优先级最低的用电系统的运行状态；所述需量控制系统直至监测到实际的用电总量、或再次预测到用电总量时序信息未接近预设的预期用电需量为止。
在间隔预设时长后所述需量控制系统还可以重新依优先级又高到低回调被调整过的用电系统的运行状态。例如，需量控制系统按照预设的优先级将优先级最低的空调系统从制热(或制冷)状态调整至省电模式的运行状态、待机状态或断电状态，并再次监测实际的用电总量，若实际的用电总量与预期的用电需量的差距大于预设的警戒偏差阈值，则重新回调空调系统至制热(或制冷)状态。
在另一些具体示例中，对于具有自供电系统的企业来说，所述需量控制系统还可以控制自供电系统向各所述用电系统的供电线路补偿供电以降低相应时期的实际用电总量。其中，所述自供电系统包括但不限于：光伏发电系统、热转换系统、储能系统、三联供系统、风能发电系统等。所述需量控制系统控制自供电系统向各所述用电系统的供电线路补偿供电以降低相应时期的实际用电总量。例如，当需量控制系统确定所预测的用电总量时序信息接近、达到或者超出预设的预期用电需量时，根据相应的补偿缺口、各自供电系统的供电量选择控制至少一个自供电系统向各用电系统所在供电线路供电，以降低相应时期的实际用电总量。
其中，各自供电系统可被视为能稳定提供供电量的系统。在实际应用中，受天气、换能率、储能损耗等影响，各自供电系统所提供的实际供电量会出现波动。为此，所述需量控制方法还包括基于所述用电相关信息预测所述自供电系统的供电量的步骤。例如，若自供电系统包含光伏发电系统、风能发电系统中的至少一种，则所述需量预测系统根据所述用电相关信息中的天气预报信息估计在已预测的时序信息的峰值及电价较高的时段自供电系统的供电量，并将所估计的供电量补偿在所述时序信息内的相应时段的预期用电总量，以得到补偿后的时序信息。又如，所述自供电系统包含热转换系统、储能系统中的至少一种，所述需量预测系统根据已预测时序信息中用电系统排放热能的时段，估计热转换系统所能转换的电能，以及按照储能系统的存储损失率估计存储在储能系统中的电能；当基于上述预测确定控制自供电系统供电时，控制至少一个自供电系统向各用电系统的供电线路提供供电。
对于包含储能系统的企业来说，所述需量控制系统所监控实际用电总量还包括储能系统充电过程所消耗的用电量。为此，所述需量控制系统在确定所述用电总量时序信息接近或达到预设的预期用电需量时，还根据用电相关信息中充电计划信息选择暂缓储能系统的充电过 程，直至再次预测的用电总量时序信息的峰值最大值相距所述预期用电需量大于预设的警戒偏差阈值。
需要说明的是，上述各利用自供电系统的供电量进行供电补偿的方式仅为举例，而非对本申请的限制。事实上根据实际自供电系统的供电方式，所述需量控制系统所预测的供电量是直接或间接依据用电相关信息而确定的，在此技术思想的指导下，由此延伸出的基于所述用电相关信息预测自供电系统的供电量，以及利用所预测的供电量进行供电补偿的方式应视为本申请的一个具体示例。
在又一具体示例中，所述需量控制系统还可以在确定所述用电总量时序信息中接近或达到预设的预期用电需量的基础上，进一步确定所述用电总量时序信息中的符合上述条件的用电总量相距当前时刻的预测时长；以及基于所述预测时长和当前所监测的实际用电总量进行用电量调控。
在此，所述需量控制系统将所预测的用电总量时序信息中的各用电总量与预期用电需量进行比较，当确定某一个用电总量与所述预期用电需量的差距小于预设的警戒偏差阈值时，确定从当前时刻到达经预测的相应用电总量的预测时长。当所述预测时长小于一时长阈值时，通常认为很快达到预期用电需量，则控制自供电系统进行供电补偿；反之，通常认为还有具备可调控的时间，则通过调整至少一个用电系统的运行状态以降低相应时期的实际用电总量。
需要说明的是，上述基于预测时长的调控方式仅为举例，而非对本申请的限制。事实上，根据实际预测得到的用电缺口和预测时长，可采用暂不予调控继续预测、同时调控自供电系统和用电系统的运行状态、或者单独调控自供电系统和用电系统的运行状态中的任一种等多种方式进行需量控制处理。在此技术思想指示下，本领域技术人员采用上述任一种调控方式或者对上述任一种调控方式的改进应视为本申请的具体示例。
还需要说明的是，上述任一种调控用电量的方式既可以利用需量控制系统与企业的用电控制系统的数据连接直接调控相应用电系统和自供电系统；又可以将所需调整的用电系统和自供电系统以界面提示的方式提供给企业的技术人员，以供企业的技术人员按照所述提示执行调控操作。
针对前述描述的所述对应关系是基于历史获取的各所述用电系统的历史用电量而确定的各种实现方式，为了更准确地从一个或多个计量装置所获取的实际用电量中分析出各用电系统的运行状态，在获取了所监测的各用电系统的用电量后，不必然与前述S220和S230有先后执行顺序地，所述需量控制方法还包括基于已监测各所述用电系统的历史用电量更新所述对应关系的步骤。在此，所述需量控制系统还将所监测到的实际用电量及实际用电总量存入 相应的数据库中。所述需量控制系统利用预设的用于确定各用电系统的各运行状态与用电量的对应关系的算法，将所积累的实际用电量及实际用电总量，甚至还可以包含所获取的用电相关信息等数据输入所述算法中，以得到更新后的对应关系。所述需量控制系统还可以在存储数据之后向运行所述算法的系统发出相应的更新指令，以供其执行更新操作。更新后的对应关系将在所述需量控制系统实时调控时被调取使用，在此不再详述。
本申请还提供一种需量控制系统。所述需量控制系统为安装在计算机设备中的软件系统。请参阅图13，其显示为需量控制系统在一实施方式中的架构图。所述需量控制系统5包括监测模块51、预测模块52和调控模块53等程序模块。
其中，为准确地确定企业中所能调控的用电系统和自供电系统，所述需量控制系统中预设包含用电计划的用电相关信息。其中，所述用电计划是指企业在相应用电周期内预知的生产计划、活动计划、经营计划中的至少一个。所述用电计划包括但不限于生产或活动时限、生产或活动所使用的用电设备等。除了上述用电计划之外，所述用电相关信息中还可以包含以下至少一种：排产信息、人员信息、天气预测信息、设备维护信息和电价信息等。其中，所述排产信息是基于生产订单、资源、班次、假日、例外班次、物料清单、作业优先级等而确定的包含排程计算日期和时间、和资源配置信息的信息。除了用电计划之外，所述用电相关信息中还可以包含以下至少一种：人员信息、天气预测信息、用电系统的维护信息、电价信息和各用电系统执行顺序。其中，所述企划活动信息是为配合企业庆典、节假日促销等确定的包含活动日期、资源配置等信息。
所述监测模块51在执行用电计划期间，监测所运行的多个用电系统的实际用电总量。其中，在执行用电计划期间，企业的用电系统不仅包含直接执行生产活动的用电系统，还包括为生产活动提供照明、温控的用电系统，以及为企业办公生活区域提供用电的用电系统。
在此，所述监测模块51与企业的计量装置(如电度表)、或者用电总控制系统数据连接，并获取企业内所有用电系统的实际用电总量。其中所述实际用电总量为单位采样时长内的瞬时用电总量。在一些具体示例中，每一个或一个区域的用电系统连接计量装置，所述监测模块51通过间隔地读取计量装置的实际用电累计值，并根据两次读取的实际用电累计值及所读取的时间间隔计算用电系统在单位采样时长的实际用电量，将所有实际用电量取和得到所述实际用电总量。在又一些具体示例中，所有用电系统连接同一个计量装置，所述监测模块51根据两次读取的实际用电累计值及所读取的时间间隔计算各用电系统在单位采样时长的实际用电总量。
预测模块52用于基于所获取的包含用电计划的用电相关信息和所监测的实际用电总量，预测所述用电计划中尚未执行期间的用电总量时序信息。
在此，所述预测模块52根据所述用电相关信息中的排产信息确定当前用于执行用电计划的各用电系统及其运行状态，并依据已执行的用电计划部分预测未执行的用电计划部分的用电总量时序信息。其中，所述用电总量时序信息是指各用电系统的用电总量随时间的序列信息。
在此，所述预测模块52可通过解析排产信息得到以下信息：用电计划的起始和结束的日期和具体时间，执行用电计划的用电系统、执行期间各用电系统的运行状态变化顺序和变化时间。在持续监测期间，所述预测模块52通过比对系统时间与排产信息确定当前和后续各用电系统及其运行状态；根据预设的各用电系统的运行状态与用电量的对应关系、所述运行状态变化顺序和变化时间，预测后续待执行的用电计划部分期间的用电总量的时序信息。
其中，所述用电系统的运行状态中包含用电系统中所有用电设备的运行状态组合。所述用电设备的运行状态是指用电设备中的电机、控制器等在至少一种状态下运行并维持相应状态的运行。以所述用电系统中包含多个空调设备为例，所述空调设备包含待机模式、新风模式、制冷模式、制热模式等多种模式，根据出风量、制冷(热)温度等，每种模式包括至少一种运行状态，该用电系统的运行状态包含每个空调设备的合理的运行状态的组合。需要说明的是，上述用电系统仅为举例，并非每个用电设备一定具备多种模式，例如照明设备仅包含开状态和关状态，包含照明设备的用电系统为各照明设备开状态和关状态的组合。技术人员应根据用电设备维持运行能力而确定相应的运行状态。
另外，所述预测模块52中预存的各用电系统在相应运行状态下的用电量，在一些具体示例中是预先通过模拟用电设备各运行状态而得到的，或者根据设备用电参数计算而得的。例如，利用用电设备的参数模拟用电设备在各运行状态下的用电量，其中，所述参数包括但不限于：额定功率、最大功率等电气参数，流量、压力、转速等物理参数，以及温度等环境参数。
在另一些具体示例中，各运行状态所对应的用电量是基于历史获取的各所述用电系统的历史用电量而确定的。为此，在进行需量控制前的一段时期，收集执行本次控制之前各用电系统各自的用电量或者用电总量，以通过机器学习方式确定各运行状态变化所对应的用电量变化量。其中，所述运行状态变化是指用电设备从运行状态A1调整为运行状态A2的变化过程，其包含运行状态A1和A2的先后顺序。例如，传送带设备从待机状态调整为传送状态为从待机状态至传送状态的运行状态变化。接着，在确定了运行状态变化所对应的用电量变化量的基础上，以用电设备停止状态下所对应的用电量为基准，按照运行状态变化顺序确定用电设备的所有运行状态所对应的用电量。例如，在确定了传送带设备从待机状态至传送状态的运行状态变化所对应的用电量变化量的基础上，根据预先确定的传送带设备在停止状态时 用电量为零，用电量从低到高的运行状态依次为停止状态、待机状态和传送状态，以及在发生所述运行状态变化之前及之后所获取的各自用电量确定传送带设备待机状态和传送状态给子所对应的用电量。
其中，所述通过机器学习方式确定各运行状态变化所对应的用电量变化量的方式包括但不限于以下示例：
在一些具体示例中，通过一段时间积累得到用电系统的变点序列以及对应各变点的用电量子序列；根据所监控的用电系统中各用电设备的用电参数、各用电设备中主要电器件的电特性等，对各用电量子序列进行特征分析，得到每个用电设备运行状态变化所对应的用电量变化量。
在另一些具体示例中，通过一段时间积累得到用电系统的变点序列以及对应各变点的用电量子序列；将各用电量子序列进行聚类分类，再对同一分类的用电量子序列进行特征分析得到用电量变化量；根据所监控的用电系统中各用电设备的用电参数、各用电设备中主要电器件的电特性等，将每个用电设备运行状态变化与各分类的用电量变化量进行匹配，如此得到每个用电运行状态变化所对应的用电量变化量。
在实际应用中企业并非一定会提供详细的排产信息，为此所述预测模块52需要估计各用电系统的运行状运行状态变化顺序和变化时间。在一具体示例中，所述预测模块52持续监测各所述用电系统的实际用电量；基于所持续监测的实际用电量的时序信息和预设的用电系统的运行状态与用电量的对应关系，确定所述用电系统当前的运行状态。
在此，所述预测模块52可依据预先所确定的各运行状态变化所对应的用电量变化量，检测经持续监测一段时间的实际用电量的时序信息中所包含的运行状态变化；接着，按照所确定的用电系统的运行状态与用电量的对应关系确定用电系统的运行状态变化之前和之后的运行状态，由此确定用电系统当前的运行状态。以用电系统包含传送带设备为例，在确定了传送带设备从待机状态至传送状态的运行状态变化所对应的实际用电量变化量的基础上，根据预先确定的传送带设备在停止状态时用电量为零，用电量从低到高的运行状态依次为停止状态、待机状态和传送状态，以及在发生各传送带设备的运行状态变化时所对应的用电量变化量，确定传送带设备当前处于待机状态和所对应的实际用电量。
接着，按照所预获取的各用电系统的运行状态与用电量的对应关系，以及所获取的用电相关信息，预测后续待执行的用电计划期间的用电总量的时序信息。在此，所述预测模块52根据所述用电相关信息中的粗略的排产信息预测后续待执行的用电计划期间与生产活动直接相关的用电系统的用电量时序信息B1，根据天气预报信息、人员数量等预测为了维持厂区和办公生活区的室内温度而对应的用电系统的用电量时序信息B2等；将多个用电量时序信 息B1和B2等叠加得到预期用电总量的时序信信息。
在一些实施方式中，为了提高实时性，所述步骤S220包括根据所述用电相关信息和所监测的实际用电总量，预测后续至少一个单位预测时长的用电总量时序信息的步骤。其中，所述单位预测时长是指预测的最小间隔。例如，所述单位预测时长为五分钟(或其他任意时长)，则所述预测模块52预测至少一个五分钟之内的用电总量时序信息。
在此，所述预测模块52预测至少一个单位预测时长的用电总量时序信息的方式与前述预测所述用电计划未执行期间的用电总量时序信息的方式相通或相似，在此不再赘述。当预测了用电总量的时序信息后，所述预测模块52将所预测的用电总量时序信息交由调控模块53。
所述调控模块53当所预测的用电总量时序信息接近或达到预设的预期用电需量时，根据当前所监测的实际用电总量进行用电量调控。
请参阅图12，其显示为所预测的用电总量时序信息上各用电总量与预期用电需量的示意图。所述调控模块53逐个比较所预测的各用电总量和预期用电需量；当所预测的用电总量时序信息中存在一用电总量与预期用电需量之间的差距小于预设警戒偏差阈值，或者存在一用电总量大于预期用电需量时，所述调控模块53根据当前所监测的实际用电总量进行用电量调控。
在此，所述根据当前所监测的所述实际用电总量进行用电量调控的方式包括以下至少一种示例：
在一些具体示例中，根据各所述用电系统当前的运行状态调整至少一种用电系统的运行状态以降低相应时期的实际用电总量。当所预测的用电总量时序信息接近或达到预设的预期用电需量时可按照预设的优先级，调整优先级最低的用电系统的运行状态；所述调控模块53直至监测到实际的用电总量、或再次预测到用电总量时序信息未接近预设的预期用电需量为止；在间隔预设时长后重新依优先级又高到底回调被调整过的用电系统的运行状态。例如，需量控制系统按照预设的优先级将优先级最低的空调系统从制热(或制冷)状态调整至省电模式的运行状态、待机状态或断电状态，并再次监测实际的用电总量，若实际的用电总量与预期的用电需量的差距大于预设的警戒偏差阈值，则重新回调空调系统至制热(或制冷)状态。在另一些具体示例中，对于具有自供电系统的企业来说，所述调控模块53还可以控制自供电系统向各所述用电系统的供电线路补偿供电以降低相应时期的实际用电总量。其中，所述自供电系统包括但不限于：光伏发电系统、热转换系统、储能系统、三联供系统、风能发电系统等。所述调控模块53控制自供电系统向各所述用电系统的供电线路补偿供电以降低相应时期的实际用电总量。例如，当调控模块53确定所预测的用电总量时序信息接近、 达到或者超出预设的预期用电需量时，根据相应的补偿缺口、各自供电系统的供电量选择控制至少一个自供电系统向各用电系统所在供电线路供电，以降低相应时期的实际用电总量。
其中，各自供电系统可被视为能稳定提供供电量的系统。在实际应用中，受天气、换能率、储能损耗等影响，各自供电系统所提供的实际供电量会出现波动。为此，所述需量控制方法还包括基于所述用电相关信息预测所述自供电系统的供电量的步骤。例如，若自供电系统包含光伏发电系统、风能发电系统中的至少一种，则所述需量预测系统根据所述用电相关信息中的天气预报信息估计在已预测的时序信息的峰值及电价较高的时段自供电系统的供电量，并将所估计的供电量补偿在所述时序信息内的相应时段的预期用电总量，以得到补偿后的时序信息。又如，所述自供电系统包含热转换系统、储能系统中的至少一种，所述需量预测系统根据已预测时序信息中用电系统排放热能的时段，估计热转换系统所能转换的电能，以及按照储能系统的存储损失率估计存储在储能系统中的电能；当基于上述预测确定控制自供电系统供电时，控制至少一个自供电系统向各用电系统的供电线路提供供电。
对于包含储能系统的企业来说，所述需量控制系统所监控实际用电总量还包括储能系统充电过程所消耗的用电量。为此，所述需量控制系统在确定所述用电总量时序信息接近或达到预设的预期用电需量时，还根据用电相关信息中充电计划信息选择暂缓储能系统的充电过程，直至再次预测的用电总量时序信息的峰值最大值相距所述预期用电需量大于预设的警戒偏差阈值。
需要说明的是，上述各利用自供电系统的供电量进行供电补偿的方式仅为举例，而非对本申请的限制。事实上根据实际自供电系统的供电方式，所述调控模块53所预测的供电量是直接或间接依据用电相关信息而确定的，在此技术思想的指导下，由此延伸出的基于所述用电相关信息预测自供电系统的供电量，以及利用所预测的供电量进行供电补偿的方式应视为本申请的一个具体示例。
在又一具体示例中，所述调控模块53还可以在确定所述用电总量时序信息中接近或达到预设的预期用电需量的基础上，进一步确定所述用电总量时序信息中的符合上述条件的用电总量相距当前时刻的预测时长；以及基于所述预测时长和当前所监测的实际用电总量进行用电量调控。
在此，所述调控模块53将所预测的用电总量时序信息中的各用电总量与预期用电需量进行比较，当确定某一个用电总量与所述预期用电需量的差距小于预设的警戒偏差阈值时，确定从当前时刻到达经预测的相应用电总量的预测时长。当所述预测时长小于一时长阈值时，通常认为很快达到预期用电需量，则控制自供电系统进行供电补偿；反之，通常认为还有具备可调控的时间，则通过调整至少一个用电系统的运行状态以降低相应时期的实际用电 总量。
需要说明的是，上述基于预测时长的调控方式仅为举例，而非对本申请的限制。事实上，根据实际预测得到的用电缺口和预测时长，可采用暂不予调控继续预测、同时调控自供电系统和用电系统的运行状态、或者单独调控自供电系统和用电系统的运行状态中的任一种等多种方式进行需量控制处理。在此技术思想指示下，本领域技术人员采用上述任一种调控方式或者对上述任一种调控方式的改进应视为本申请的具体示例。
还需要说明的是，上述任一种调控用电量的方式既可以利用调控模块53与企业的用电控制系统的数据连接直接调控相应用电系统和自供电系统；又可以将所需调整的用电系统和自供电系统以界面提示的方式提供给企业的技术人员，以供企业的技术人员按照所述提示执行调控操作。
针对前述描述的所述对应关系是基于历史获取的各所述用电系统的历史用电量而确定的各种实现方式，为了更准确地从一个或多个计量装置所获取的实际用电量中分析出各用电系统的运行状态，在获取了所监测的各用电系统的用电量后，不必然与前述预测模块和调控模块有先后执行顺序地，所述需量控制他还包括更新模块，用于基于已监测各所述用电系统的历史用电量更新所述对应关系。在此，所述监测模块还将所监测到的实际用电量及实际用电总量存入相应的数据库中。所述更新模块利用预设的用于确定各用电系统的各运行状态与用电量的对应关系的算法，将所积累的实际用电量及实际用电总量，甚至还可以包含所获取的用电相关信息等数据输入所述算法中，以得到更新后的对应关系。所述更新模块还可以在存储数据之后向运行所述算法的系统发出相应的更新指令，以供其执行更新操作。更新后的对应关系将在所述预测模块实时调控时被调取使用，在此不再详述。
综上所述，本申请所提供的所述需量预测方法通过引入用电周期内的用电相关信息能够更准确地预测预期用电需量，以供企业更合理规划用电。另外，本申请还提供需量控制方法通过对执行用电计划的过程进行有效监控以利用企业内部资源予以及时调控使得企业的用电效率大幅提高。
上述实施例仅例示性说明本申请的原理及其功效，而非用于限制本申请。任何熟悉此技术的人士皆可在不违背本申请的精神及范畴下，对上述实施例进行修饰或改变。因此，举凡所属技术领域中具有通常知识者在未脱离本申请所揭示的精神与技术思想下所完成的一切等效修饰或改变，仍应由本申请的权利要求所涵盖。

Claims (37)

  1. 一种需量预测方法，其特征在于，包括：
    获取一用电周期内的包含用电计划的用电相关信息；
    基于预确定的多个用电系统各自的运行状态与用电量的对应关系，预测各所述用电系统在所述用电相关信息约束下执行所述用电计划的预期用电总量的时序信息；
    基于所述预期用电总量的时序信息确定所述用电周期的预期用电需量。
  2. 根据权利要求1所述的需量预测方法，其特征在于，所述基于预设的多个用电系统各自的运行状态与用电量的对应关系，预测各所述用电系统在所述用电相关信息约束下执行用电计划期间的预期用电总量的时序信息的步骤包括：
    基于所述对应关系和所述用电相关信息中的至少第一排产信息，预测在一用电周期内执行所述用电计划的预期用电总量的第一时序信息。
  3. 根据权利要求2所述的需量预测方法，其特征在于，所述基于预设的多个用电系统各自的运行状态与用电量的对应关系，预测各所述用电系统在所述用电相关信息约束下执行用电计划期间的预期用电总量的时序信息的步骤包括：
    基于所述对应关系调整基于所述用电相关信息而确定的至少第二排产信息；
    基于调整后的至少第二排产信息预测在一用电周期内执行所述用电计划的期间的预期用电总量的时序信息；
    重复上述调整用电相关信息和预测预期用电总量的时序信息的步骤直至得到优于所述第一时序信息的第二时序信息。
  4. 根据权利要求3所述的需量预测方法，其特征在于，所述得到优于第一时序信息的第二时序信息的步骤包括：
    按照所述用电相关信息中的至少用电计划，选取用电费用最低、预期用电总量的波动最小、和预期用电总量的峰值最大值最小中至少一种的时序信息作为第二时序信息。
  5. 根据权利要求1所述的需量预测方法，其特征在于，还包括：
    基于所述用电相关信息预测自供电系统的供电量；以及
    利用所述供电量补偿所述时序信息内的预期用电总量，以便基于补偿后的所述时序信息确定所述预期用电需量。
  6. 根据权利要求1所述的需量预测方法，其特征在于，所述基于预期用电总量的时序信息确定所述用电周期的预期用电需量的步骤包括以下至少一种：
    将所述预期用电时序信息中的峰值最大值作为所述预期用电需量；
    按照预设比例放大所述预期用电时序信息中的峰值最大值以得到所述预期用电需量；
    基于所述预期用电时序信息的波动情况自所述预期用电时序信息中选择预期用电需量。
  7. 根据权利要求1所述的需量预测方法，其特征在于，还包括基于至少上一个用电周期内各所述用电系统的历史用电量更新多个用电系统各自的运行状态与用电量的对应关系的步骤。
  8. 根据权利要求1所述的需量预测方法，其特征在于，所述用电相关信息还包括以下至少一种：人员信息、天气预测信息、排产信息、各用电系统的维护信息和电价信息。
  9. 根据权利要求1所述的需量预测方法，其特征在于，所述用电系统包括以下至少一种：用于生产制造的用电系统和用于生活办公的用电系统。
  10. 一种需量控制方法，其特征在于，包括：
    在执行用电计划期间，监测所运行的多个用电系统的实际用电总量；
    基于所获取的包含用电计划的用电相关信息和所监测的实际用电总量，预测所述用电计划中尚未执行期间的用电总量时序信息；
    当所述用电总量时序信息接近或达到预设的预期用电需量时，根据当前所监测的实际用电总量进行用电量调控。
  11. 根据权利要求10所述的需量控制方法，其特征在于，所述基于所获取的包含用电计划的用电相关信息和所监测的实际用电总量，预测所述用电计划中尚未执行期间的用电总量时序信息的步骤包括：根据所述用电相关信息和所监测的实际用电总量，预测后续至少一个单位预测时长的用电总量时序信息。
  12. 根据权利要求10或11所述的需量控制方法，其特征在于，所述当用电总量时序信息接近或达到预设的预期用电需量时，根据当前所监测的实际用电总量进行用电量调控的步骤包 括：
    确定所述用电总量时序信息中接近或达到预设的预期用电需量的用电总量相距当前时刻的预测时长；
    基于所述预测时长和当前所监测的实际用电总量进行用电量调控。
  13. 根据权利要求10或11所述的需量控制方法，其特征在于，还包括持续监测各所述用电系统的实际用电量；以及基于所持续监测的实际用电量的时序信息和预设的用电系统的运行状态与用电量的对应关系，确定所述用电系统当前的运行状态的步骤。
  14. 根据权利要求13所述的需量控制方法，其特征在于，所述根据当前所监测的实际用电总量进行用电量调控的步骤包括以下步骤：当所述用电总量时序信息接近或达到预设的预期用电需量时，根据各所述用电系统当前的运行状态调整至少一种用电系统的运行状态以降低相应时期的实际用电总量。
  15. 根据权利要求13所述的需量控制方法，其特征在于，还包括基于已监测的各所述用电系统的历史用电量更新所述对应关系的步骤。
  16. 根据权利要求10所述的需量控制方法，其特征在于，所述根据当前所监测的实际用电总量进行用电量调控的步骤包括：
    当所述用电总量时序信息接近或达到预设的预期用电需量时，控制自供电系统向各所述用电系统的供电线路补偿供电以降低相应时期的实际用电总量。
  17. 根据权利要求16所述的需量控制方法，其特征在于，还包括基于所述用电相关信息预测所述自供电系统的供电量的步骤。
  18. 一种需量预测系统，其特征在于，包括：
    获取模块，用于获取一用电周期内的包含用电计划的用电相关信息；
    预测模块，用于基于预确定的多个用电系统各自的运行状态与用电量的对应关系，预测各所述用电系统在所述用电相关信息约束下执行所述用电计划的预期用电总量的时序信息；以及用于基于所述预期用电总量的时序信息确定所述用电周期的预期用电需量，以便基于所述预期用电需量控制在所述用电周期内的用电量。
  19. 根据权利要求18所述的需量预测系统，其特征在于，所述预测模块基于所述对应关系和所述用电相关信息中的至少第一排产信息，预测在一用电周期内执行所述用电计划的预期用电总量的第一时序信息。
  20. 根据权利要求19所述的需量预测系统，其特征在于，所述预测模块还基于所述对应关系调整基于所述用电相关信息而确定的至少第二排产信息；基于调整后的至少第二排产信息预测在一用电周期内执行所述用电计划的期间的预期用电总量的时序信息；以及重复上述调整用电相关信息和预测预期用电总量的时序信息的步骤直至得到优于所述第一时序信息的第二时序信息。
  21. 根据权利要求20所述的需量预测系统，其特征在于，所述预测模块得到优于第一时序信息的第二时序信息的方式包括：
    按照所述用电相关信息中的至少用电计划，选取用电费用最低、预期用电总量的波动最小、和预期用电总量的峰值最大值最小中至少一种的时序信息作为第二时序信息。
  22. 根据权利要求21所述的需量预测系统，其特征在于，所述预测模块还用于基于所述用电相关信息预测自供电系统的供电量；以及用于利用所述供电量补偿所述时序信息内的预期用电总量，以便基于补偿后的所述时序信息确定所述预期用电需量。
  23. 根据权利要求18所述的需量预测系统，其特征在于，所述预测模块基于预期用电总量的时序信息确定所述用电周期的预期用电需量的方式包括以下至少一种：
    将所述预期用电时序信息中的峰值最大值作为所述预期用电需量；
    按照预设比例放大所述预期用电时序信息中的峰值最大值以得到所述预期用电需量；
    基于所述预期用电时序信息的波动情况自所述预期用电时序信息中选择预期用电需量。
  24. 根据权利要求18所述的需量预测系统，其特征在于，还包括更新模块，用于基于至少上一个用电周期内各所述用电系统的用电量更新多个用电系统各自的运行状态与用电量的对应关系。
  25. 根据权利要求18所述的需量预测系统，其特征在于，所述用电相关信息还包括以下至少一种：人员信息、天气预测信息、排产信息、各用电系统的维护信息和电价信息。
  26. 根据权利要求18所述的需量预测系统，其特征在于，所述用电系统包括以下至少一种：用于生产制造的用电系统和用于生活办公的用电系统。
  27. 一种服务端，其特征在于，包括：
    接口单元，用于获取一用电周期内的包含用电计划的用电相关信息；
    存储单元，用于存储至少一个程序；
    处理单元，用于调用所述至少一个程序以协调所述接口单元和存储单元执行如权利要求1-9中任一所述的方法。
  28. 一种需量控制系统，其特征在于，包括：
    监测模块，用于在执行用电计划期间，监测所运行的多个用电系统的实际用电总量；
    预测模块，用于基于所获取的包含用电计划的用电相关信息和所监测的实际用电总量，预测所述用电计划中尚未执行期间的用电总量时序信息；
    调控模块，用于当所述用电总量时序信息接近或达到预设的预期用电需量时，根据当前所监测的实际用电总量进行用电量调控。
  29. 根据权利要求28所述的需量控制系统，其特征在于，所述预测模块根据所述用电相关信息和所监测的实际用电总量，预测后续至少一个单位预测时长的用电总量时序信息。
  30. 根据权利要求28或29所述的需量控制系统，其特征在于，当所述用电总量时序信息接近或达到预设的预期用电需量时，所述调控模块根据当前所监测的实际用电总量进行用电量调控的方式包括：
    确定所述用电总量时序信息中接近或达到预设的预期用电需量的用电总量相距当前时刻的预测时长；
    基于所述预测时长和当前所监测的实际用电总量进行用电量调控。
  31. 根据权利要求28或29所述的需量控制系统，其特征在于，所述监测模块还用于持续监测各所述用电系统的实际用电量的时序信息和预设的用电系统的运行状态与用电量的对应 关系，确定所述用电系统当前的运行状态。
  32. 根据权利要求31所述的需量控制系统，其特征在于，当所述用电总量时序信息接近或达到预设的预期用电需量时，所述调控模块根据各所述用电系统当前的运行状态调整至少一种用电系统的运行状态以降低相应时期的实际用电总量。
  33. 根据权利要求31所述的需量控制系统，其特征在于，还包括更新模块，用于基于已监测的各所述用电系统的历史用电量更新所述对应关系。
  34. 根据权利要求28所述的需量控制系统，其特征在于，所述调控模块根据当前所监测的实际用电总量进行用电量调控的方式还包括：
    当所述用电总量时序信息接近或达到预设的预期用电需量时，控制自供电系统向各所述用电系统的供电线路补偿供电以降低相应时期的实际用电总量。
  35. 根据权利要求34所述的需量控制系统，其特征在于，所述预测模块还用于基于所述用电相关信息预测所述自供电系统的供电量。
  36. 一种计算机设备，其特征在于，包括：
    接口单元，用于获取当前所执行的用电计划所对应的用电相关信息，以及；
    存储单元，用于存储至少一个程序；
    处理单元，用于调用所述至少一个程序以协调所述接口单元和存储单元执行如权利要求10-17中任一所述的方法。
  37. 一种需量控制系统，其特征在于，包括：
    至少一个计量装置，用于计量所连接的用电系统的累积用电量；
    计算机设备，与各所述计量装置通信连接且具有接口单元，用于从所述计量装置获取各所述计量装置的累积用电量，从所述接口单元获取包含用电计划的用电相关信息，以及执行如权利要求10-17中任一所述的方法。
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