CN110175778B - Unit energy efficiency planning system - Google Patents
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Abstract
The application relates to a unit energy efficiency planning system. The system comprises: the device comprises a data acquisition module, a model selection scheme generation module, a control strategy determination module, a performance prediction module and a model selection scheme determination module; the data acquisition module acquires relevant unit operation parameters; the model selection scheme generation module generates various model selection schemes according to relevant parameters of unit operation; the control strategy determination module predicts the energy consumption of each type selection scheme based on each control strategy and determines the control strategy corresponding to each type selection scheme; the performance prediction module acquires the performance of each type selection scheme according to the control strategy corresponding to each type selection scheme; and the model selection scheme determining module determines the model selection scheme and the corresponding control strategy of the final unit system from the model selection schemes according to the performance of each model selection scheme. The system operation energy consumption and performance can be predicted, the model selection scheme and the control strategy are determined based on the prediction result, the final unit system and the corresponding control strategy are designed, and the problem of resource waste is effectively avoided.
Description
Technical Field
The application relates to the technical field of air conditioners, in particular to a unit energy efficiency planning system.
Background
In the heating and ventilation industry, the problem of energy consumption of an air conditioner room is an important concern, deep discussion is conducted in the industry all the time, and whether the type selection of equipment is reasonable or not and the energy conservation is not in accordance with the standard when system design/transformation is conducted in the early stage of a design institute. Because the system scheme is various, the scheme not only relates to a primary pump, a secondary pump model selection scheme, a fixed frequency conversion system scheme, a large-machine system and a small-machine system, and the like, and the energy consumption in the refrigeration season is unrealistic through manual calculation.
At present, designers often design according to past experience, and judge whether an energy-saving strategy is reasonable when a device manufacturer designs a group control system or not and how much EERS (EERS refers to the comprehensive energy efficiency ratio of the system) runs in the system year after the system runs for a long time after the system is debugged artificially according to the end load working condition on an engineering field.
If the energy consumption of the system can not be guaranteed to reach the expected value, if the equipment is in the optimal operation level, if the energy-saving control strategy used by the system is optimal, and if the optimization space is available, the problem of resource waste is caused.
Disclosure of Invention
Therefore, in order to solve the above technical problems, it is necessary to provide an energy efficiency planning system for a unit, which effectively reduces resource waste.
An energy efficiency planning system for an aircraft, the system comprising:
the device comprises a data acquisition module, a model selection scheme generation module, a control strategy determination module, a performance prediction module and a model selection scheme determination module;
the data acquisition module acquires relevant unit operation parameters; the model selection scheme generation module generates various model selection schemes according to the relevant parameters of the unit operation; the control strategy determining module predicts the energy consumption of each model selection scheme based on each control strategy and determines the control strategy corresponding to each model selection scheme; the performance prediction module acquires the performance of each model selection scheme according to the control strategy corresponding to each model selection scheme; and the model selection scheme determining module determines the model selection scheme and the corresponding control strategy of the final unit system from each model selection scheme according to the performance of each model selection scheme.
In one embodiment, the system further comprises: a control strategy testing module;
and the control strategy testing module calls a simulation platform to simulate the final unit system based on the model selection scheme, and adopts the control strategy corresponding to the final unit system to carry out simulation test on the final unit system so as to obtain a control strategy testing result corresponding to the final unit system.
In one embodiment, the system further comprises: a control strategy optimization module;
and the control strategy optimization module calls the simulation platform to simulate the final unit system according to a control strategy test result corresponding to the final unit system, carries out strategy optimization on the control strategy corresponding to the final unit system based on a group control strategy and determines the final control strategy of the final unit system.
In one embodiment, the system further comprises: a checking and accepting module;
and the verification and acceptance module calls the simulation platform, and performs energy consumption analysis on the final unit system based on the final control strategy and the field data acquired in real time to obtain an energy consumption analysis result and perform energy consumption verification.
In one embodiment, the system further comprises: a monitoring module;
and the monitoring module acquires air conditioner data in the final unit system server by accessing the final unit system server in real time and monitors the running state information of the final unit system in real time.
In one embodiment, the system further comprises: a physical examination module;
the physical examination module acquires the recent running condition, the abnormal condition and the energy efficiency running condition of the final unit system, analyzes the reason of the abnormal condition and generates a physical examination report.
In one embodiment, the system further comprises: an energy consumption real-time prediction module;
the energy consumption real-time prediction module acquires the field meteorological parameters, the building load and the equipment energy consumption of the final unit system in real time; according to the meteorological parameters and the cold and hot loads, adopting a final control strategy to predict the energy consumption of the final unit system, and obtaining the current predicted energy consumption; determining whether the energy consumption of the equipment reaches a predicted value according to the current predicted energy consumption; and when the energy consumption of the equipment does not reach the predicted value, carrying out anomaly analysis based on the actual measurement data of all working conditions to obtain an anomaly analysis result.
In one embodiment, the system further comprises: the energy efficiency ratio pre-estimating module is used for estimating the energy efficiency ratio,
the energy efficiency ratio estimation module acquires historical building load data; acquiring weather forecast information of the final unit system installation area from a meteorological platform; and estimating the energy efficiency ratio of the pre-selected control strategy in the future preset time period according to the building load historical data and the weather forecast information to obtain the estimated energy efficiency ratio of the pre-selected control strategy in the future preset time period.
In one embodiment, the system further comprises: a control strategy adjusting module;
the control strategy adjusting module acquires the field meteorological parameters, building load and equipment energy consumption of the final unit system; based on the meteorological parameters and the cold and hot loads, adopting each preselected control strategy to predict the energy consumption of the final unit system, and obtaining the predicted energy consumption of each preselected control strategy; and sending the control strategy closest to the energy consumption of the equipment in the predicted energy consumption of each preselected control strategy to the final unit system server, so that the final unit system server updates the final control strategy of the final unit system according to a preset strategy updating mode.
In one embodiment, the control strategy adjustment module is further configured to:
analyzing the estimated energy efficiency ratio of each preselected control strategy in a future preset time period, determining the optimal control strategy in the future preset time period, and sending the optimal control strategy in the future preset time period to the final unit system server.
In one embodiment, the system further comprises: a control cabinet test module;
the control cabinet testing module calls the simulation platform to provide a physical environment required by testing, and performs equipment testing on the control cabinet of the unit to obtain a control cabinet testing result of the unit.
In one embodiment, the system further comprises: a program test module;
and the program test module calls the test platform to test the control program of the unit and determines whether the control process of the control program is abnormal.
In one embodiment, the system further comprises a pre-debug module;
the pre-debugging module calls a control program of a test platform simulation unit to test, calls a simulation platform to simulate a control strategy corresponding to the final unit system and a physical environment required by the test, and pre-debugs the final unit system to finish the pre-debugging of the final unit system.
In one embodiment, the performance prediction module comprises: an energy efficiency ratio prediction unit;
the energy efficiency ratio prediction unit predicts the energy efficiency ratio of each type selection scheme to obtain the predicted energy efficiency ratio of each type selection scheme; and determining the performance of each selected scheme according to the predicted energy efficiency ratio of each selected scheme.
In one embodiment, the performance prediction module comprises: a cost performance prediction unit;
the cost performance prediction unit predicts the cost performance of each model selection scheme to obtain predictive cost ratio data of each model selection scheme; determining performance of each of the selection scenarios based on the predictive cost ratio data for each of the selection scenarios.
In one embodiment, the cost/performance prediction unit includes: a cost and energy saving data acquisition subunit and a cost performance analysis subunit;
the cost energy-saving data acquisition subunit is used for acquiring cost data and energy-saving data of each type selection scheme; and the cost performance analysis subunit performs cost performance analysis according to the cost data and the energy-saving data to obtain predictive cost performance data of each type selection scheme.
The unit energy efficiency planning system comprises: the device comprises a data acquisition module, a model selection scheme generation module, a control strategy determination module, a performance prediction module and a model selection scheme determination module; the data acquisition module acquires relevant unit operation parameters; the model selection scheme generation module generates various model selection schemes according to relevant parameters of unit operation; the control strategy determination module predicts the energy consumption of each type selection scheme based on each control strategy and determines the control strategy corresponding to each type selection scheme; the performance prediction module acquires the performance of each type selection scheme according to the control strategy corresponding to each type selection scheme; and the model selection scheme determining module determines the model selection scheme and the corresponding control strategy of the final unit system from the model selection schemes according to the performance of each model selection scheme. The system operation energy consumption and performance can be predicted during design, an optimal selection scheme and a control strategy are determined based on a prediction result, a final unit system and a corresponding control strategy are designed, and the problem of resource waste is effectively avoided.
Drawings
FIG. 1 is a diagram illustrating an exemplary implementation of a system for energy efficiency planning for a unit;
FIG. 2 is a block diagram of an embodiment of a unit energy efficiency planning system;
FIG. 3 is a block diagram of an embodiment of a unit energy efficiency planning system;
FIG. 4 is a block flow diagram of a unit energy efficiency planning system in one embodiment;
fig. 5 is a block flow diagram of a unit energy efficiency planning system in an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The unit energy efficiency planning system provided by the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. A user sends a unit energy efficiency planning instruction to a server 104 through a terminal 102, so that a data acquisition module of the server 104 acquires unit operation related parameters; a model selection scheme generation module of the server 104 generates various model selection schemes according to the relevant parameters of unit operation; the control strategy determination module of the server 104 predicts the energy consumption of each type selection scheme based on each control strategy and determines the control strategy corresponding to each type selection scheme; the performance prediction module of the server 104 obtains the performance of each model selection scheme according to the control strategy corresponding to each model selection scheme; the model selection scheme determining module of the server 104 determines the model selection scheme and the corresponding control strategy of the final unit system from the model selection schemes according to the performance of each model selection scheme. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a system for energy efficiency planning of a unit is provided, which is described by taking an example of the system applied to a server.
An energy efficiency planning system for an aircraft, the system comprising: a data acquisition module 310, a model selection scheme generation module 320, a control strategy determination module 330, a performance prediction module 340, and a model selection scheme determination module 350.
The data acquisition module 310 acquires unit operation related parameters; the model selection scheme generation module 320 generates various model selection schemes according to the relevant parameters of the unit operation; the control strategy determination module 330 predicts the energy consumption of each type selection scheme based on each control strategy and determines the control strategy corresponding to each type selection scheme; the performance prediction module 340 obtains the performance of each model selection scheme according to the control strategy corresponding to each model selection scheme; the model selection scheme determining module 350 determines the model selection scheme and the corresponding control strategy of the final unit system from the model selection schemes according to the performance of each model selection scheme.
Wherein, the unit refers to the combination of each equipment that central air conditioning system needs, includes: indoor units, outdoor units, and water systems, etc. The unit operation related parameters comprise meteorological parameters of a unit pre-installation area and the cold and hot loads of a unit pre-installation building, and the meteorological parameters comprise: the parameters such as temperature, humidity and the like can be obtained from the meteorological platform through the determined pre-installed region, the meteorological platform is a platform for releasing meteorological conditions of each region, the parameters such as temperature, humidity and the like of the corresponding region can be obtained by crawling the meteorological platform through the data acquisition module 310, and the parameters can be provided for the data acquisition module 310 through the terminal by a user. The cold and hot load of the unit pre-installed building refers to the cold and hot load of the pre-installed building, the cold load refers to the cold load and the hot and humid environment of the pre-installed building and the required indoor temperature, the heat which must be taken away from the room by the air conditioning system (or the central air conditioning system), or the cold which needs to be supplied to the room at a certain moment is called the cold load, the cold load includes two parts of sensible heat and latent heat, the hot load refers to the heat which needs to be supplied for maintaining the heat balance of the room per unit time, the cold and hot load of the unit pre-installed building can be determined according to the using place, the using area and the unit area cold load, the maximum instantaneous cold load and the maximum instantaneous cold load are used as the cold and hot load of the unit pre-installed building, and the using place is as: malls, office buildings, individual residents, and the like.
The model selection scheme generation module 320 automatically generates all model selection schemes capable of meeting the cold and hot loads of the unit pre-installed building according to the cold and hot loads of the unit pre-installed building in unit operation related parameters, wherein the model selection schemes comprise model selection schemes of an indoor unit, an outdoor unit, a water system and other equipment, and the water system model selection schemes comprise model selection schemes of a refrigerating pump, a cooling tower and other equipment; the system selection of the model selection scheme is determined according to the type of a use place, for example, a primary pump flow system, a natural cooling system, an ice cold storage system, a wind-water linkage system and the like can be selected according to the model selection scheme.
The control strategy determination module 330 predicts the energy consumption of each type selection scheme based on each control strategy to obtain each predicted energy consumption of each type selection scheme, and determines the control strategy with the highest predicted energy consumption of each control strategy in each type selection scheme as the control strategy corresponding to each type selection scheme, wherein each control strategy can be a freezing side constant temperature difference, a freezing side constant pressure difference and a worst tail end pressure difference; resetting the temperature of the frozen effluent and the like; controlling the constant temperature difference of the cooling side and the variable temperature difference of the cooling side; and a system plus-minus machine control strategy and the like. The energy consumption prediction can be the sum of the running energy consumption accumulations of equipment in a calculation period, wherein the equipment comprises a chilled water pump, a cooling tower, a water chilling unit and the like. The control strategy corresponding to each type selection scheme is determined, or a system comprehensive Energy Efficiency Ratio (EERs) of each control strategy in each type selection scheme is calculated according to each predicted energy consumption of each control strategy in each type selection scheme, wherein the system comprehensive energy efficiency ratio is the system accumulated refrigerating capacity/the system accumulated power consumption, and the control strategy corresponding to each type selection scheme is determined based on the highest system comprehensive energy efficiency ratio of each control strategy in each type selection scheme.
When the control strategy determining module 330 determines the control strategy corresponding to each type selection scheme according to the highest predicted energy consumption of each control strategy in each type selection scheme, the performance predicting module 340 may determine the performance of each type selection scheme based on the highest performance of each control strategy in each type selection scheme according to each performance of each control strategy in each type selection scheme.
In one embodiment, the performance prediction module 340 includes: an energy efficiency ratio prediction unit; the energy efficiency ratio prediction unit predicts the predicted energy efficiency ratio of each type selection scheme to obtain the predicted energy efficiency ratio of each type selection scheme; and determining the performance of each type selection scheme according to the predicted energy efficiency ratio of each type selection scheme.
Wherein, when the control strategy determining module 330 determines the control strategy corresponding to each model selection scheme according to the highest predicted energy consumption of each control strategy in each model selection scheme, the energy efficiency ratio predicting unit of the performance predicting module 340 may calculate a predicted energy efficiency ratio of each control strategy in each model selection scheme according to each predicted energy consumption of each control strategy in each model selection scheme, where a system integrated Energy Efficiency Ratio (EERs) is system integrated refrigerating capacity/system integrated power consumption, and determines the predicted energy efficiency ratio of each model selection scheme based on the highest system integrated energy efficiency ratio of each control strategy in each model selection scheme, when the control strategy determining module 330 determines the control strategy corresponding to each model selection scheme according to the highest comprehensive energy efficiency ratio of each control strategy in each model selection scheme, the comprehensive energy efficiency ratio of each option system may be directly obtained from the control strategy determination module 330.
In one embodiment, the performance prediction module 340 includes: a cost performance prediction unit; the cost performance prediction unit predicts the cost performance of each type selection scheme to obtain the predictive cost performance data of each type selection scheme; and determining the performance of each type selection scheme according to the predicted cost performance data of each type selection scheme.
Wherein, the cost performance prediction unit comprises: a cost and energy saving data acquisition subunit and a cost performance analysis subunit; the cost energy-saving data acquisition subunit is used for acquiring cost data and energy-saving data of each type selection scheme; and the cost performance analysis subunit performs cost performance analysis according to the cost data and the energy-saving data to obtain predictive cost performance data of each type selection scheme. The cost data refers to the cost invested in purchasing equipment and a control system, and the cost data can be obtained through a related quotation system. The energy-saving data refers to the cost of energy saving for operating the system, and the cost cycle is required to be recovered for several years.
The model selection scheme determining module 350 determines the model selection scheme and the corresponding control strategy of the final unit system from each model selection scheme according to the performance of each model selection scheme. The performance of each type selection scheme can be determined according to the predicted energy efficiency ratio corresponding to each type selection scheme, the performance of each type selection scheme can also be determined according to the predicted price ratio data corresponding to each type selection scheme, the performance of each type selection scheme can also be determined according to the predicted energy efficiency ratio and the predicted price ratio data corresponding to each type selection scheme, and when the performance of each type selection scheme is determined according to the predicted energy efficiency ratio corresponding to each type selection scheme, the type selection scheme and the corresponding control strategy of the final unit system are determined according to the highest predicted energy efficiency ratio in each type selection scheme. And when the performance of each model selection scheme is determined according to the corresponding predictive price ratio data of each model selection scheme, determining the model selection scheme and the corresponding control strategy of the final unit system from each model selection scheme according to the highest predictive price ratio data. The method can be determined according to the requirements of users, and the users can provide the user requirements for the unit energy efficiency planning system through the terminal, such as: the user's requirement on the energy efficiency is higher than the cost performance, the model selection scheme of the unit system with the highest energy efficiency ratio is determined, the model selection scheme of the final unit system is determined, the user's requirement on the cost performance is higher than the energy efficiency, the model selection scheme of the unit system with the highest cost performance is determined, and the model selection scheme of the final unit system is determined, and the control strategy corresponding to the model selection scheme is based on the control strategy determined by the control strategy determination module 330. The final unit system may be referred to as a central air conditioning system.
As shown in fig. 3, in an embodiment, the unit energy efficiency planning system further includes: a control strategy test module 360; the control strategy testing module 360 calls the simulation platform to simulate the final unit system based on the model selection scheme, tests the final unit system by adopting the control strategy corresponding to the final unit system, and obtains the control strategy test result corresponding to the final unit system.
After the equipment required by the final unit system is produced according to the model selection scheme of the final unit system, the control strategy corresponding to the final unit system is tested through the control strategy testing module 360. Calling a simulation platform, selecting or customizing (region and use place category) through simulation software of the simulation platform, simulating a final unit system, setting a system model and an equipment model required by the final unit system, carrying out simulation control strategy test on the final unit system by adopting a corresponding control strategy, obtaining a control strategy test result corresponding to the final unit system, comparing an energy efficiency ratio of a test result with a predicted energy efficiency ratio, meeting the prediction requirement when the energy efficiency ratio of the test result is greater than the predicted energy efficiency ratio, not optimizing the control strategy, and not meeting the prediction requirement and needing to optimize the control strategy when the energy efficiency ratio of the test result is less than the predicted energy efficiency ratio.
As shown in fig. 3, in an embodiment, the unit energy efficiency planning system further includes: a control strategy optimization module 370; the control strategy optimization module 370 invokes the simulation platform to simulate the final unit system according to the test result of the control strategy corresponding to the final unit system, performs strategy optimization on the control strategy corresponding to the final unit system based on the group control strategy, and determines the final control strategy of the final unit system.
When the energy efficiency ratio of the test result is smaller than the predicted energy efficiency ratio, the prediction requirement is not met, the simulation platform is called through the control strategy optimization module 370, the final unit system is simulated through simulation software of the simulation platform, strategy optimization is carried out on the control strategy corresponding to the final unit system based on the group control strategy, and the final control strategy of the final unit system is determined. Or when the energy efficiency ratio of the test result is greater than the predicted energy efficiency ratio, and the user issues a control strategy optimization instruction through the terminal, the control strategy optimization module 370 invokes the simulation platform according to the control strategy optimization instruction, simulates the final unit system through simulation software of the simulation platform, performs strategy optimization on the control strategy corresponding to the final unit system based on the group control strategy, and determines the final control strategy of the final unit system. Selecting or customizing (region and use place category) through simulation software, simulating a final unit system, setting the system and equipment model required by the final unit system, carrying out computer virtual simulation by internally arranging a plurality of control strategies or adding a customized control strategy, and selecting a simulation result as a final control strategy of the final unit system according to the highest comprehensive energy efficiency ratio.
As shown in fig. 3, in an embodiment, the unit energy efficiency planning system further includes: a check acceptance module 380; the verification acceptance module 380 calls the simulation platform, and based on the final control strategy and the field data obtained in real time, the final unit system is subjected to energy consumption analysis, and energy consumption verification is performed on the obtained energy consumption analysis result.
After the final unit system is installed on the site, the site project is debugged, and after the debugging is completed, the inspection and acceptance module 380 acquires site data from the DTU of the final unit system in real time by calling the simulation platform, so that the energy consumption is checked; the acquired field data can be data such as field meteorological parameters, building loads, equipment energy consumption, system refrigerating capacity, system comprehensive energy efficiency ratio, equipment fault parameters, equipment operation parameters and the like, and deeper energy consumption analysis is realized. Energy consumption checking, namely guiding field meteorological parameters and system refrigerating capacity into a simulation platform, ensuring that the test working condition is consistent with the field working condition, performing in-plant group control test, and testing whether the system energy efficiency is consistent with the equipment energy consumption; if the simulation environment and the simulation equipment are inconsistent, analyzing the reason, correcting the equipment model through the large data platform, if the equipment model is accurate, analyzing whether the field equipment has abnormity or not, operating efficiently or not, giving an analysis result, and automatically correcting the built-in equipment model of the simulation platform by obtaining effective operating data of relevant parameters such as flow, lift, power and the like of the field refrigerator, the water pump, the cooling tower and the relevant equipment under different working conditions, so that the simulation environment and the simulation equipment are consistent with the actual situation of the field.
As shown in fig. 3, in an embodiment, the unit energy efficiency planning system further includes: a monitoring module 390; the monitoring module 390 obtains the air conditioning data in the final unit system server by accessing the final unit system server in real time, and monitors the operation status information of the final unit system in real time.
After the final unit system is put into use, the monitoring module 390 accesses the final unit system server in real time to obtain the air conditioning data in the final unit system server, where the air conditioning data includes: on-site meteorological parameters, building load, equipment energy consumption, system refrigerating capacity, system comprehensive energy efficiency ratio, equipment fault parameters, equipment operation parameters and the like. The near-term running condition, the abnormal condition and the energy efficiency running condition of the final unit system can be determined by analyzing the acquired air conditioner data in real time, so that the running condition of the unit system can be known in time, and the abnormal condition can be alarmed and adjusted in time.
As shown in fig. 3, in an embodiment, the unit energy efficiency planning system further includes: a physical examination module 410; the physical examination module 410 acquires recent operating conditions, abnormal conditions and energy efficiency operating conditions of the final unit system, analyzes reasons of the abnormal conditions and generates a physical examination report.
After the final unit system is put into use, the physical examination module 410 acquires the recent operating condition, the abnormal condition and the energy efficiency operating condition of the final unit system, analyzes the recent operating condition, the abnormal condition and the energy efficiency operating condition according to the operating condition, the abnormal condition and the energy efficiency operating condition, determines the reason of the abnormal condition, and generates a physical examination report, wherein the content of the physical examination report includes the reason of the abnormal condition, the recent operating condition and the energy efficiency operating condition.
As shown in fig. 3, in an embodiment, the unit energy efficiency planning system further includes: an energy consumption real-time prediction module 420; the energy consumption real-time prediction module 420 acquires the field meteorological parameters, the building load and the equipment energy consumption of the final unit system in real time; according to the meteorological parameters and the cold and hot loads, a final control strategy is adopted to predict the energy consumption of the final unit system, and the current predicted energy consumption is obtained; determining whether the energy consumption of the equipment reaches a predicted value according to the current predicted energy consumption; and when the energy consumption of the equipment does not reach the predicted value, carrying out anomaly analysis based on the actual measurement data of all working conditions to obtain an anomaly analysis result.
The energy consumption real-time prediction module 420 acquires parameters such as field meteorological parameters, building loads, equipment energy consumption and the like, calls a simulation platform to perform simulation test on a final unit system in real time by adopting a final control strategy, and performs energy consumption real-time prediction, wherein the final control strategy of the simulation platform is consistent with the field final control strategy; and (5) carrying out calibration with the site, if the site operation does not reach a predicted value, analyzing abnormal conditions by analyzing actual measurement data under all working conditions to obtain an abnormal analysis result.
As shown in fig. 3, in an embodiment, the unit energy efficiency planning system further includes: the energy efficiency ratio estimation module 430 is configured to estimate the energy efficiency ratio,
the energy efficiency ratio estimation module 430 acquires historical building load data; acquiring weather forecast information of a final unit system installation area from a meteorological platform; and estimating the energy efficiency ratio of the pre-selected control strategy in the future preset time period according to the building load historical data and the weather forecast information to obtain the estimated energy efficiency ratio of the pre-selected control strategy in the future preset time period.
The historical data of the building load refers to the building load at each time point of the final unit system installation place before the current time point, and the weather forecast information refers to data obtained by predicting the weather in the future time period, wherein the weather forecast information can be obtained from a weather platform. The preselected control strategy can be a built-in control strategy or a control strategy customized by a user. The future preset time period may be a plurality of time periods or one time period, and the future preset time period may be set according to a time period in which the energy efficiency ratio is estimated as required. The building load historical data can be analyzed based on big data, the building load of a future preset time period is determined, corresponding weather forecast information is determined according to the future preset time period, the energy efficiency ratio of the future preset time period of each pre-selected control strategy is estimated according to the building load of the future preset time period and the corresponding weather forecast information, and the estimated energy efficiency ratio of the future preset time period of each pre-selected control strategy is obtained. Such as: the energy efficiency ratio after 24 hours needs to be estimated, weather forecast information after 24 hours can be obtained, the building load after 24 hours is analyzed, the energy efficiency ratio after 24 hours of each preselected control strategy is estimated according to the weather forecast information after 24 hours and the building load, and the estimated energy efficiency ratio after 24 hours of each preselected control strategy is obtained, wherein the estimated energy efficiency ratio after 24 hours is the future preset time period.
As shown in fig. 3, in an embodiment, the unit energy efficiency planning system further includes: a control strategy adjustment module 440; the control strategy adjusting module 440 acquires the field meteorological parameters, building load and equipment energy consumption of the final unit system; based on meteorological parameters and cold and hot loads, adopting each preselected control strategy to predict the energy consumption of the final unit system, and obtaining the predicted energy consumption of each preselected control strategy; and sending the control strategy closest to the energy consumption of the equipment in the predicted energy consumption of each pre-selected control strategy to the final unit system server, so that the final unit system server updates the final control strategy of the final unit system according to a preset strategy updating mode.
The control strategy adjusting module 440 obtains the final on-site meteorological parameters, building loads and equipment energy consumption of the unit system, calls a simulation platform, virtually calculates the simulation platform by adopting each pre-selected control strategy, and elects an optimal control strategy according to the highest energy efficiency comprehensive ratio, wherein the optimal control strategy is the control strategy which is closest to the equipment energy consumption in the predicted energy consumption of each pre-selected control strategy. And sending the optimal control strategy to a final unit system server, so that the final unit system server updates the final control strategy of the final unit system according to a preset strategy updating mode, wherein the preset strategy updating mode can be that the final unit system server automatically updates the optimal control strategy into the final control strategy of the final unit system after receiving the optimal control strategy, the final unit system server can also display the optimal control strategy on a terminal, and a user determines whether the optimal control strategy needs to be updated into the final control strategy of the final unit system, so that the unit system can always keep high-efficiency operation.
In one embodiment, the control strategy adjustment module 440 is further configured to: and analyzing the estimated energy efficiency ratio of each pre-selected control strategy in a future preset time period, determining the optimal control strategy in the future preset time period, and sending the optimal control strategy in the future preset time period to the final unit system server.
Based on the estimated energy efficiency ratio of each pre-selected control strategy in the future preset time period by the energy efficiency ratio estimation module 430, it can be determined that the control strategy with the highest estimated energy efficiency ratio in each pre-selected control strategy in the future preset time period is the optimal control strategy in the future preset time period, the optimal control strategy in the future preset time period is sent to the final unit system server, and the optimal control strategy in the future preset time period can be provided for the user.
As shown in fig. 3, in an embodiment, the unit energy efficiency planning system further includes: a control cabinet test module 450; the control cabinet testing module 450 calls the simulation platform to perform equipment testing on the control cabinet of the unit, and obtains a control cabinet testing result of the unit.
The group control test platform is built in a laboratory, the group control cabinet and the simulation platform board card cabinet are connected, the simulation platform is called by the control cabinet test module 450, the generation control signal of the simulation platform is transmitted to the group control cabinet, the function test is carried out, and whether the connection and the components are normal is verified.
As shown in fig. 3, in an embodiment, the unit energy efficiency planning system further includes: a program test module 460; the program testing module 460 calls the testing platform to perform the control program test of the unit, and determines whether the control process of the control program is abnormal.
The group control cabinet (including the controller) is connected to the test platform, the program test module 460 calls the test platform to perform control program test, start and stop of the test system, load and unload of the equipment, optimization of control algorithms, abnormal switching of equipment faults and the like, the control strategy outputs a system overall test analysis report, a test result is obtained, and whether the control process of the control program is abnormal or not is determined.
As shown in fig. 3, in an embodiment, the unit energy efficiency planning system further includes: a pre-debug module 470;
the pre-debugging module 470 calls a control program of the test platform simulation unit to test, calls a simulation platform to simulate a control strategy corresponding to the final unit system and a physical environment required by the test, and pre-debugs the final unit system to complete the pre-debugging of the final unit system.
The control strategy corresponding to the final unit system, the physical environment required by the test and the control cabinet of the unit are simulated by the test platform simulation unit control program test and the simulation platform simulation, a scene for simulating the operation of the unit is built, and the operation of the unit is simulated by changing relevant parameters of the operation of the unit, so that the pre-debugging of the final unit system is realized. Such as: after a scene for simulating the operation of the unit is built, the operation condition of the final unit system in one year, data possibly generated in the operation in one year and the like are simulated. The final pre-debugging of the unit system is completed by building a scene simulating the operation of the unit, only the external wiring of the control cabinet needs to be checked on site, and the debugging work of the site system can be completed quickly.
The energy efficiency planning system of the unit according to the above embodiment may be applied to each stage of the overall energy efficiency management shown in fig. 4.
In the model selection stage of system scheme design, the data acquisition module 310 acquires the meteorological parameters of the unit pre-installation area and the cold and hot loads of the unit pre-installation building, and the model selection scheme generation module 320 generates various model selection schemes according to the meteorological parameters and the cold and hot loads, wherein the model selection schemes comprise model selection schemes and equipment combinations corresponding to the model selection schemes, namely: and determining the type selection scheme, namely determining the corresponding equipment combination.
In the energy consumption prediction stage in the bidding process, the control strategy determination module 330 predicts the energy consumption of each type selection scheme based on each control strategy and determines the control strategy corresponding to each type selection scheme; the performance prediction module 340 obtains the predicted energy efficiency ratio of each type selection scheme according to the control strategy corresponding to each type selection scheme, predicts the cost performance of each type selection scheme, and obtains the predictive cost ratio data of each type selection scheme; the model selection scheme determining module 350 determines a model selection scheme and a corresponding control strategy of the final unit system from the model selection schemes according to the predicted energy efficiency ratio of each model selection scheme and the cost performance data of each model selection scheme. The bid rate can be improved based on the determined model selection scheme of the final unit system and the corresponding control strategy according to the achieved energy efficiency ratio data, and therefore orders are taken.
After taking an order, producing a corresponding product based on the model selection scheme of the final unit system, and testing the product, wherein the product testing comprises control strategy testing, control cabinet testing and program testing, the control strategy testing calls a simulation platform to simulate the final unit system designed based on the model selection scheme through a control strategy testing module 360, and the control strategy corresponding to the final unit system is adopted to test the final unit system, so that a control strategy testing result corresponding to the final unit system is obtained. The control strategy can be tested based on the produced product, and the energy efficiency ratio data required to be achieved is agreed to determine whether the product meets the standard. When the energy efficiency ratio data is not reached, the control strategy optimization module 370 is used for calling a simulation platform to simulate the final unit system according to the control strategy test result corresponding to the final unit system, strategy optimization is carried out on the control strategy corresponding to the final unit system based on the group control strategy, and the control strategy which reaches the required energy efficiency ratio data is determined as the final control strategy of the final unit system.
The control cabinet test module 450 calls the simulation platform to perform the equipment test on the control cabinet of the unit, so as to obtain the control cabinet test result of the unit. The program test module 460 calls the test platform to perform the control program test of the unit, and determines whether the control process of the control program is abnormal.
After the pre-debugging is completed and the engineering is in the checking and accepting stage, the simulation platform is called through the checking and accepting module 380, based on the final control strategy and the field data acquired in real time, the energy consumption analysis is carried out on the final unit system, and the energy consumption analysis result is obtained for energy consumption checking. And analyzing the running condition of the equipment and whether the system runs efficiently. If the expected effect is not achieved, the reason is analyzed.
In the using stage, acquiring parameters such as field meteorological parameters, building loads, equipment energy consumption and the like returned by the big data platform, carrying out simulation test in real time through the simulation platform, keeping the operation control strategy of the simulation platform consistent with the actual operation strategy in the field, and carrying out real-time prediction on the energy consumption; and (5) carrying out calibration with the site, and analyzing abnormal conditions by analyzing actual measurement data under all working conditions if the site operation does not reach a predicted value. And virtual calculation is carried out by adopting each preselected control strategy through a simulation platform, and the optimal control strategy is selected according to the highest energy efficiency comprehensive ratio. And updating the optimal control strategy into a final control strategy of the final unit system, so that the final unit system always keeps high-efficiency operation. The preselected control strategy can be a built-in control strategy or a control strategy customized by a user.
The energy efficiency planning system of the unit according to the above embodiment may be applied to each stage of the whole-process management shown in fig. 5.
In the consulting or bidding stage of the buyer, the data acquisition module 310 and the model selection scheme generation module 320 of the unit energy efficiency planning system may generate a plurality of model selection schemes and equipment combination schemes matched with the model selection schemes according to the built-in local meteorological parameters and the site load (building cold and heat load), where the meteorological parameters generally include: temperature, humidity. The performance prediction module 340 of the unit energy efficiency planning system can perform model selection scheme energy consumption calculation according to a control strategy, can perform energy consumption calculation in a self-defined mode in time consumption, year, refrigeration season, month, day and other time periods, and obtains energy consumption in corresponding time periods. And a performance prediction module 340 of the unit energy efficiency planning system obtains a cost return period according to the cost input and the return.
In the production and shipment stage, an order tracking module of the unit energy efficiency planning system can track the production progress of the product. The control strategy testing module 360, the control cabinet testing module 450 and the program testing module 460 of the unit energy efficiency planning system can test a produced control cabinet, test a selected control strategy, test a control program, and store each test result or send the test result to a corresponding responsible person. After the test is passed, the buyer is delivered, and an order tracking module of the unit energy efficiency planning system can track logistics.
In the debugging and acceptance stage, the installation progress in the installation process can be recorded by an order tracking module of the unit energy efficiency planning system, the installation progress of a product is tracked, the progress in the product debugging process can be followed, the checking and acceptance module 380 of the unit energy efficiency planning system can transmit real-time engineering field Data which is specially used for converting serial port Data into IP Data or converting the IP Data into serial port Data and transmitting the serial port Data through a wireless communication network through a DTU (Data Transfer unit), a simulation platform is called, the checking of energy consumption is realized, and the running energy consumption of the system meets the expectation. And an order tracking module of the unit energy efficiency planning system records the acceptance of the project.
In the using stage, the monitoring module 390 of the unit energy efficiency planning system accesses the server of the final unit system through the internet, and can check the system operation condition in real time to realize the real-time monitoring of the system operation. The physical examination module 410 of the unit energy efficiency planning system automatically analyzes reasons according to the recent running condition, abnormal condition and energy efficiency running condition of the equipment, and automatically generates a physical examination report to realize system physical examination. A fault diagnosis module of the unit energy efficiency planning system can perform early warning on equipment faults through big data analysis according to the operation condition of field equipment; when equipment fails, the fault reason is automatically analyzed, an effective solution is provided, and fault diagnosis and prediction are realized. And a warranty reminding module of the unit energy efficiency planning system can analyze the big data, and the equipment automatically carries out maintenance on-line reminding according to the service time.
The energy consumption real-time prediction module 420 of the unit energy efficiency planning system can call the simulation platform to perform simulation test in real time according to parameters such as field meteorological parameters, building loads, equipment energy consumption and the like returned by the big data platform, and the operation control strategy of the simulation platform is consistent with the actual operation strategy in the field to perform energy consumption real-time prediction; and (4) carrying out calibration with the site, if the site operation does not reach a predicted value, analyzing abnormal conditions by analyzing all-working-condition actual measurement data, and realizing deep analysis of energy consumption. The control strategy adjusting module 440 of the unit energy efficiency planning system can call the simulation platform based on the parameters such as field meteorological parameters, building loads, equipment energy consumption and the like returned by the big data platform, perform virtual calculation through the built-in control strategy of the simulation platform, and optimally select the optimal control strategy according to the energy efficiency comprehensive ratio. And the latest control strategy is updated to the field group control system, so that the user system always keeps high-efficiency operation, and the strategy optimization online updating is realized.
All or part of each module in the unit energy efficiency planning system can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
It will be understood by those skilled in the art that all or part of the functions of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a non-volatile computer readable storage medium, and when executed, may include the processes of the above embodiments of the methods. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (16)
1. An energy efficiency planning system for an aircraft, the system comprising: the device comprises a data acquisition module, a model selection scheme generation module, a control strategy determination module, a performance prediction module and a model selection scheme determination module;
the data acquisition module acquires relevant unit operation parameters; the unit operation related parameters comprise the cold and heat load of a unit pre-installation building, and the cold and heat load of the unit pre-installation building comprises the maximum instantaneous cold load and the minimum instantaneous cold load which are determined according to the using place, the using area and the unit area cold load;
the model selection scheme generation module generates various model selection schemes according to the relevant parameters of the unit operation;
the control strategy determining module predicts the energy consumption of each model selection scheme based on each control strategy and determines the control strategy with the highest predicted energy consumption of each control strategy in each model selection scheme as the control strategy corresponding to each model selection scheme; the predicted energy consumption is the sum of the running energy consumption accumulations of the equipment in the calculation period;
the performance prediction module determines the performance of each scheme based on the highest performance of the control strategy in each selected scheme; the highest performance of the control strategy comprises predictive cost ratio data;
and the model selection scheme determining module determines the model selection scheme and the corresponding control strategy of the final unit system based on the model selection scheme with the highest predictive price ratio data.
2. The system of claim 1, further comprising: a control strategy testing module;
and the control strategy testing module calls a simulation platform to simulate the final unit system based on the model selection scheme, and adopts the control strategy corresponding to the final unit system to carry out simulation test on the final unit system so as to obtain a control strategy testing result corresponding to the final unit system.
3. The system of claim 2, further comprising: a control strategy optimization module;
and the control strategy optimization module calls the simulation platform to simulate the final unit system according to a control strategy test result corresponding to the final unit system, carries out strategy optimization on the control strategy corresponding to the final unit system based on a group control strategy and determines the final control strategy of the final unit system.
4. The system of claim 3, further comprising: a checking and accepting module;
and the verification and acceptance module calls the simulation platform, and performs energy consumption analysis on the final unit system based on the final control strategy and the field data acquired in real time to obtain an energy consumption analysis result and perform energy consumption verification.
5. The system of claim 3, further comprising: a monitoring module;
and the monitoring module acquires air conditioner data in the final unit system server by accessing the final unit system server in real time and monitors the running state information of the final unit system in real time.
6. The system of claim 5, further comprising: a physical examination module;
the physical examination module acquires the recent running condition, the abnormal condition and the energy efficiency running condition of the final unit system, analyzes the reason of the abnormal condition and generates a physical examination report.
7. The system of claim 5, further comprising: an energy consumption real-time prediction module;
the energy consumption real-time prediction module acquires the field meteorological parameters, the building load and the equipment energy consumption of the final unit system in real time; according to the meteorological parameters and the cold and hot loads, adopting a final control strategy to predict the energy consumption of the final unit system, and obtaining the current predicted energy consumption; determining whether the energy consumption of the equipment reaches a predicted value according to the current predicted energy consumption; and when the energy consumption of the equipment does not reach the predicted value, carrying out anomaly analysis based on the actual measurement data of all working conditions to obtain an anomaly analysis result.
8. The system of claim 7, further comprising: an energy efficiency ratio estimation module;
the energy efficiency ratio estimation module acquires historical building load data; acquiring weather forecast information of the final unit system installation area from a meteorological platform; and estimating the energy efficiency ratio of the pre-selected control strategy in the future preset time period according to the building load historical data and the weather forecast information to obtain the estimated energy efficiency ratio of the pre-selected control strategy in the future preset time period.
9. The system of claim 8, further comprising: a control strategy adjusting module;
the control strategy adjusting module acquires the field meteorological parameters, building load and equipment energy consumption of the final unit system; based on the meteorological parameters and the cold and hot loads, adopting each preselected control strategy to predict the energy consumption of the final unit system, and obtaining the predicted energy consumption of each preselected control strategy; and sending the control strategy closest to the energy consumption of the equipment in the predicted energy consumption of each preselected control strategy to the final unit system server, so that the final unit system server updates the final control strategy of the final unit system according to a preset strategy updating mode.
10. The system of claim 9, wherein the control strategy adjustment module is further configured to:
analyzing the estimated energy efficiency ratio of each preselected control strategy in a future preset time period, determining the optimal control strategy in the future preset time period, and sending the optimal control strategy in the future preset time period to the final unit system server.
11. The system according to any one of claims 1-10, further comprising: a control cabinet test module;
the control cabinet testing module calls the simulation platform to provide a physical environment required by testing, and performs equipment testing on the control cabinet of the unit to obtain a control cabinet testing result of the unit.
12. The system according to any one of claims 1-10, further comprising: a program test module;
and the program test module calls the test platform to test the control program of the unit and determines whether the control process of the control program is abnormal.
13. The system according to any one of claims 1-10, wherein the system further comprises a pre-debug module;
the pre-debugging module calls a control program of a test platform simulation unit to test, calls a simulation platform to simulate a control strategy corresponding to the final unit system and a physical environment required by the test, and pre-debugs the final unit system to finish the pre-debugging of the final unit system.
14. The system of claim 1, wherein the performance prediction module comprises: an energy efficiency ratio prediction unit;
the energy efficiency ratio prediction unit predicts the energy efficiency ratio of each type selection scheme to obtain the predicted energy efficiency ratio of each type selection scheme; and determining the performance of each selected scheme according to the predicted energy efficiency ratio of each selected scheme.
15. The system according to any one of claims 1 or 14, wherein the performance prediction module comprises: a cost performance prediction unit;
the cost performance prediction unit predicts the cost performance of each model selection scheme to obtain predictive cost ratio data of each model selection scheme; determining performance of each of the selection scenarios based on the predictive cost ratio data for each of the selection scenarios.
16. The system according to claim 15, wherein the cost performance prediction unit includes: a cost and energy saving data acquisition subunit and a cost performance analysis subunit;
the cost energy-saving data acquisition subunit is used for acquiring cost data and energy-saving data of each type selection scheme; and the cost performance analysis subunit performs cost performance analysis according to the cost data and the energy-saving data to obtain predictive cost performance data of each type selection scheme.
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| CN111797460A (en) * | 2020-05-21 | 2020-10-20 | 武汉理工大学 | Method and device for selecting type of equipment of cruise ship air conditioning system |
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| CN116007137A (en) * | 2022-12-22 | 2023-04-25 | 珠海格力电器股份有限公司 | Control strategy self-optimizing method and device of air conditioning system |
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