WO2020237468A1

WO2020237468A1 - Method, apparatus and system for determining temperature setting value, and storage medium and processor

Info

Publication number: WO2020237468A1
Application number: PCT/CN2019/088598
Authority: WO
Inventors: 汤琦; 曲颖; 罗章维; 王焦剑; 李聪超; 刘晓南; 鲁雷
Original assignee: 西门子（中国）有限公司
Priority date: 2019-05-27
Filing date: 2019-05-27
Publication date: 2020-12-03
Also published as: CN113825955A

Abstract

The present application relates to a method, apparatus and system for determining a temperature setting value, and a storage medium and a processor. The method comprises: a step of acquiring a comfort zone temperature range of each sub-time period from among a plurality of sub-time periods comprised in a future predetermined time period; a step of acquiring a plurality of first temperature value combinations, wherein each of the first temperature value combinations comprises a plurality of temperature values, each temperature value from among the plurality of temperature values corresponds to one sub-time period from among the plurality of sub-time periods, and each temperature value falls within the comfort zone temperature range corresponding to the sub-time period; and a step of determining a target temperature value combination from among the plurality of first temperature value combinations, such that by means of setting, according to the target temperature value combination, a temperature setting value in each sub-time period from among the plurality of sub-time periods, the operating state, in the future predetermined time period, of an air-conditioning cooling system is optimal. The technical solution of the present application can ensure the comfort of people in a building, and can also effectively reduce the energy consumption of an air-conditioning cooling system in the building.

Description

Method, device, system, storage medium and processor for determining temperature setting value

Technical field

This application relates to the field of temperature control. Specifically, this application relates to a method, device, system, storage medium, and processor for determining the temperature setting value of an air conditioning and cooling system.

Background technique

When considering building and building HVAC (HVAC, Heating Ventilation Air Conditioning) systems and related energy consumption and cost minimization, on-demand control specifications are very useful. In current practice, some demand control strategy decisions are usually applied to promote the balance of power supply and demand in buildings. These demand control strategy decisions include peak demand limiting, night setback control, and temperature compensation. Cycle (temperature-compensated duty cycling). Generally speaking, the existing cooling demand control strategy is to input expert experience and calendar events into the programmable logic controller in advance. In this case, these strategies lack flexibility and cannot dynamically reflect real-time changes in building cooling demand, so they are passive lagging control strategies. In addition, computational complexity problems arise when trying to find an optimal demand control strategy. Some control strategies will consider the hourly building temperature setting optimal value during a predetermined time period of the day, but in this case the calculation amount will be very large. For example, considering that there are ten adjustable temperatures in one hour, there may be 10^24 possibilities for the temperature setting for a predetermined time period of a day. Under such a large calculation background, it is unrealistic to calculate and update a set of optimal temperature value combinations for a predetermined time period in the future every hour.

Many buildings use high-performance modular direct digital control (DDC) monitoring field panels, such as the PXC modular series products to perform complex control, monitoring and energy management functions. The application related to the demand control strategy is applied this modularity during programming and is implemented by entering the required parameters. By using built-in applications (such as peak demand limits and temperature compensation duty cycles), users can adjust cooling balance load demand and supply, reducing overall energy usage. Since the demand control strategies of these modular products are specially designed, the control strategies under these strategies are static and cannot be based on real-time data (for example, cooling side data, such as the operating parameters of chillers and pumps, and dynamic electricity tariffs ) Dynamically update the cooling load demand.

Summary of the invention

The embodiments of the present application provide a method, device, system, storage medium, and processor for determining the temperature setting value of the air conditioning and cooling system to at least solve the problem of how to determine the optimal temperature setting for the air conditioning and cooling system in a building.

According to an aspect of the embodiments of the present application, a method for determining a temperature setting value of an air conditioning and cooling system in a building is provided, including: obtaining the comfort zone of each of the plurality of sub-time periods included in a predetermined time period in the future The temperature range step, where the comfort zone temperature range is the temperature range that makes the people in the building feel comfortable in the corresponding sub-time period, and the temperature range of each comfort zone is smaller than the temperature adjustment range of the air conditioning and cooling system; obtain a plurality of A temperature value combination step, wherein each first temperature value combination includes a plurality of temperature values, each temperature value of the plurality of temperature values corresponds to one of the plurality of sub-time periods, and each temperature value is in The temperature range of the comfort zone corresponding to the sub-time period; and the step of determining a target temperature value combination from a plurality of first temperature value combinations, so that each sub-time in the plurality of sub-time periods is set according to the target temperature value combination The temperature setting value of the segment, the air-conditioning and cooling system will perform best in the future scheduled time period.

According to another aspect of the embodiments of the present application, there is also provided a device for determining the temperature setting value of an air conditioning and cooling system in a building, including: a comfort zone module configured to execute acquiring a plurality of sub-times included in a predetermined time period in the future The steps of the comfort zone temperature range for each sub-period in the segment, where the comfort zone temperature range is the temperature range that makes the people in the building feel comfortable in the corresponding sub-period, and the temperature range of each comfort zone is less than the air conditioning cooling The temperature adjustment range of the system; the temperature value combination acquisition module is configured to perform the step of acquiring a plurality of first temperature value combinations, wherein each first temperature value combination includes a plurality of temperature values, and each temperature in the plurality of temperature values The value corresponds to one of the plurality of sub-periods, and each temperature value is in the comfort zone temperature range corresponding to the sub-period; the target temperature value combination determination module is configured to execute the first temperature The step of determining a target temperature value combination in the value combination, so that the temperature setting value of each of the plurality of sub-time periods is set according to the target temperature value combination, and the air-conditioning and cooling system will operate in the best predetermined time period in the future .

According to another aspect of the embodiments of the present application, a system for determining the temperature setting value of an air conditioning and cooling system in a building is also provided. The system includes: an air conditioning and cooling system; and determining the temperature setting of an air conditioning and cooling system in a building The value of the device, the device includes: a comfort zone module, configured to execute the step of obtaining the comfort zone temperature range of each of the plurality of sub-time periods included in the future predetermined time period, wherein the comfort zone temperature range is to make the building The temperature range within which the personnel feel comfortable in the corresponding sub-time period, and the temperature range of each comfort zone is smaller than the temperature adjustment range of the air-conditioning cooling system; the temperature value combination acquisition module is configured to perform multiple first temperature value combinations , Wherein each first temperature value combination includes a plurality of temperature values, each temperature value of the plurality of temperature values corresponds to one of the plurality of sub-time periods, and each temperature value is in the sub-time period In the corresponding comfort zone temperature range; the target temperature value combination determining module is configured to perform the step of determining a target temperature value combination from a plurality of first temperature value combinations, so that a plurality of sub-time periods are set according to the target temperature value combination For the temperature setting value of each sub-time period in, the air-conditioning and cooling system will perform best in the future scheduled time period.

According to another aspect of the embodiments of the present application, there is also provided a storage medium with a program stored on the storage medium, and when the program is executed by a computer including the storage medium, the computer executes the foregoing method.

According to another aspect of the embodiments of the present application, a processor is also provided, the processor is configured to run a program stored in a memory, wherein the processor executes the foregoing method when the program is running.

In this way, it is possible to provide the user with the optimal temperature setting value for each sub-period in the future predetermined time period, and determining the comfort zone temperature range reduces the amount of calculation to determine the optimal temperature setting value.

In the exemplary embodiment according to the above-mentioned method, device, system and storage medium, the step of determining a target temperature value combination from a plurality of first temperature value combinations includes: obtaining the temperature setting value of the air conditioning and cooling system and the air conditioning supply The temperature setting value between the cooling capacity provided by the cooling system-the corresponding relationship between the cooling capacity and the cooling between the cooling capacity provided by the air-conditioning cooling system and the power consumption required by the air-conditioning cooling system to provide the cooling capacity The corresponding relationship between the amount and the power consumption; the combination of the power consumption corresponding to the plurality of temperature setting values and the smallest first temperature value is used as the target temperature value combination.

In this way, it is possible to determine for the air-conditioning and cooling system the temperature setting value that enables the people in the building to feel comfortable in each sub-time period in the future predetermined time period.

In the exemplary embodiment according to the above-mentioned method, device, system and storage medium, the step of determining a target temperature value combination from a plurality of first temperature value combinations includes: obtaining the temperature setting value of the air conditioning and cooling system and the air conditioning supply The temperature setting value between the cooling capacity provided by the cooling system-the corresponding relationship between the cooling capacity, the cooling between the cooling capacity provided by the air-conditioning cooling system and the power consumption required by the air-conditioning cooling system to provide the cooling capacity The corresponding relationship between the amount of electricity and the electricity consumption and the rate of each sub-time period associated with the air conditioning and cooling system; according to the temperature setting value-the corresponding relationship of cooling capacity, the corresponding relationship of cooling capacity-power consumption and the rate, determine the The electricity fee corresponding to the temperature setting value of each sub-time period; and the minimum first temperature value combination of the electricity fee corresponding to the plurality of temperature setting values included is used as the target temperature value combination.

In this way, it is possible to determine the temperature setting value for the air-conditioning and cooling system that enables the people in the building to feel comfortable in each sub-time period in the future predetermined time period.

In the exemplary embodiment according to the above-mentioned method, device, system and storage medium, it further includes: acquiring historical data, the historical data including the actual temperature of each sub-period included in the air-conditioning and cooling system in a historical period of time Set value and actual cooling capacity; obtain forecast data, including forecasted cooling capacity according to the corresponding relationship between temperature setting value and cooling capacity; establish a loss function based on historical data and forecast data to determine the predicted cooling capacity and actual cooling capacity The prediction error between the cooling capacity; adjust the temperature setting value-cooling capacity correspondence relationship according to the prediction error; and/or obtain historical data, which includes each sub-time period included in the air-conditioning cooling system in a historical time period The actual cooling capacity and the actual power consumption; the forecast data includes the predicted power consumption according to the corresponding relationship between the cooling capacity and the power consumption; the loss function is established based on the historical data and the predicted data to determine the predicted consumption The prediction error between the electricity and the actual electricity consumption; the corresponding relationship between the cooling capacity and the electricity consumption is adjusted according to the forecast error.

In this way, the parameters of the optimization algorithm can be updated according to the error between the historical data and the predicted value, so that the temperature setting value of the air conditioning and cooling system determined in the future is more accurate.

In the exemplary embodiment according to the above-mentioned method, device, system and storage medium, the method further includes obtaining the time length of the adjusted target temperature value combination, and determining the adjusted target temperature value combination within the time length, and determining the adjusted target temperature within the time length The value combination includes: after the step of obtaining a plurality of first temperature value combinations, for at least one first temperature value combination in the first temperature value combination, for each sub-period of some or all of the sub-periods, based on the preset Set the step length to adjust the temperature value of the sub-time period to obtain at least one second temperature value combination, wherein the temperature value of each sub-time period in the at least one second temperature value combination is at the comfort zone temperature corresponding to the sub-time period Range; and after the step of determining a target temperature value combination from a plurality of first temperature value combinations, determining an adjusted target temperature value combination from the first temperature value combination and the second temperature value combination so that the The target temperature value is combined to set the temperature setting value of each of the plurality of sub-time periods, and the air-conditioning and cooling system will perform best in the future predetermined time period.

In this way, the determination of the temperature setting value of the air conditioning and cooling system is completed within a prescribed time period, and the optimization calculation result is repeated within the time period to update the temperature setting value to the optimal value.

In the exemplary embodiments according to the above-mentioned method, device, system and storage medium, the step of determining an adjusted target temperature value combination from the first temperature value combination and the second temperature value combination includes: acquiring the air conditioning and cooling system The temperature setting value between the temperature setting value and the cooling capacity provided by the air conditioning cooling system-the corresponding relationship between the cooling capacity, the cooling capacity provided by the air conditioning cooling system and the electricity required for the cooling capacity provided by the air conditioning cooling system The corresponding relationship between the cooling capacity-power consumption and the rate of each sub-period associated with the air-conditioning cooling system; according to the temperature setting value-cooling capacity corresponding relationship, cooling capacity-power consumption corresponding relationship And the rate, determine the electricity rate corresponding to the temperature setting value of each sub-time period; and use the minimum first temperature value combination or the second temperature value combination of the included plural temperature setting values and the smallest first temperature value combination as the target temperature value combination.

In this way, it is possible to determine an updated temperature setting value for the air-conditioning and cooling system that makes the people in the building feel comfortable in each sub-time period in the future predetermined time period and has the least electricity cost.

In the exemplary embodiments according to the above-mentioned method, device, system and storage medium, the step of determining an adjusted target temperature value combination from the first temperature value combination and the second temperature value combination includes: acquiring the air conditioning and cooling system The temperature setting value-cooling capacity correspondence between the temperature setting value and the cooling capacity provided by the air conditioning cooling system, and the cooling capacity provided by the air conditioning cooling system and the electricity required for the cooling capacity provided by the air conditioning cooling system The corresponding relationship between the cooling capacity and the power consumption; the minimum first temperature value combination or the second temperature value combination of the power consumption corresponding to the plurality of temperature setting values included is used as the target temperature value combination.

In this way, it is possible to determine an updated temperature setting value for the air-conditioning and cooling system that makes the people in the building feel comfortable in each sub-time period in the future predetermined time period and has the least power consumption.

In this way, the parameters of the optimization algorithm can be updated based on the error between the historical data and the predicted value, so that the updated temperature setting value of the air conditioning and cooling system determined in the future is more accurate.

In the exemplary embodiment according to the above-mentioned method, device, system, and storage medium, it further includes: obtaining prediction errors of multiple historical time periods; if the prediction error increases with time in the multiple historical time periods, adjusting In the step of adjusting the target temperature value combination within the duration of the target temperature value combination, at least one of the following is performed: extending the length of the duration, reducing the preset step size, increasing the temperature value of the sub-time period based on the preset step size to obtain The number of combinations of at least one second temperature value.

In this way, the optimization algorithm for adjusting the updated temperature setting value by calculating the prediction error allows a more accurate result to be obtained in the subsequent process of determining the updated temperature setting value of the air conditioning and cooling system.

In the exemplary embodiments according to the above-mentioned method, device, system and storage medium, it is further included in each new sub-period: performing acquisition of each of the plurality of sub-periods included in the future predetermined period of time. The steps of the temperature range of the comfort zone, the steps of obtaining a plurality of first temperature value combinations, and the step of determining a target temperature value combination from the plurality of first temperature value combinations; and the plurality of sub-times in the determined target temperature value combination The temperature value of the segment is used as the temperature value of the corresponding plurality of sub-periods in one of the plurality of first temperature value combinations to be acquired in the next predetermined time period in the future.

In this way, the temperature setting value of the air-conditioning and cooling system is re-determined in each new sub-time period, thereby obtaining a more accurate temperature setting value for each sub-time period.

In the exemplary embodiments according to the above-mentioned method, device, system and storage medium, it is further included in each new sub-period: performing acquisition of each of the plurality of sub-periods included in the future predetermined period of time. The steps of the temperature range of the comfort zone, the steps of obtaining a plurality of first temperature value combinations, and the step of determining a target temperature value combination from the plurality of first temperature value combinations; execute the time length of obtaining and adjusting the target temperature value combination, and within the time length The step of determining the adjusted target temperature value combination; and using the determined temperature value of the plurality of sub-time periods in the adjusted target temperature value combination as one of the plurality of first temperature value combinations to be acquired in the next predetermined time period in the future The temperature values of the corresponding plural sub-time periods in the temperature value combination.

In this way, the temperature setting value of the air-conditioning cooling system is re-determined in each new sub-period and the optimization calculation result is repeated within the specified time period to update the temperature setting value to the optimal value, thereby obtaining a more accurate temperature Settings.

In the exemplary embodiments according to the above-mentioned method, device, system and storage medium, the step of determining the adjusted target temperature value combination within the time period is periodically repeated within the time period; and the length of each sub-time period is equal, And the duration of adjusting the target temperature value combination is less than the length of the sub-period.

In this way, the update process of the optimization calculation result to update the temperature setting value to the optimal value is repeatedly performed within a prescribed time period, and sufficient operating time is reserved for the air conditioning and cooling system to adjust the temperature.

In the exemplary embodiments according to the above-mentioned method, device, system and storage medium, obtaining the comfort zone temperature range includes: obtaining one or more of the outdoor temperature, humidity, and flow of people associated with the building; according to one or The plural pieces of information determine the comfort zone temperature range of each of the plural sub-time periods included in the future predetermined time period.

In this way, the value range of the temperature setting value of the air conditioning and cooling system that makes the people in the building comfortable is determined, and the calculation amount for determining the temperature setting value is reduced.

The technical solution of this application provides the temperature setting value of the best (to make indoor occupants feel comfortable and minimize energy consumption) for each sub-time period in the future predetermined time period. In addition, the technical solution of this application provides data The driving model can dynamically update the algorithm of the temperature setting value according to the prediction error to ensure the accuracy of the calculation. In addition, users can adjust model parameter values to explore energy saving opportunities. The data-driven building cooling demand optimization solution proposed in this application has the characteristics of high flexibility and modular design. Therefore, the solution is suitable for different types of buildings, such as single-region or multi-region buildings, commercial buildings or office buildings. This solution is also suitable for buildings with different air-conditioning and cooling systems, such as all-water systems or air-water systems. The demand side (for example, the control end of the building's air-conditioning and cooling system) optimization solution proposed in this application can be used in conjunction with the corresponding optimization solution of the cooling test (the building's air-conditioning and cooling system) to further reduce the total energy usage of the building.

Description of the drawings

The drawings described here are used to provide a further understanding of the application and constitute a part of the application. The exemplary embodiments and descriptions of the application are used to explain the application and do not constitute an improper limitation of the application. In the attached drawing:

Fig. 1 is a flowchart of a method for determining a temperature setting value of an air conditioning and cooling system in a building according to an embodiment of the present application;

Figure 2 is a schematic diagram of an exemplary comfort zone temperature range;

Fig. 3 is a schematic diagram of a method for determining a target temperature value combination according to an exemplary embodiment of the present application;

4 is a schematic diagram of another method for determining a combination of target temperature values according to an exemplary embodiment of the present application;

Figure 5 is a schematic diagram of an exemplary electricity rate;

Fig. 6 is a schematic diagram of adjusting an algorithm according to a prediction error according to an exemplary embodiment of the present application;

Fig. 7 is a schematic diagram of adjusting an algorithm according to a prediction error according to an exemplary embodiment of the present application;

Fig. 8 is a schematic diagram of a method for optimizing a temperature setting value according to an exemplary embodiment of the present application;

FIG. 9 is a flowchart of a method for determining a combination of the electricity bill and the minimum temperature value for the next 24 hours according to an exemplary embodiment of the present application;

Fig. 10 is a performance curve of a refrigerator according to an exemplary embodiment of the present application;

Fig. 11 is a schematic diagram showing a determination result of an optimal temperature value combination according to an exemplary embodiment of the present application;

Fig. 12 is a schematic diagram showing a determination result of an optimal temperature value combination according to an exemplary embodiment of the present application;

Fig. 13 is a schematic diagram of a temperature range of a comfort zone according to an embodiment of the present application;

14 is a schematic diagram of a modified comfort zone temperature range according to an embodiment of the present application;

15 is a schematic diagram of a device for determining a temperature setting value of an air conditioning and cooling system in a building according to an embodiment of the present application;

Fig. 16 is a schematic diagram of an apparatus according to an exemplary embodiment of the present application;

FIG. 17 is a schematic diagram of a system for determining a temperature setting value of an air conditioning and cooling system in a building according to an embodiment of the present application;

Fig. 18 is a schematic diagram of a system according to an exemplary embodiment of the present application.

Description with icon number:

S102, S104, S106: steps;

S302, S304: steps;

S402, S404, S406: steps;

S602, S604, S606: steps;

S702, S704, S706: steps;

S802, S804, S806: steps;

S902, S904, S906, S908, S910, S912, S914, S916, S918: steps;

1: Device;

102: Comfort zone module;

104: Temperature value combination acquisition module;

106: Target temperature value combination determination module;

108: Correspondence adjustment module;

110: Target temperature value combination adjustment module;

112: Adjust and optimize the module;

114: Target temperature value combination update module;

3: Air conditioning and cooling system;

302: sensor;

304: Control feedback module;

5: System.

Detailed ways

In order to enable those skilled in the art to better understand the solution of the application, the technical solutions in the embodiments of the application will be clearly and completely described below in conjunction with the drawings in the embodiments of the application. Obviously, the described embodiments are only It is a part of the embodiments of this application, not all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work should fall within the protection scope of this application.

It should be noted that the terms "first" and "second" in the description and claims of the application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations of them are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules or units is not necessarily limited to clearly listed Instead, those steps or modules or units listed may include other steps or modules or units that are not clearly listed or are inherent to these processes, methods, products, or equipment.

According to an embodiment of the present application, a method for determining the temperature setting value of an air conditioning and cooling system in a building is provided. Fig. 1 is a flowchart of a method for determining a temperature setting value of an air conditioning and cooling system in a building according to an embodiment of the present application. As shown in FIG. 1, the method for determining the temperature setting value of an air conditioning and cooling system in a building according to an embodiment of the present application includes the following steps.

Step S102, a step of obtaining the temperature range of the comfort zone of each of the plurality of sub-time periods included in the predetermined time period in the future, wherein the temperature range of the comfort zone makes the people in the building feel comfortable in the corresponding sub-time period The temperature range, and the temperature range of each comfort zone is smaller than the temperature adjustment range of the air conditioning and cooling system.

The goal of introducing the comfort zone temperature range is to set an hourly search space for the optimal temperature, thereby providing an optimization algorithm. When the temperature value of the air conditioning and cooling system of a building is set within the temperature range of the comfort zone, people in the building will feel comfortable. More specifically, the comfort zone temperature range provides the upper and lower limits of the hourly temperature setting value of the air conditioning and cooling system. In this application, the optimal temperature set point is found from the comfort zone temperature range, so that the People feel comfortable, but at the same time use the least electricity for cooling or require the least electricity bill. By considering the temperature range of the comfort zone instead of the entire adjustable temperature range of the air-conditioning and cooling system, the calculation cost of the optimization algorithm can also be minimized. The temperature range of the comfort zone temperature range (such as working hours and non-working hours) must be narrower than the air-conditioning supply. Full adjustable temperature range of the cooling system. In fact, the user can set the comfort zone temperature range according to the comfort zone temperature range set by himself or according to other commonly used (for example, ASHRAE standard) thermal comfort zone models (for example, predictive mean voting (PMV) index). Fig. 2 is a schematic diagram of an exemplary comfort zone temperature range. From the exemplary comfort zone temperature range shown in FIG. 2, it can be seen that the comfort zone temperature range (the value range of temperature values that indoor personnel feel comfortable) is wider during non-working hours than during working hours. In addition, the comfort zone temperature range should be dynamically updated based on, for example, outdoor ambient temperature, room occupancy rate, and room relative humidity to obtain accurate results. In the predetermined time period shown in Figure 2, such as the next 24 hours, for each of the sub-time periods, such as every hour, the temperature range of the comfort zone changes. Therefore, the temperature setting value of the air conditioning and cooling system can be changed from every Select within the hourly comfort zone temperature range, instead of selecting from the entire temperature setting range of the air-conditioning cooling system, thereby reducing the amount of calculation for selecting the temperature value.

In addition, users can also customize the hourly comfort zone temperature range. No matter which method is used, the temperature range of the comfort zone should be updated in real time (for example, every sub-time period or every hour) to ensure a reasonable and accurate calculation of the temperature setting of the air conditioning and cooling system.

After obtaining the comfort zone temperature range for a predetermined period of time in the future, proceed to step S104 to obtain a plurality of first temperature value combinations, wherein each first temperature value combination includes a plurality of temperature values, and each of the plurality of temperature values The temperature value corresponds to one of the plurality of sub-time periods, and each temperature value is in the comfort zone temperature range corresponding to the sub-time period. For example, in the next 24 hours, corresponding to the temperature range of the comfort zone for each hour, a temperature value is selected from the temperature range of the comfort zone to obtain 24 temperature values to form a temperature value combination. Such a combination of temperature values represents the temperature setting value of the air-conditioning cooling system in each of the 24 hours in the next 24 hours. Obtain a plurality of such temperature value combinations as a selection range for selecting the temperature value of the air conditioning and cooling system in the next 24 hours. For example, from the plurality of such temperature value combinations, a desired temperature value combination is selected.

Next, step S106 is performed, that is, a step of determining a target temperature value combination from the plurality of first temperature value combinations, so that the temperature setting value of each of the plurality of sub-time periods is set according to the target temperature value combination, and the air conditioner The cooling system will perform best in the future scheduled time period.

The way to determine the best operating state of the air-conditioning and cooling system for a predetermined period of time in the future can be set according to needs. The exemplary embodiment of this application considers the total heat output of the building, which includes infrastructure, such as heat generated by IT equipment and lighting, and air conditioning systems, such as chillers, chilled water pumps, condenser water pumps, secondary chilled water pumps, PAU, Power supply for AHU and cooling tower. The data-driven solution proposed here can flexibly consider any configuration. For example, according to the following embodiments of the present application, it is determined that the operating state of the air conditioning and cooling system is optimal.

Fig. 3 is a schematic diagram of a method for determining a target temperature value combination according to an exemplary embodiment of the present application. As shown in FIG. 3, according to an exemplary embodiment of the present application, the step of determining a target temperature value combination from a plurality of first temperature value combinations includes: step S302, acquiring the temperature setting value of the air conditioning cooling system and the air conditioning cooling system The temperature setting value between the cooling capacity provided-the correspondence between the cooling capacity and the cooling capacity between the cooling capacity provided by the air conditioning cooling system and the power consumption required by the air conditioning cooling system to provide cooling capacity- The power consumption correspondence relationship, and step S304, the minimum first temperature value combination of the power consumption corresponding to the plurality of temperature setting values included is used as the target temperature value combination.

The corresponding relationship between the temperature setting value and the cooling capacity is determined based on the temperature setting value of the air conditioning and cooling system and the corresponding cooling capacity. The corresponding relationship between cooling capacity and power consumption is determined based on the performance curve of the air-conditioning cooling system. According to the technical solution of the exemplary embodiment of the present application, the goal is to calculate the temperature value combination with the least power consumption of the air conditioning and cooling system in the next predetermined time period, for example, the next 24 hours. Set the temperature of each sub-period of the air-conditioning and cooling system in the predetermined time period according to the multiple temperature values included in the temperature value combination, and the power consumption in the predetermined time period is the least, so that the air-conditioning and cooling system is operating in the best state .

Fig. 4 is a schematic diagram of another method for determining a target temperature value combination according to an exemplary embodiment of the present application. As shown in FIG. 4, according to an exemplary embodiment of the present application, the step of determining a target temperature value combination from a plurality of first temperature value combinations includes: step S402, obtaining the temperature setting value of the air conditioning cooling system and the air conditioning cooling system The temperature setting value between the cooling capacity provided-the corresponding relationship between the cooling capacity, the cooling capacity between the cooling capacity provided by the air conditioning cooling system and the power consumption required by the air conditioning cooling system to provide cooling capacity- The corresponding relationship between power consumption and the rate of each sub-time period associated with the air conditioning and cooling system; step S404, according to the temperature setting value-the corresponding relationship between cooling capacity, the corresponding relationship between cooling capacity-power consumption and the rate, determine The electricity fee corresponding to the temperature setting value of each sub-time period; and step S406, the minimum first temperature value combination of the electricity fee corresponding to the plurality of temperature setting values included is used as the target temperature value combination.

The corresponding relationship between the temperature setting value and the cooling capacity is determined based on the temperature setting value of the air conditioning and cooling system and the corresponding cooling capacity. The corresponding relationship between cooling capacity and power consumption is determined based on the performance curve of the air-conditioning cooling system. The rate is related to the building, and the rate is different at different times, so in different time periods, the electricity bill corresponding to the electricity consumption of the building changes according to the rate. According to the technical solution of another exemplary embodiment of the present application, the goal is to calculate the temperature value combination that requires the least electricity cost for cooling in the next predetermined time period, for example, the next 24 hours.

Generally speaking, the electricity tariff rate of a city is fluctuating and time-sharing (for example, peak-to-valley electricity tariff). Therefore, it is necessary to consider the electricity metering mode applied to the city or region, and construct a reasonable time-sharing or component electricity tariff pricing model. The electricity tariff rate of any type of electricity consumer may also vary according to the time of use (time-of-use, TOU), because during the peak period of electricity use, the electricity tariff rate is set to be higher, and when the electricity demand is low , The electricity rate is set to be lower. Fig. 5 is a schematic diagram of an exemplary electricity rate. As shown in Figure 5, during the peak period of electricity consumption, such as working hours, the tariff rate is higher, while in the time period after get off work, the demand for electricity is small and the electricity tariff rate is lower. According to the corresponding relationship between tariff and time, and the electricity consumption of the air conditioning and cooling system in each sub-period, such as each hour, the hourly electricity cost of the air conditioning and cooling system can be determined, and then the predetermined time period, such as the next 24 Hour’s electricity bill. According to the multiple temperature values included in the temperature value combination, the temperature of each sub-time period of the air-conditioning and cooling system is set in the predetermined time period, and the electricity cost in the predetermined time period is the least, so that the air-conditioning and cooling system has the best operating state.

In the method according to the exemplary embodiment of the present application, the cooling capacity and power consumption associated with the air conditioning and cooling system are determined according to the temperature setting value-cooling capacity correspondence and the cooling capacity-electricity consumption correspondence, There may be discrepancies with the actual cooling capacity and power consumption of the air conditioning cooling system. In order to determine the error and optimize the method according to the embodiment of the present application according to the error, the following technical solutions can be adopted.

6 and 7 are schematic diagrams of adjusting an algorithm according to a prediction error according to an exemplary embodiment of the present application.

As shown in FIG. 6, the method according to the exemplary embodiment of the present application further includes: step S602, acquiring historical data, the historical data including the actual temperature setting of each sub-time period included in the air-conditioning and cooling system in a historical time period Step S604, obtain predicted data, including the predicted cooling capacity based on the corresponding relationship between the temperature setting value and the cooling capacity; and Step S606, establish a loss function based on historical data and predicted data, and determine the prediction The prediction error between the cooling capacity and the actual cooling capacity; the corresponding relationship between the temperature setting value and the cooling capacity is adjusted according to the prediction error.

As shown in FIG. 7, the method according to the exemplary embodiment of the present application further includes: step S702, acquiring historical data, the historical data including the actual cooling of each sub-time period included in the air-conditioning cooling system in a historical time period Step S704: Obtain forecast data. The forecast data includes the forecasted electricity consumption based on the corresponding relationship between cooling capacity and electricity consumption. Step S706: Build a loss function based on historical data and forecast data to determine the forecast The forecast error between the actual electricity consumption and the actual electricity consumption; the corresponding relationship between the cooling capacity and the electricity consumption is adjusted according to the forecast error.

According to the method of the exemplary embodiment of the present application, the method of calculating the combination of the optimal temperature setting value in the future predetermined time period, such as the next 24 hours, is based on a dynamically updated optimization algorithm, and the error can be automatically corrected to increase the hourly temperature Set the accuracy of the prediction of the value. Moreover, the optimization algorithm is time-constrained, that is, to find the optimal temperature value combination of the air conditioning and cooling system within a given calculation time, so as to minimize the total power consumption or electricity bills of the target building in the future.

Fig. 8 is a schematic diagram of a method for optimizing a temperature setting value according to an exemplary embodiment of the present application. As shown in FIG. 8, the method according to the exemplary embodiment of the present application further includes step S802, acquiring the duration of adjusting the target temperature value combination, and determining the adjusted target temperature value combination within the duration, and determining the adjusted target temperature value within the duration The combination includes: step S804, after the step of obtaining a plurality of first temperature value combinations, for at least one first temperature value combination in the first temperature value combination, for each sub-period of some or all of the sub-periods, The temperature value of the sub-time period is adjusted based on the preset step length to obtain at least one second temperature value combination, wherein the temperature value of each sub-time period in the at least one second temperature value combination corresponds to the comfort of the sub-time period Zone temperature range; and step S806, after the step of determining a target temperature value combination from a plurality of first temperature value combinations, determine an adjusted target temperature value combination from the first temperature value combination and the second temperature value combination , So that the temperature setting value of each of the plurality of sub-time periods is set according to the adjusted target temperature value combination, and the air-conditioning and cooling system will perform best in the future predetermined time period.

For example, in each of the next 24 hours, obtain a period of time, and determine the target temperature value combination in the period of time in each hour. Moreover, after obtaining the first plurality of first temperature value combinations, at least one second temperature value combination is also obtained, and the second temperature value combination is obtained on the basis of the first temperature value combination. For example, the first temperature value combination includes 24 temperature values corresponding to the next 24 hours. For these 24 temperature values, at least one of the temperature values can be adjusted, for example, adding at least one temperature value to a preset step length, Thereby, a new combination of second temperature values is obtained. It should be understood that one of the 24 temperature values can be added to the preset step length to obtain a new second temperature value combination, or multiple temperature values of the 24 temperature values can be added to the preset step length , Or add all 24 temperature values to the preset step length. In practical applications, the preset step length may be an integer multiple of the temperature adjustment step length of the air conditioning and cooling system. For example, the air conditioner can be 0.1 degrees Celsius as the temperature adjustment step size, and the preset step size can be ±0.1 degrees Celsius, ±0.2 degrees Celsius, or ±N degrees Celsius. Assuming that among the multiple temperature values included in the first temperature value combination, the one-hour temperature value added to the preset step size is 20 degrees, and the preset step size is 1 degrees Celsius, then the second temperature value combination includes multiple In the temperature value, the temperature value corresponding to the hour is 21 degrees Celsius. If the preset step size is -1 degrees Celsius, among the multiple temperature values included in the second temperature value combination, the temperature value corresponding to the hour is 19 degrees Celsius. If the preset step size is 2 degrees Celsius, among the multiple temperature values included in the second temperature value combination, the temperature value corresponding to the hour is 22 degrees Celsius. The 24 temperature values in the second temperature value combination are also within the corresponding hourly comfort zone temperature range. After at least one second temperature value combination is obtained, the temperature value combination that optimizes the operating state of the air-conditioning and cooling system for a predetermined time period in the future is determined from the first temperature value combination and the second temperature value combination. In this way, the calculation result is further optimized within the obtained time period of each hour.

According to the embodiment of the present application, the particle swarm optimization algorithm (PSO) in the heuristic algorithm is used to complete the optimization process of the calculation result. Because the PSO algorithm can calculate multiple potentially optimal temperature value combinations at the same time, it has a faster convergence rate and shorter calculation time than other traditional heuristic algorithms. Each particle in the PSO algorithm represents a possible best combination of temperature values for the next 24 hours, and the fitness of these possible combinations is evaluated by the objective function (24-hour total electricity consumption, total electricity bill). Each particle should be located in the preset search space, that is, within the hourly comfort zone temperature range. The PSO algorithm generates new particles by updating the flight speed (ie search step length such as 0.1 or 1 degrees Celsius) and position (ie different combinations of potential optimal temperature values for 24 hours) of each particle, and updates the local and global maximum values based on the objective function. Excellent solution. In the following two situations, the algorithm stops and outputs the result of the best temperature value combination: 1. The adaptability no longer increases (that is, the total power consumption and total electricity bill no longer decrease or decrease); 2. The algorithm calculation time is optimized, namely The time consumed by the optimization algorithm exceeds the preset value.

The method of optimizing the result of the temperature value combination in the calculation time of the optimization algorithm may also be based on the cooling capacity, power consumption or tariff, which is the same as determining the target temperature value combination from the first temperature value combination.

For example, according to an exemplary embodiment of the present application, the step of determining an adjusted target temperature value combination from the first temperature value combination and the second temperature value combination includes: obtaining the temperature setting value of the air conditioning and cooling system and the temperature setting of the air conditioning and cooling system. The temperature setting value between the cooling capacity provided-the corresponding relationship between the cooling capacity, the cooling capacity between the cooling capacity provided by the air conditioning cooling system and the power consumption required by the air conditioning cooling system to provide cooling capacity-usage Electricity correspondence and the tariff rate of each sub-time period associated with the air conditioning and cooling system; determine the corresponding relationship with each sub-time according to the temperature setting value-the corresponding relationship between the cooling capacity, the corresponding relationship between the cooling capacity-the power consumption and the fee rate The electricity fee corresponding to the temperature setting value of the segment; and the minimum first temperature value combination or the second temperature value combination of the electricity fee corresponding to the plurality of temperature setting values included as the target temperature value combination.

For example, according to an exemplary embodiment of the present application, the step of determining an adjusted target temperature value combination from the first temperature value combination and the second temperature value combination includes: obtaining the temperature setting value of the air conditioning and cooling system and the temperature setting of the air conditioning and cooling system. The temperature setting value between the cooling capacity provided-the corresponding relationship between the cooling capacity and the cooling capacity between the cooling capacity provided by the air conditioning cooling system and the power consumption required by the air conditioning cooling system to provide cooling capacity-usage Electricity corresponding relationship: the minimum first temperature value combination or the second temperature value combination of the power consumption corresponding to the plurality of temperature setting values included as the target temperature value combination.

For the method of optimizing the result of the temperature value combination in the calculation time of the optimization algorithm, the prediction error can also be applied to make the optimization algorithm accurate. The method according to the exemplary embodiment of the present application further includes: acquiring historical data, the historical data including the actual temperature setting value and the actual cooling capacity of each sub-period included in the air-conditioning cooling system in a historical period; obtaining Forecast data, the forecast data includes the cooling capacity predicted according to the corresponding relationship between the temperature setting value and the cooling capacity; the loss function is established based on the historical data and the forecast data to determine the forecast error between the predicted cooling capacity and the actual cooling capacity; The prediction error adjusts the temperature setting value-cooling capacity correspondence; and/or acquiring historical data, which includes the actual cooling capacity and actual use of each sub-period included in the air-conditioning cooling system in a historical period Electricity; Obtain forecast data, including forecasted electricity consumption based on the correspondence between cooling supply and electricity consumption; establish a loss function based on historical data and forecast data, and determine the difference between the predicted electricity consumption and the actual electricity consumption Forecast error; adjust the cooling capacity-power consumption correspondence relationship according to the forecast error.

The method according to the exemplary embodiment of the present application further includes: obtaining prediction errors of multiple historical time periods; if the prediction error increases with time in the multiple historical time periods, adjusting the target temperature within the duration of adjusting the target temperature value combination In the value combination step, at least one of the following is performed: extending the length of the time period, reducing the preset step length, and increasing the number of times that the temperature value of the sub-time period is adjusted based on the preset step length to obtain at least one second temperature value combination.

In this method, the loss function (for example, the function of the mean square error) is set, and based on the historical time period, such as the actual data of each hour and the forecast data of the time period, the variables (power consumption , Cooling capacity, electricity bill, etc.), reflecting the accuracy of the current prediction model. Calculate the value of the loss function for each hour in the history. If the value of the function tends to converge (that is, the value gets smaller and smaller with time), it proves that the current optimization algorithm has good performance and stability, which is suitable for the air conditioning and cooling system of the current scene Prediction of the temperature setting value. Conversely, if the value of the function tends to diverge (that is, it grows with time, the value becomes larger and larger), it proves that the performance of the optimized function becomes worse, the performance is unstable, and it is not suitable for the current scene. At this time, the parameters of the optimization algorithm will be adjusted, for example:

1. Increase the running time of the optimization algorithm. The optimization algorithm is time-constrained. For example, every hour, the calculation needs to be forcibly stopped within 15 minutes, and the best temperature value combination found within 15 minutes is output. In this case, it is recommended to extend the running time of the algorithm, such as extending the running time to 20 minutes, so that the algorithm has more time to find a better temperature value combination. If the later optimization algorithm is stable, the calculation time can be gradually reduced.

2. Increase the number of iterations of the optimization algorithm, which indirectly increases the running time of the algorithm optimization. Through multiple iterations of the optimization algorithm, the second temperature value combination is determined multiple times, and the optimal temperature value combination is determined multiple times.

3. If the optimization algorithm adopts the PS0 algorithm, adjust the size of the inertia factor and the learning factor.

The method according to the exemplary embodiment of the present application further includes in each new sub-period: performing the step of obtaining the comfort zone temperature range of each of the plurality of sub-periods included in the future predetermined period of time, and obtaining the plural sub-periods. A step of combining a first temperature value and a step of determining a target temperature value combination from a plurality of first temperature value combinations; and using the temperature values of a plurality of sub-time periods in the determined target temperature value combination as the next future reservation The temperature values of a plurality of corresponding sub-time periods in a temperature value combination of the plurality of first temperature value combinations to be acquired in the time period. That is to say, in each new sub-time period, for example, every hour, the method of determining the optimal temperature value combination is repeatedly executed, so that the optimal temperature value combination is updated every hour.

The method according to the exemplary embodiment of the present application further includes in each new sub-period: performing the step of obtaining the comfort zone temperature range of each of the plurality of sub-periods included in the future predetermined period of time, and obtaining the plural sub-periods. A step of combining a first temperature value and a step of determining a target temperature value combination from a plurality of first temperature value combinations; performing the step of obtaining the time length for adjusting the target temperature value combination, and determining the adjusted target temperature value combination within the time length ; And the determined adjusted target temperature value combination of the temperature values of the plurality of sub-time periods as the next predetermined time period in the future to obtain a plurality of first temperature value combinations in a temperature value combination corresponding to the plurality of sub-times The temperature value of the segment. That is to say, in each new sub-period, such as every hour, the method of optimizing the optimal temperature value combination is repeatedly executed during the running time of the optimization algorithm, so that the optimal temperature value combination is updated every hour (Adjusted target temperature value combination).

According to the method of the exemplary embodiment of the present application, the step of determining the adjusted target temperature value combination within the duration is periodically repeated within the duration; and the length of each sub-period is equal, and the duration of adjusting the target temperature value combination is less than The length of the sub-period. For example, every hour, within 15 minutes of the optimization algorithm, repeat the optimization of the result of the best temperature value combination. The optimization algorithm time does not exceed one hour, and the optimization algorithm time does not exceed one hour after adjustment, and it is guaranteed to leave enough time for the air conditioning and cooling system.

The following further describes the method for determining the combination of the electricity bill and the minimum temperature value for the next 24 hours according to an exemplary embodiment of the present application.

First, set the initial comfort zone temperature range C _i (using classic comfort zone models, such as PMV models, or user-defined comfort zone temperature ranges) for each hour of the next 24 hours (ie each sub-period), and comfort zone temperature The range should be updated in real time (hourly) to ensure reasonable and accurate calculation of the optimal temperature value combination. Based on the comfort zone temperature range, set the hourly initial temperature value for the next 24 hours as the initial temperature value combination, and set multiple such initial temperature value combinations as the input for the following steps.

The goal of the algorithm of the solution proposed by the exemplary embodiment of the present application is to determine the combination of the electricity bill and the smallest temperature value in the next 24 hours. The purpose of the next few steps is to construct a mapping function (ie, objective function) from the temperature set by the air conditioning and cooling system to the power consumption. First, build the mapping relationship D (“temperature setting value-cooling capacity” model) from the temperature set by the air-conditioning cooling system to the cooling capacity. The method is to use the historical data of the temperature corresponding to the cooling capacity in the database related to the air-conditioning cooling system. Build temperature setting value-cooling capacity model, such as linear/nonlinear regression model, or other machine learning regression model. The amount of the cooling temperature setpoint T _i of the i-th hour of the hour D _i associated with the map shown below:

d=D(T).

Then, the mapping relationship E from cooling capacity to power consumption ("cooling capacity-power consumption" model) is constructed. The cooling capacity comes from the air conditioning and cooling system of the target building, so the overall power consumption of the air conditioning and cooling system has a corresponding mapping relationship with the cooling capacity. When constructing the mapping relationship, the performance curve of each chiller of the building's air conditioning and cooling system represents the relationship between chiller performance (COP) and cooling capacity, and the performance curve is closely related to the model and parameter design of the chiller. These data can be obtained from the factory design instructions of the chiller, or historical data can be collected to predict the relationship between COP and cooling capacity. After obtaining the performance curve, use the image recognition method to construct the relationship function between the chiller performance COP and the cooling capacity:

cop=F(d).

Different types of coolers have different performance curves. Fig. 10 is a performance curve of a refrigerator according to an exemplary embodiment of the present application. As shown in Figure 10, the cooling capacity is different, the performance of the chiller is different. Reuse formula: Electricity consumption E=Cooling capacity/COP to get the relationship between cooling capacity and power consumption:

E=cooling capacity/cop=d/F(d).

Then, combine the "temperature setting value-cooling capacity" model, the "cooling capacity-power consumption" model, and the peak-to-valley electricity rate model P (for example, the peak-to-valley electricity rate as shown in Figure 5). Rate): Electricity fee p=P(E). According to the tariff rate ("electricity consumption E-electricity charge P" model), the final objective function (representing the sum of electricity charges for the next 24 hours) is constructed.

Combining the three mapping function models from the temperature setting value to the cooling capacity, from the cooling capacity to the electricity consumption, and from the electricity consumption to the electricity bill (calculated by the rate), the mapping relationship from the temperature setting value to the electricity bill is obtained. The goal of the exemplary embodiment of the present application is to consider that the cumulative value (sum) of electricity charges per hour in the next 24 hours is the smallest, so the following formula is adopted:

The left part of the formula

Refers to the optimal solution output by the optimization algorithm: the optimal temperature value combination of the optimized temperature setting for each hour of the next 24 hours, the right side is the objective function, argmin refers to the minimum value, and ∑x refers to the sum , I=current time+1, means that the summation algorithm starts from the next hour, current time+25 means the 24th hour calculated from the current hour, and the last P() is "temperature setting value-cooling capacity ”Model, “cooling capacity-power consumption” model and the mapping relationship of the rate from the temperature setting value to the electricity fee, used to find the optimization algorithm for the optimal temperature setting value per hour in the next 24 hours.

The method for determining the combination of the electricity bill for the next 24 hours and the minimum temperature value according to an embodiment of the present application will be further described below in conjunction with the accompanying drawings. Fig. 9 is a flowchart of a method for determining a combination of the next 24 hours electricity bill and the minimum temperature value according to an exemplary embodiment of the present application. As shown in Figure 9, the optimization algorithm used to find the best temperature setting value per hour in the next 24 hours is time-constrained, so as to ensure that the search calculation of the best temperature setting value is completed within the time period specified by the user. It can also set aside time to complete the system deployment.

In order to achieve the above purpose, heuristic algorithms can be used to complete the calculation of the optimal temperature value combination. It should be understood that the modular design is flexible, and users can also deploy other optimization algorithms. The search space of the objective function of the optimization algorithm is very large, and there are more than billions of possible combinations to try to find the best temperature setting value. However, considering that the optimal temperature value combination is updated every hour, the running time of the optimization algorithm should be limited to ensure that there is enough time to change the temperature setting of the air conditioning system. In other words, the calculation time of the optimization algorithm should be less than one hour.

As shown in Figure 9, in step S902, input the previously determined model, for example, input the "temperature setting value-cooling capacity" model, "cooling capacity-electricity consumption" model, and the peak-to-valley electricity rate representing the fluctuation of the rate Rate model, and determine the objective function as described above in step S904. In step S906, the parameters of the optimization algorithm (such as the running time of the optimization algorithm, the number of iterations of the optimization algorithm, etc.) are input, and the optimization algorithm is executed in step S908 (as described above). In step S901, it is determined whether the optimization algorithm has timed out. If the algorithm has not timed out, proceed to step S908 to execute the optimization algorithm. If the algorithm has timed out, in step S912, output the predicted optimal temperature value combination, cooling capacity, power consumption, and electricity bill. , The optimal temperature value combination is used to set the temperature value of the air-conditioning cooling system in the next 24 hours. In step S914, the number of hours of the algorithm is increased by 1, so as to optimize the optimal temperature value combination for the next hour. In step S916, the historical data of the cooling capacity and electricity consumption and the electricity bill are obtained, and then in step S918, the parameters of the optimization algorithm are updated based on the loss function of the historical data and the predicted data, such as adjusting the running time of the optimization algorithm and adjusting the number of iterations of the optimization algorithm 1. Adjust the inertia factor and the size of the learning factor of the PSO algorithm, and use the updated parameters as the parameters of the optimization algorithm in step S906, so as to be used in the optimization algorithm of the next hour (for the historical time period, the current hour). By providing a reasonable initial value of the optimization algorithm, the computational efficiency of the optimization algorithm is improved.

The following points should be noted in the optimization process given in Figure 9:

1. If the time for the preset optimization algorithm has not been reached, the optimization algorithm will be updated cyclically, and iteratively find the best temperature value combination according to different algorithm characteristics.

2. When the calculation time reaches the upper limit of the optimization algorithm, the optimization algorithm will stop and output the optimal result of the currently calculated temperature value combination.

3. After each calculation, the optimization algorithm will update the temperature value combination used for search in the current hour according to the loss function between the model prediction value and the actual value (historical data) in the previous few hours to improve the optimization algorithm Calculation efficiency.

The objective function of the PSO algorithm is to minimize the total electricity bill in the next 24 hours. Each particle in the algorithm represents the solution of a possible temperature value combination for the next 24 hours, and will be evaluated by the objective function to determine its suitability for the optimal temperature value combination. In addition, each particle is located in the search space, and the search space is the hourly comfort zone temperature range in the exemplary embodiment of the present application. In the next step, the PSO algorithm updates the velocity and position of each particle. The formula for updating the velocity of each particle is as follows:

There are three parameters that need to be updated for speed:

1, wv _i (t): component is inertia (inertia component), the particles that maintain the original direction of movement. The parameter w determines the convergence rate, where a higher value of w encourages exploration of the search space.

2,

Known as the cognitive component, it acts as the memory of the particle and returns the particle to the best areas of the search space. The parameter c ₁ limits the size of the step length that the particle travels toward the individual's optimal value, and r ₁ is a unit random value regenerated every time the speed is updated.

3. c ₂ r ₂ [g(t)-x _i (t)]: It is called the social component, which makes the particles move to the best area found by the particle cluster so far. The parameter c ₂ limits the size of the step length that the particles travel towards the global optimal value, and r ₂ is a unit random value regenerated every time the speed is updated.

Then update the position of each particle using the following formula:

_{x i (t + 1) =} x i (t) + v i (t + 1).

After the above procedure, new particles are generated and the individual and global optimal solutions will be updated according to the objective function. This iterative optimization will terminate when the mass of the new particle converges, that is, when the maximum fitness value (electricity and minimum) among the current particles no longer increases, or when the calculation time expires. In both cases, the particle with the largest fitness value represents the best temperature value combination for the next 24 hours. 11 and 12 show the determination result of the optimal temperature value combination according to an exemplary embodiment of the present application.

According to the method of the exemplary embodiment of the present application, obtaining the temperature range of the comfort zone includes: obtaining one or more of the outdoor temperature, humidity, and pedestrian flow associated with the building; and determining the future predetermined time period based on the one or more information. The comfort zone temperature range of each of the multiple sub-periods included.

In addition, according to another embodiment of the present application, users can adjust model parameter values to explore energy saving opportunities. For example, the user can manually adjust the calculation parameters of the comfort zone temperature range (based on the PMV model) to change the search space of the algorithm and obtain different results.

Fig. 13 is a schematic diagram of a temperature range of a comfort zone according to an embodiment of the present application. Fig. 14 is a schematic diagram of a modified comfort zone temperature range according to an embodiment of the present application.

Comparing Figure 14 with Figure 13, the parameter values (wind speed, relative humidity, ambient temperature) at the bottom of the figure have changed, and the temperature range of the comfort zone calculated based on this has also changed accordingly. The calculated optimal temperature setting curve (the most The best temperature value combination) changes.

According to another embodiment of the present application, a device for determining the temperature setting value of an air conditioning and cooling system in a building is also provided. 15 is a schematic diagram of a device for determining a temperature setting value of an air conditioning and cooling system in a building according to an embodiment of the present application. As shown in FIG. 15, the device 1 for determining the temperature setting value of an air conditioning and cooling system in a building according to an embodiment of the present application includes: a comfort zone module 102 configured to execute acquiring a plurality of sub-times included in a predetermined time period in the future The steps of the comfort zone temperature range for each sub-period in the segment, where the comfort zone temperature range is the temperature range that makes the people in the building feel comfortable in the corresponding sub-period, and the temperature range of each comfort zone is less than the air conditioning cooling The temperature adjustment range of the system; the temperature value combination obtaining module 104 is configured to perform the step of obtaining a plurality of first temperature value combinations, wherein each first temperature value combination includes a plurality of temperature values, each of the plurality of temperature values The temperature value corresponds to one of the plurality of sub-time periods, and each temperature value is in the comfort zone temperature range corresponding to the sub-time period; the target temperature value combination determination module 106 is configured to execute the second The step of determining a target temperature value combination in a temperature value combination, so that the temperature setting value of each of the plurality of sub-time periods is set according to the target temperature value combination, and the operating state of the air conditioning and cooling system in the future predetermined time period optimal.

According to an exemplary embodiment of the present application, the step of determining a target temperature value combination from a plurality of first temperature value combinations performed by the target temperature value combination determining module 106 includes: obtaining the temperature setting value of the air conditioning cooling system and the air conditioning cooling system location. The temperature setting value between the cooling capacity provided-the corresponding relationship between the cooling capacity and the cooling capacity between the cooling capacity provided by the air conditioning cooling system and the power consumption required by the air conditioning cooling system to provide cooling capacity-usage Electricity corresponding relationship: the combination of the power consumption corresponding to the plurality of temperature setting values and the smallest first temperature value is used as the target temperature value combination.

According to another exemplary embodiment of the present application, the step of determining a target temperature value combination from a plurality of first temperature value combinations performed by the target temperature value combination determining module 106 includes: obtaining the temperature setting value of the air conditioning cooling system and the air conditioning cooling system. The temperature setting value between the cooling capacity provided by the system-the correspondence between the cooling capacity, the cooling capacity between the cooling capacity provided by the air-conditioning cooling system and the power consumption required by the air-conditioning cooling system to provide the cooling capacity -The corresponding relationship of electricity consumption and the rate of each sub-period associated with the air conditioning and cooling system; according to the temperature setting value-the corresponding relationship of cooling capacity, the corresponding relationship of cooling capacity-power consumption and the rate, determine the corresponding The electricity fee corresponding to the temperature setting value of the sub-time period; and the minimum first temperature value combination of the electricity fee corresponding to the plurality of temperature setting values included is used as the target temperature value combination.

Fig. 16 is a schematic diagram of an apparatus according to an exemplary embodiment of the present application. As shown in FIG. 16, the apparatus 1 according to the exemplary embodiment of the present application further includes a correspondence adjustment module 108, configured to: obtain historical data, the historical data including each sub-component included in the air-conditioning and cooling system in a historical time period. The actual temperature setting value and actual cooling capacity of the time period; obtaining forecast data, including the predicted cooling capacity according to the corresponding relationship between temperature setting value and cooling capacity; establishing a loss function based on historical data and forecast data to determine the forecast The forecast error between the cooling capacity and the actual cooling capacity; adjust the temperature setting value-cooling capacity correspondence relationship according to the forecast error; and/or obtain historical data, which includes the air-conditioning cooling system in a historical time period The actual cooling capacity and actual power consumption for each sub-period of the ”; Obtain forecast data, including the predicted power consumption based on the corresponding relationship between cooling capacity and power consumption; Establish losses based on historical data and forecast data Function to determine the prediction error between the predicted power consumption and the actual power consumption; adjust the cooling capacity-power consumption correspondence relationship according to the prediction error.

The device 1 according to the exemplary embodiment of the present application further includes a target temperature value combination adjustment module 110, which is configured to obtain a time length for adjusting the target temperature value combination, and determine the adjusted target temperature value combination within the time length, and determine the adjusted target temperature value combination within the time length. The target temperature value combination includes: after the step of obtaining a plurality of first temperature value combinations, for at least one first temperature value combination in the first temperature value combination, for each sub-period of some or all of the sub-periods, The temperature value of the sub-time period is adjusted based on the preset step length to obtain at least one second temperature value combination, wherein the temperature value of each sub-time period in the at least one second temperature value combination corresponds to the comfort of the sub-time period Zone temperature range; and after the step of determining a target temperature value combination from a plurality of first temperature value combinations, an adjusted target temperature value combination is determined from the first temperature value combination and the second temperature value combination so that According to the adjusted target temperature value combination, the temperature setting value of each of the plurality of sub-time periods is set, and the air-conditioning and cooling system will perform best in the future predetermined time period.

According to an exemplary embodiment of the present application, the target temperature value combination adjustment module 110 performing the step of determining an adjusted target temperature value combination from the first temperature value combination and the second temperature value combination includes: obtaining the temperature setting value of the air conditioning and cooling system The temperature setting value between the cooling capacity provided by the air-conditioning and cooling system-the corresponding relationship between the cooling capacity, the cooling capacity provided by the air-conditioning cooling system and the power consumption required by the air-conditioning cooling system to provide the cooling capacity The corresponding relationship between cooling capacity and power consumption and the rate of each sub-period associated with the air conditioning and cooling system; according to the temperature setting value-the corresponding relationship between cooling capacity, the corresponding relationship between cooling capacity and power consumption, and the rate , Determine the electricity rate corresponding to the temperature setting value of each sub-time period; and use the minimum first temperature value combination or the second temperature value combination of the included plurality of temperature setting values and the minimum first temperature value combination as the target temperature value combination.

According to another exemplary embodiment of the present application, the target temperature value combination adjustment module 110 performing the step of determining an adjusted target temperature value combination from the first temperature value combination and the second temperature value combination includes: acquiring the air conditioning and cooling system The temperature setting value-cooling capacity correspondence between the temperature setting value and the cooling capacity provided by the air conditioning cooling system, and the cooling capacity provided by the air conditioning cooling system and the electricity required for the cooling capacity provided by the air conditioning cooling system The corresponding relationship between the cooling capacity and the power consumption; the minimum first temperature value combination or the second temperature value combination of the power consumption corresponding to the plurality of temperature setting values included is used as the target temperature value combination.

The device 1 according to the exemplary embodiment of the present application further includes an adjustment and optimization module 112 configured to: obtain prediction errors of multiple historical time periods; if the prediction error increases with time in the multiple historical time periods, adjust the target In the step of adjusting the target temperature value combination within the duration of the temperature value combination, at least one of the following is performed: extending the length of the duration, reducing the preset step size, increasing the temperature value of the sub-time period based on the preset step size to obtain at least The number of combinations of a second temperature value.

The device 1 according to the exemplary embodiment of the present application further includes a target temperature value combination update module 114, which is configured to execute in each new sub-period: acquiring each of the plurality of sub-periods included in the future predetermined period of time The step of the comfort zone temperature range of the segment, the step of obtaining a plurality of first temperature value combinations, and the step of determining a target temperature value combination from the plurality of first temperature value combinations; and combining the plurality of determined target temperature value combinations The temperature values of the sub-time periods are used as the temperature values of the corresponding plurality of sub-time periods in one temperature value combination of the plurality of first temperature value combinations to be acquired in the next predetermined time period in the future.

According to the apparatus 1 of another exemplary embodiment of the present application, the target temperature value combination update module 114 is further configured to execute in each new sub-period: acquiring each of the plurality of sub-periods included in the future predetermined period of time The steps of the comfort zone temperature range of the time period, the steps of obtaining a plurality of first temperature value combinations, and the step of determining a target temperature value combination from the plurality of first temperature value combinations; executing the time length of obtaining and adjusting the target temperature value combination, and The step of determining the adjusted target temperature value combination within the time period; and using the temperature values of the plurality of sub-time periods in the determined adjusted target temperature value combination as the plurality of first temperature value combinations to be acquired in the next predetermined time period in the future The temperature values of the corresponding multiple sub-time periods in a temperature value combination in.

According to the exemplary embodiment of the present application, the step of determining the adjusted target temperature value combination within the time length is periodically repeated within the time length; and the length of each sub-time period is equal, and the time length of adjusting the target temperature value combination is less than the sub-time The length of the segment.

According to an exemplary embodiment of the present application, obtaining the comfort zone temperature range by the comfort zone module includes: obtaining one or more of the outdoor temperature, humidity, and flow of people associated with the building; and determining the predetermined time period in the future according to the one or more information The comfort zone temperature range of each of the multiple sub-periods included.

According to another embodiment of the present application, a system for determining the temperature setting value of an air conditioning and cooling system in a building is provided. Fig. 17 is a schematic diagram of a system for determining a temperature setting value of an air conditioning and cooling system in a building according to an embodiment of the present application.

As shown in Figure 17, the system 5 includes: an air-conditioning and cooling system 3; and a device 1 for determining the temperature setting value of the air-conditioning and cooling system in a building. The device 1 includes: a comfort zone module 102 configured to perform obtaining future reservations The steps of the comfort zone temperature range of each of the plurality of sub-time periods included in the time period, where the comfort zone temperature range is the temperature range that makes the people in the building feel comfortable in the corresponding sub-time period, and each The temperature range of the comfort zone is smaller than the temperature adjustment range of the air conditioning and cooling system; the temperature value combination obtaining module 104 is configured to perform the step of obtaining a plurality of first temperature value combinations, wherein each first temperature value combination includes a plurality of temperature values, Each temperature value in the plurality of temperature values corresponds to one of the plurality of sub-time periods, and each temperature value is within the comfort zone temperature range corresponding to the sub-time period; the target temperature value combination determination module 106, Is configured to perform the step of determining a target temperature value combination from a plurality of first temperature value combinations, so that the temperature setting value of each of the plurality of sub-periods is set according to the target temperature value combination, and the air conditioning and cooling system The operating state is the best in the future scheduled time period.

The system 5 shown in FIG. 17 includes two main parts: an air conditioning and cooling system 3 (for example, HVAC) on the cooling side and a data-driven device 1 for determining the temperature setting of the air conditioning and cooling system. The entire system is designed to be flexible and modular, and can be applied to different air-conditioning and cooling systems. For example, the facilities that provide cooling capacity on the cooling side can be freely added or removed from the air-conditioning and cooling system 3. In this facility Equipped with related sensors 302 for collecting data. In addition, any control strategy related to the supplier can be applied, such as chiller control and optimization of operating parameters. The air-conditioning and cooling system 3 further includes a control feedback module 304 for feeding back data collected from the air-conditioning and cooling system 3 to the device 1, such as feeding back historical data, and performing data interaction with the device 1.

According to an exemplary embodiment of the present application, the target temperature value combination determining module 106 of the device 1 executes the step of determining a target temperature value combination from a plurality of first temperature value combinations including: obtaining the temperature setting value of the air conditioning and cooling system and the air conditioning supply The temperature setting value between the cooling capacity provided by the cooling system-the corresponding relationship between the cooling capacity and the cooling between the cooling capacity provided by the air-conditioning cooling system and the power consumption required by the air-conditioning cooling system to provide the cooling capacity The corresponding relationship between the amount and the power consumption; the combination of the power consumption corresponding to the plurality of temperature setting values and the smallest first temperature value is used as the target temperature value combination.

According to another exemplary embodiment of the present application, the target temperature value combination determining module 106 of the device 1 performs the step of determining a target temperature value combination from a plurality of first temperature value combinations including: obtaining the temperature setting value of the air conditioning and cooling system and The temperature setting value between the cooling capacity provided by the air-conditioning and cooling system-the corresponding relationship between the cooling capacity, the cooling capacity provided by the air-conditioning cooling system and the power consumption required by the air-conditioning cooling system to provide the cooling capacity The corresponding relationship between cooling capacity and power consumption and the rate of each sub-time period associated with the air conditioning and cooling system; according to the temperature setting value-the corresponding relationship between cooling capacity, the corresponding relationship between cooling capacity and power consumption, and the rate, Determine the electricity fee corresponding to the temperature setting value of each sub-time period; and use the minimum first temperature value combination of the electricity fee corresponding to the plurality of temperature setting values included as the target temperature value combination.

In addition, the system 5 according to the embodiment of the present application includes all the modules of the device 1 shown above. Fig. 18 is a schematic diagram of a system according to an exemplary embodiment of the present application.

According to an exemplary embodiment of the present application, the device 1 further includes a correspondence adjustment module 108 configured to obtain historical data, the historical data including the actual temperature of each sub-time period included in the air-conditioning and cooling system in a historical time period Set value and actual cooling capacity; obtain forecast data, including forecasted cooling capacity according to the corresponding relationship between temperature setting value and cooling capacity; establish a loss function based on historical data and forecast data to determine the predicted cooling capacity and actual cooling capacity The prediction error between the cooling capacity; adjust the temperature setting value-cooling capacity correspondence relationship according to the prediction error; and/or obtain historical data, which includes each sub-time period included in the air-conditioning cooling system in a historical time period The actual cooling capacity and the actual power consumption; the forecast data includes the predicted power consumption according to the corresponding relationship between the cooling capacity and the power consumption; the loss function is established based on the historical data and the predicted data to determine the predicted consumption The prediction error between the electricity and the actual electricity consumption; the corresponding relationship between the cooling capacity and the electricity consumption is adjusted according to the forecast error.

According to an exemplary embodiment of the present application, the device 1 further includes a target temperature value combination adjustment module 110, which is configured to obtain a time length for adjusting the target temperature value combination, and determine the adjusted target temperature value combination within the time length, and determine the adjusted target temperature value combination within the time length. The target temperature value combination includes: after the step of obtaining a plurality of first temperature value combinations, for at least one first temperature value combination in the first temperature value combination, for each sub-period of some or all of the sub-periods, The temperature value of the sub-time period is adjusted based on the preset step length to obtain at least one second temperature value combination, wherein the temperature value of each sub-time period in the at least one second temperature value combination corresponds to the comfort of the sub-time period Zone temperature range; and after the step of determining a target temperature value combination from a plurality of first temperature value combinations, an adjusted target temperature value combination is determined from the first temperature value combination and the second temperature value combination so that According to the adjusted target temperature value combination, the temperature setting value of each of the plurality of sub-time periods is set, and the air-conditioning and cooling system will perform best in the future predetermined time period.

According to an exemplary embodiment of the present application, the target temperature value combination adjustment module 110 of the device 1 performs the step of determining an adjusted target temperature value combination from the first temperature value combination and the second temperature value combination, including: acquiring the air conditioning and cooling system The temperature setting value between the temperature setting value and the cooling capacity provided by the air conditioning cooling system-the corresponding relationship between the cooling capacity, the cooling capacity provided by the air conditioning cooling system and the electricity required for the cooling capacity provided by the air conditioning cooling system The corresponding relationship between the cooling capacity-power consumption and the rate of each sub-period associated with the air-conditioning cooling system; according to the temperature setting value-cooling capacity corresponding relationship, cooling capacity-power consumption corresponding relationship And the rate, determine the electricity rate corresponding to the temperature setting value of each sub-time period; and use the minimum first temperature value combination or the second temperature value combination of the included plural temperature setting values and the smallest first temperature value combination as the target temperature value combination.

According to another exemplary embodiment of the present application, the target temperature value combination adjustment module 110 of the device 1 performs the step of determining an adjusted target temperature value combination from the first temperature value combination and the second temperature value combination, including: obtaining an air conditioner supply The temperature setting value between the temperature setting value of the cooling system and the cooling capacity provided by the air conditioning cooling system-the corresponding relationship between the cooling capacity and the cooling capacity provided by the air conditioning cooling system and the cooling capacity required by the air conditioning cooling system The corresponding relationship between the cooling capacity and the power consumption; the minimum first temperature value combination or the second temperature value combination of the power consumption corresponding to the plurality of temperature setting values included as the target temperature value combination.

According to an exemplary embodiment of the present application, the device 1 further includes an adjustment and optimization module 112 configured to: obtain prediction errors of multiple historical time periods; if the prediction error increases with time in the multiple historical time periods, adjust the target In the step of adjusting the target temperature value combination within the duration of the temperature value combination, at least one of the following is performed: extending the length of the duration, reducing the preset step size, increasing the temperature value of the sub-time period based on the preset step size to obtain at least The number of combinations of a second temperature value.

According to an exemplary embodiment of the present application, the target temperature value combination update module 114 of the device 1 is configured to perform the acquisition of each of the plurality of sub-time periods included in the future predetermined time period in each new sub-time period. The steps of the temperature range of the comfort zone, the steps of obtaining a plurality of first temperature value combinations, and the step of determining a target temperature value combination from the plurality of first temperature value combinations; and the plurality of sub-times in the determined target temperature value combination The temperature value of the segment is used as the temperature value of the corresponding plurality of sub-periods in one of the plurality of first temperature value combinations to be acquired in the next predetermined time period in the future.

According to another exemplary embodiment of the present application, the target temperature value combination update module 114 of the device 1 is further configured to execute in each new sub-period: acquiring each of the plurality of sub-periods included in the future predetermined period of time The steps of the comfort zone temperature range of the time period, the steps of obtaining a plurality of first temperature value combinations, and the step of determining a target temperature value combination from the plurality of first temperature value combinations; executing the time length of obtaining and adjusting the target temperature value combination, and The step of determining the adjusted target temperature value combination within the time period; and using the temperature values of the plurality of sub-time periods in the determined adjusted target temperature value combination as the plurality of first temperature value combinations to be acquired in the next predetermined time period in the future The temperature values of the corresponding multiple sub-time periods in a temperature value combination in.

The system according to the embodiment of the present application can operate according to the following steps.

Step 1: Correctly select and set up cooling side facilities and sensors 302.

Step 2.1: The cooling side continuously sends sensor data to the device 1 that determines the temperature setting value of the air-conditioning cooling system 3 to reflect the operating status of the system. The device 1 optimizes the parameters of the algorithm based on historical update data.

Step 2.2: Device 1 establishes an optimization model (objective function) to provide input and constraints for the optimization algorithm.

Step 3: The device 1 outputs the optimal solution (optimum temperature value combination) predicted within the calculation time to the air conditioning and cooling system 3 on the cooling side once an hour according to the optimization algorithm. The optimal temperature value combination includes the hourly temperature settings in the building for the next 24 hours, as well as cooling capacity, electricity consumption and electricity bills.

Step 4: Device 1 adjusts the temperature setting value of the air-conditioning cooling system 3 to the hourly temperature setting value of the optimal temperature value combination, and sends the predicted cooling capacity, power consumption and electricity bill to the control of the air-conditioning cooling system 3 Feedback module 304.

Step 5: The cooling side of the air-conditioning cooling system 3 checks the temperature setting suggestions in the control feedback module 304, adjusts and sends real-time historical data back to the control feedback module 304, and the control feedback module 304 sends the historical data to the device 1. .

Step 6: The device 1 updates the optimization algorithm parameters based on the error analysis of the prediction data and the historical data. Finally, propose new hourly temperature settings.

The system according to the present application provides users with a predetermined time period in the future, for example, the combination of the optimal temperature setting value per hour in the next 24 hours (the optimal temperature value combination, that is, a total of 24 temperatures, and the optimal refers to the set of temperature values. Under the setting, the total electricity bill in the next 24 hours is the smallest). It also provides forecasts of hourly cooling capacity, electricity consumption and hourly electricity bills corresponding to the group of optimal temperature values. In addition, the data-driven model of the system according to the present application will correct the error of the current hourly predicted value based on the deviation of the past hourly predicted value, thereby improving the accuracy of the predicted optimal temperature value combination.

According to another embodiment of the present application, there is provided a storage medium with a program stored on the storage medium, and when the program is executed by a computer including the storage medium, the computer executes the method according to the foregoing embodiment.

According to another embodiment of the present application, a processor is provided, the processor is configured to run a program stored on a memory, wherein the processor executes the method according to the foregoing embodiment when the program is running.

The solution proposed in this application is flexible and modular in design, and can be adapted to buildings with different air conditioning and cooling systems. The optimization module and algorithm of this application are flexible: various optimization algorithms can be used as the core algorithm. The error analysis of this application helps to ensure the reliability of the prediction results and improve the calculation efficiency. The solution of the present application allows to optimize the cooling demand and the cooling side of the target building at the same time. This application considers sub-models such as the temperature range of the comfort zone, which can ensure that the air-conditioning and cooling system consumes the least electricity or the total electricity bill in the next 24 hours, while ensuring the comfort of indoor personnel. The data-driven cooling optimization process of this application is based on dynamically updated real-time data (for example, cooling measurement data, such as operating parameters of chillers and pumps, peak and valley electricity rates). Therefore, the technical solution of the present application can reflect the real situation of the system when in use, and make optimization at any time.

In the above-mentioned embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in an embodiment, reference may be made to related descriptions of other embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are only illustrative, for example, the division of the units or modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or modules or components. Can be combined or integrated into another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, modules or units, and may be in electrical or other forms.

The units or modules described as separate parts may or may not be physically separate, and the parts displayed as units or modules may or may not be physical units or modules, that is, they may be located in one place, or they may be distributed to Multiple network units or modules. Some or all of the units or modules may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

In addition, each functional unit or module in each embodiment of the present application can be integrated into one processing unit or module, or each unit or module can exist alone physically, or two or more units or modules can be integrated into one. Unit or module. The above-mentioned integrated units or modules can be implemented in the form of hardware or software functional units or modules.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solution of this application essentially or the part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , Including several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage media include: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program code .

The above are only the preferred embodiments of this application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of this application, several improvements and modifications can be made, and these improvements and modifications are also Should be regarded as the scope of protection of this application.

Claims

The method for determining the temperature setting value of the air conditioning and cooling system in a building is characterized in that it includes:

The step of obtaining the temperature range of the comfort zone of each of the plurality of sub-time periods included in the predetermined time period in the future, wherein the temperature range of the comfort zone is such that the people in the building are in the corresponding sub-time period A comfortable temperature range, and the temperature range of each comfort zone is smaller than the temperature adjustment range of the air conditioning and cooling system;

The step of obtaining a plurality of first temperature value combinations, wherein each of the first temperature value combinations includes a plurality of temperature values, and each temperature value of the plurality of temperature values corresponds to one of the plurality of sub-time periods A sub-period, and each temperature value is within the comfort zone temperature range corresponding to the sub-period; and

The step of determining a target temperature value combination from a plurality of the first temperature value combinations, so that the temperature setting value of each of the plurality of sub-time periods is set according to the target temperature value combination, the The air-conditioning and cooling system has the best operating state during the predetermined time period in the future.
The method according to claim 1, wherein the step of determining a target temperature value combination from a plurality of said first temperature value combinations comprises:

Obtain the temperature setting value-cooling capacity correspondence between the temperature setting value of the air conditioning and cooling system and the cooling capacity provided by the air conditioning and cooling system, and the cooling capacity provided by the air conditioning and cooling system and the total cooling capacity The air-conditioning cooling system provides the cooling capacity-power consumption correspondence between the power consumption required for the cooling capacity;

The minimum first temperature value combination of the power consumption corresponding to the plurality of temperature setting values included is used as the target temperature value combination.
The method according to claim 1, wherein the step of determining a target temperature value combination from a plurality of said first temperature value combinations comprises:

Obtain the temperature setting value-cooling capacity correspondence between the temperature setting value of the air conditioning and cooling system and the cooling capacity provided by the air conditioning and cooling system, the cooling capacity provided by the air conditioning and cooling system and the The air-conditioning and cooling system provides the cooling capacity-power consumption correspondence between the power consumption required for the cooling capacity and the rate of each of the sub-time periods associated with the air-conditioning cooling system;

Determine the electricity fee corresponding to the temperature setting value of each of the sub-time periods according to the temperature setting value-cooling capacity correspondence, the cooling capacity-power consumption correspondence, and the fee rate; and

The combination of the minimum first temperature value and the sum of the electricity charges corresponding to the plurality of temperature setting values included is used as the target temperature value combination.
The method according to claim 2 or 3, further comprising:

Obtain historical data, the historical data including the actual temperature setting value and the actual cooling capacity of each sub-period included in the air-conditioning and cooling system in a historical period; obtain predicted data, the predicted data including The cooling capacity predicted according to the corresponding relationship between the temperature setting value and the cooling capacity; a loss function is established according to the historical data and the predicted data, and the prediction error between the predicted cooling capacity and the actual cooling capacity is determined; The prediction error adjusts the corresponding relationship between the temperature setting value and the cooling capacity; and/or

Obtain historical data, where the historical data includes the actual cooling capacity and actual power consumption of each sub-period included in the air-conditioning and cooling system in a historical period; obtain predicted data, the predicted data including The electricity consumption predicted according to the correspondence between the cooling capacity and the electricity consumption; establish a loss function according to the historical data and the predicted data, and determine the prediction error between the predicted electricity consumption and the actual electricity consumption; The prediction error adjusts the corresponding relationship between the cooling capacity and the power consumption.
The method according to claim 1, further comprising obtaining a time length for adjusting the target temperature value combination, and determining the adjusted target temperature value combination within the time length, and determining the adjusted target temperature value combination within the time length include:

After the step of obtaining a plurality of the first temperature value combinations, for at least one first temperature value combination in the first temperature value combinations, for each of some or all of the sub-periods, based on the preset Set the step size to adjust the temperature value of the sub-time period to obtain at least one second temperature value combination, wherein the temperature value of each sub-time period in the at least one second temperature value combination is in all the corresponding sub-time periods. Within the temperature range of the comfort zone; and

After the step of determining a target temperature value combination from a plurality of the first temperature value combinations, an adjusted target temperature value combination is determined from the first temperature value combination and the second temperature value combination, so that The temperature setting value of each of the plurality of sub-time periods is set according to the adjusted target temperature value combination, and the air-conditioning and cooling system has the best operating state in the future predetermined time period.
The method according to claim 1, further comprising in each new sub-time period:

Performing the step of obtaining the comfort zone temperature range of each of the plurality of sub-time periods included in the predetermined time period in the future, the step of obtaining a plurality of first temperature value combinations, and determining from the plurality of first temperature value combinations A combination of target temperature values; and

Use the temperature values of the multiple sub-periods in the determined target temperature value combination as the temperature values of the corresponding multiple sub-periods in one of the multiple first temperature value combinations to be acquired in the next predetermined time period in the future .
The method according to claim 1, wherein obtaining the temperature range of the comfort zone comprises:

Acquiring one or more of outdoor temperature, humidity, and human flow associated with the building;

The comfort zone temperature range of each of the plurality of sub-time periods included in the future predetermined time period is determined according to the one or more pieces of information.
The device for determining the temperature setting value of the air conditioning and cooling system in a building is characterized in that it includes:

A comfort zone module (102) is configured to perform the step of obtaining the comfort zone temperature range of each of the plurality of sub-time periods included in the future predetermined time period, wherein the comfort zone temperature range is the The temperature range within which the persons in the building feel comfortable in the corresponding sub-time period, and the temperature range of each comfort zone is smaller than the temperature adjustment range of the air conditioning and cooling system;

A temperature value combination obtaining module (104) is configured to perform the step of obtaining a plurality of first temperature value combinations, wherein each of the first temperature value combinations includes a plurality of temperature values, and each of the plurality of temperature values Each temperature value corresponds to a sub-time period of the plurality of sub-time periods, and each temperature value is in the comfort zone temperature range corresponding to the sub-time period;

A target temperature value combination determining module (106) is configured to perform the step of determining a target temperature value combination from a plurality of the first temperature value combinations, so that the plurality of sub-times are set according to the target temperature value combination For the temperature setting value of each sub-time period in the period, the air-conditioning and cooling system has the best operating state in the future predetermined time period.
The device according to claim 8, wherein the step of determining a target temperature value combination from a plurality of the first temperature value combinations by the target temperature value combination determining module (106) comprises:

Obtain the temperature setting value-cooling capacity correspondence between the temperature setting value of the air conditioning and cooling system and the cooling capacity provided by the air conditioning and cooling system, and the cooling capacity provided by the air conditioning and cooling system and the total cooling capacity The air-conditioning cooling system provides the cooling capacity-power consumption correspondence between the power consumption required for the cooling capacity;

The minimum first temperature value combination of the power consumption corresponding to the plurality of temperature setting values included is used as the target temperature value combination.
The device according to claim 8, wherein the step of determining a target temperature value combination from a plurality of the first temperature value combinations by the target temperature value combination determining module (106) comprises:

Obtain the temperature setting value-cooling capacity correspondence between the temperature setting value of the air conditioning and cooling system and the cooling capacity provided by the air conditioning and cooling system, the cooling capacity provided by the air conditioning and cooling system and the The air-conditioning and cooling system provides the cooling capacity-power consumption correspondence between the power consumption required for the cooling capacity and the rate of each of the sub-time periods associated with the air-conditioning cooling system;

Determine the electricity fee corresponding to the temperature setting value of each of the sub-time periods according to the temperature setting value-cooling capacity correspondence, the cooling capacity-power consumption correspondence, and the fee rate; and

The combination of the minimum first temperature value and the sum of the electricity costs corresponding to the plurality of temperature setting values included is used as the target temperature value combination.
A system for determining the temperature setting value of an air conditioning and cooling system in a building is characterized in that the system includes:

The air-conditioning and cooling system (3); and

A device (1) for determining the temperature setting value of an air conditioning and cooling system in a building, the device (1) includes:

A comfort zone module (102) is configured to perform the step of obtaining the comfort zone temperature range of each of the plurality of sub-time periods included in the future predetermined time period, wherein the comfort zone temperature range is the The temperature range within which the persons in the building feel comfortable in the corresponding sub-time period, and the temperature range of each comfort zone is smaller than the temperature adjustment range of the air conditioning and cooling system;

A temperature value combination obtaining module (104) is configured to perform the step of obtaining a plurality of first temperature value combinations, wherein each of the first temperature value combinations includes a plurality of temperature values, and each of the plurality of temperature values Each temperature value corresponds to a sub-time period of the plurality of sub-time periods, and each temperature value is in the comfort zone temperature range corresponding to the sub-time period;

A target temperature value combination determining module (106) is configured to perform the step of determining a target temperature value combination from a plurality of the first temperature value combinations, so that the plurality of sub-times are set according to the target temperature value combination For the temperature setting value of each sub-time period in the period, the air-conditioning and cooling system has the best operating state in the future predetermined time period.
A storage medium, characterized in that a program is stored on the storage medium, and when the program is executed by a computer including the storage medium, the computer executes the method according to any one of claims 1 to 7 method.
A processor, characterized in that the processor is used to run a program stored on a memory, wherein the processor executes the method according to any one of claims 1 to 7 when the program is running.