User side temperature control load identification and quantification method, system and equipment based on power load data driving and medium

Abstract

The invention discloses a user side temperature control load identification quantification method, system and device based on power load data driving and a medium, and the method comprises the steps: obtaining regional power load data, temperature data and user power load data, and carrying out the preprocessing; carrying out temperature sensitivity analysis on the power load, and determining a high-temperature interval and a suitable-temperature interval; calculating the load fluctuation ratio between the high temperature and the appropriate temperature of each user; dividing the user power load data according to user electricity price types; grouping the power load data of each user high-temperature interval in each electricity price type; calculating identification boundary conditions of users of different electricity price types; air conditioner users are identified; and the temperature control load resources of the air conditioner users are quantified. According to the method, the load fluctuation rates of different users between the high temperature and the proper temperature are compared, and the user load capacity caused by the temperature is excavated; and based on power load data driving, data of temperature control equipment does not need to be acquired, and better popularization and application are facilitated.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method, system, device and medium for identifying and quantifying user-side temperature control loads driven by electric power load data. Background Art

[0002] In recent years, the "dual peaks" of winter and summer have become more pronounced, creating significant pressure to reduce peak loads and fill valleys, and resulting in low utilization rates of grid investment and construction. To alleviate this pressure, the power sector is gradually promoting the construction of new power systems and optimizing the load dispatch of various flexible resources.

[0003] Temperature-controlled loads, caused by the use of temperature-controlled devices such as air conditioners and heaters, account for a significant portion of the tertiary industry and residential loads. Therefore, for regions with high tertiary industry and residential electricity consumption, the extensive use of temperature-controlled devices in winter and summer often leads to a surge in regional loads, exacerbating the imbalance between electricity supply and demand. Furthermore, temperature-controlled loads often serve as auxiliary loads for production and daily life, participating in industrial production or commercial activities. They are user-side load resources with high flexibility, low regulation costs, and high regulation security. Therefore, accurately identifying and effectively regulating temperature-controlled loads in a region can significantly alleviate power supply pressure. Existing temperature-controlled load identification technologies can be mainly divided into two categories: data-driven and mechanism-driven. Data-driven approaches analyze power load data to obtain relevant information, but existing analysis results focus primarily on the total temperature-controlled load in a region and cannot accurately identify individual temperature-controlled loads. Mechanism-driven approaches focus on individual temperature-controlled devices and develop regulation and aggregation models based on relevant parameters such as the device's power information and accurate indoor and outdoor temperature information. While accurate, these approaches require a large number of device parameters and constantly changing temperature parameters, making them difficult to scale and implement. Summary of the Invention

[0004] In response to the above-mentioned problems in the existing technology, the present invention provides a user-side temperature control load identification and quantification method, system, equipment and medium driven by power load data, which solves two technical problems. The first is to solve the problem of identifying the temperature control load of a single user through power load data and temperature data; the second is to quantify individual temperature control load resources through power load data and temperature data when the temperature control device parameters cannot be obtained.

[0005] In a first aspect, a method for identifying and quantifying user-side temperature control loads based on power load data is provided, comprising the following steps:

[0006] S1: Obtain regional power load data, temperature data, and user power load data, and perform preprocessing;

[0007] S2: Conduct temperature sensitivity analysis of power load based on regional power load and temperature data to determine high temperature range and suitable temperature range;

[0008] S3: Calculate the load fluctuation rate between high temperature and suitable temperature of each user based on the user's power load data;

[0009] S4: Divide the user power load data by electricity price type, which includes large industrial electricity price, general industrial and commercial electricity price, and residential electricity price;

[0010] S5: Data grouping: For each user in each electricity price type, extract the power load data of users in the high temperature range. In chronological order, the power load of every five consecutive working days is divided into a high temperature load group;

[0011] S6: Utilizing the strong correlation between temperature control load and temperature, the identification boundary value of temperature control load for each electricity price type user is determined by maximizing the optimal solution of the correlation coefficient between temperature control load and temperature series;

[0012] S7: Identify air-conditioning users. If the user's load fluctuation rate is greater than the temperature control load identification boundary value of the corresponding electricity price type, the user is an air-conditioning user.

[0013] S8: Quantify the temperature control load resources of air-conditioning users and quantify the user temperature control load resources through the benchmark comparison method.

[0014] Furthermore, the preprocessing process includes removing abnormal values and filling missing values from the user's power load data.

[0015] Furthermore, step S2 specifically includes: drawing a scatter plot of power load and temperature in the area to be analyzed, and determining the temperature range between high temperature and suitable temperature according to the trend of power load changing with temperature.

[0016] Furthermore, in step S3, the load fluctuation rate between the user's high temperature and the appropriate temperature is calculated by the following formula:

[0017]

[0018] Where p n represents the load fluctuation rate of user n, F n represents the average load value of user n in the high temperature range, f n Indicates the average load value in user n suitable temperature range.

[0019] Furthermore, step S6 specifically includes:

[0020] Set the value range and initial value of the temperature control load identification boundary value q, and determine the iterative step size of q;

[0021] For each electricity price type user, the correlation coefficient between the temperature control load and temperature of all users whose load fluctuation rate is greater than q under this electricity price type is iteratively calculated. The q with the largest correlation coefficient among all iterative results is taken as the identification boundary value of the temperature control load under this electricity price type.

[0022] Furthermore, the correlation coefficient is calculated by the following formula:

[0023]

[0024] Where, ρ X,Y Indicates the correlation coefficient between the user's temperature control load and temperature, They represent the average temperature control load value and average temperature of user i in the jth high temperature load group, They represent the average temperature control load value and average temperature of all users in the jth high temperature load group respectively; N represents the number of all users; M represents the number of high temperature load groups.

[0025] Furthermore, in step S8, the temperature control load resources of the air-conditioning user are quantified by the following formula:

[0026] b n =r n -c n

[0027] Where r n is the average load of air-conditioning user n during the high temperature noon peak period, c n is the average load of air-conditioning user n at the peak of suitable temperature in the afternoon, b n is the load difference of air-conditioning user n caused by temperature.

[0028] Secondly, a user-side temperature control load identification and quantification system driven by power load data is provided, including:

[0029] Data preprocessing module, used to obtain regional power load data, temperature data and user power load data, and perform preprocessing;

[0030] The temperature analysis module is used to conduct temperature sensitivity analysis of power load based on regional power load data and temperature data, and determine the high temperature range and the appropriate temperature range;

[0031] The load fluctuation rate calculation module is used to calculate the load fluctuation rate between high temperature and suitable temperature of each user based on the user's power load data;

[0032] The electricity price type classification module is used to classify user power load data according to electricity price types, including large industrial electricity prices, general industrial and commercial electricity prices, and residential electricity prices;

[0033] A data grouping module is used to extract the power load data of users in the high temperature range for each user in each electricity price type, and divide the power load of every five consecutive working days into a high temperature load group in chronological order;

[0034] The boundary value calculation module is used to utilize the strong correlation between temperature control load and temperature and determine the temperature control load identification boundary value for each electricity price type user through the optimal solution that maximizes the correlation coefficient between temperature control load and temperature series;

[0035] Air conditioning user identification module, used to identify air conditioning users. If the user's load fluctuation rate is greater than the temperature control load identification boundary value of the corresponding electricity price type, it is an air conditioning user;

[0036] The temperature control load resource quantification module is used to quantify the temperature control load resources of air-conditioning users and quantify the user's temperature control load resources through the benchmark comparison method.

[0037] According to a third aspect, an electronic device is provided, including:

[0038] Memory on which computer programs or instructions are stored;

[0039] The processor is used to load and execute the computer program or instructions to implement the user-side temperature control load identification and quantification method driven by power load data as described above.

[0040] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the user-side temperature control load identification and quantification method driven by power load data as described above is implemented.

[0041] The present invention proposes a user-side temperature control load identification and quantification method, system, device, and medium based on power load data. Compared with the existing technology, it has the following advantages: First, the present invention fully considers the electricity usage habits of users with different electricity price types under different temperature conditions, deeply explores user electricity usage characteristics, compares the load fluctuation rates of different users between high and moderate temperatures, and mines the user load most likely caused by temperature control load, which helps to rationally allocate power resources and ensure the safe operation of the power grid. Second, the present invention is based on power load data and does not require obtaining temperature control device data from the user side. This avoids the cost of installing controllers or monitors on temperature control products and the risk of user privacy data leakage, reduces user resistance, and promotes better promotion and application. Third, the present invention's identification and quantification of temperature control load data is more accurate and precise. In the process of identifying and quantifying temperature control load, the present invention not only identifies and quantifies the entire region, but can accurately determine the temperature control load of each user at each electricity price type. This helps the power grid system to accurately dispatch, implement peak shaving and valley filling policies, and reduce power losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a flow chart of a method for identifying and quantifying user-side temperature control loads based on power load data provided by an embodiment of the present invention;

[0044] Figure 2 This is a flow chart of iterative calculation of the boundary value of temperature control load identification provided by an embodiment of the present invention;

[0045] Figure 3 This is a scatter plot of power load and temperature in a certain city from April to October 2022, provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other implementations obtained by those of ordinary skill in the art without inventive effort are within the scope of protection of the present invention.

[0047] The embodiment of the present invention provides a user-side temperature control load identification and quantification method based on power load data. In this embodiment, the power data used is the daily 96-point load data of a high-voltage transformer user in a certain city from March 1, 2022 to March 1, 2023, and the temperature data is the daily 8:00 temperature data from April to October 2022. Specifically, Figure 1 As shown, the method provided in this embodiment includes the following steps:

[0048] S1: Obtain regional power load data, temperature data, and user power load data and perform preprocessing. This preprocessing includes removing outliers and filling missing values in the user power load data. Specifically, the Lagrange interpolation method is used to fill missing values, and the six-standard deviation principle is used to find outliers.

[0049] In this implementation, complete temperature data was acquired, and subsequent preprocessing focused on power load data. Specifically, the number of missing values in user load data was counted, and users with more than 20% missing values were removed. For the remaining users, six times the standard deviation of their data was calculated, and the missing values were used to replace the data in the curve that exceeded six times the standard deviation. Lagrange interpolation was then used to fill in the missing values, resulting in usable load data.

[0050] S2: Conduct temperature sensitivity analysis of power load based on regional power load data and temperature data to determine the high temperature range and suitable temperature range.

[0051] Specifically, step S2 includes: drawing a scatter plot of power load and temperature in the area to be analyzed, and determining a high temperature range and a suitable temperature range according to the trend of power load changing with temperature.

[0052] In this embodiment, a scatter plot of power load and temperature in a city from April to October 2022 (see Appendix Figure 3 ) shows the trend of load changing with temperature, and it is found that the temperature sensitive range of regional load is approximately 25℃-34℃. Therefore, the high temperature range is determined to be 25℃-34℃, and the suitable temperature range is 20℃-24℃.

[0053] S3: Calculate the user's load fluctuation rate between high temperature and suitable temperature based on the user's power load data and temperature sensitivity analysis results. The calculation formula is as follows:

[0054]

[0055] Where p n represents the load fluctuation rate of user n, F n represents the average load value of user n in the high temperature range, f n Indicates the average load value in user n suitable temperature range.

[0056] S4: Divide user power load data by electricity price type, which includes large industrial electricity price, general industrial and commercial electricity price, and residential electricity price.

[0057] In this embodiment, the number of three types of users is as follows: 2,706 users at the general industrial and commercial electricity price, 759 users at the large industrial electricity price, and 967 users at the residential electricity price.

[0058] S5: Data grouping: For each user in each electricity price type, extract the power load data of users with temperatures in the high temperature range, and divide the power load of every five consecutive working days into a high temperature load group in chronological order.

[0059] In this embodiment, 43 days from July 1 to September 30 with a temperature within the high temperature range (25° C.-34° C.) are grouped into 5 consecutive working days, and a total of i groups (i=9).

[0060] S6: Calculate the temperature control load identification boundary values for users of different electricity price types. Leveraging the strong correlation between temperature control load and temperature, determine the temperature control load identification boundary values for each electricity price type through the optimal solution that maximizes the correlation coefficient between the temperature control load and temperature series. Specifically, set the value range and initial value of the temperature control load identification boundary value q, and determine the iterative step size of q. For each electricity price type user, iteratively calculate the correlation coefficient between the temperature control load and temperature for all users with a load fluctuation rate greater than q under that electricity price type. The q with the largest correlation coefficient among all iterative results is taken as the temperature control load identification boundary value for that electricity price type.

[0061] More specifically, the calculation process of the temperature control load identification boundary value for users with different electricity price types is as follows: Figure 2 As shown in the figure, for a certain electricity price type user, the calculation of the corresponding temperature control load identification boundary value includes the following steps:

[0062] Step 0: Set the value range, initial value and iteration step of the temperature control load identification boundary value. In this embodiment, the value range of the temperature control load identification boundary value q is [0.05, 0.5], the initial value of the temperature control load identification boundary value q is 0.05, and the iteration step is 0.01.

[0063] Step 1: Obtain the load fluctuation rate between high temperature and suitable temperature of N users under this electricity price type;

[0064] Step 2: Determine whether the load fluctuation rate of each user is greater than the current temperature control load identification boundary value q. If yes, retain it; otherwise, discard it.

[0065] Step 3: Calculate the temperature control load of each high temperature load group of the retained user. The temperature control load of user m is F mjh -f;F mjh represents the high temperature load of user m on the hth day in the jth high temperature load group; f represents the suitable temperature load, and the suitable temperature load of user m is the average power load of 5 days with the temperature fixed between 20℃ and 24℃;

[0066] Step 4: Calculate the correlation coefficient between the temperature control load and the temperature in this iteration, and record the correlation coefficient and the corresponding temperature control load identification boundary value q; the correlation coefficient calculation formula is:

[0067]

[0068] Where, ρ X,Y Indicates the correlation coefficient between the user's temperature control load and temperature, They represent the average temperature control load value and average temperature of user i in the jth high temperature load group, They represent the average temperature control load value and average temperature of all users in the jth high temperature load group respectively; N represents the number of all users; M represents the number of high temperature load groups.

[0069] Step 5: Determine whether q is greater than or equal to 0.5. If so, end the process. Otherwise, jump back to Step 2 and increase the step size of q once, i.e., set q = q + 0.01.

[0070] Step 6: Output the q value corresponding to the largest correlation coefficient in the record, and use the q value as the temperature control load identification boundary value under this electricity price type.

[0071] The boundary conditions of the three types of electricity price users in this embodiment obtained through iterative calculation are as follows: the boundary condition of general industrial and commercial electricity price users is: q = 36%, and the corresponding correlation coefficient is 0.87; the boundary condition of large industrial electricity price users is: q = 17%, and the corresponding correlation coefficient is 0.901; the boundary condition of residential electricity price users is: q = 48%, and the corresponding correlation coefficient is 0.886.

[0072] S7: Identify air-conditioning users. If the user's load fluctuation rate is greater than the temperature control load identification boundary value of the corresponding electricity price type, the user is an air-conditioning user.

[0073] S8: Quantify the temperature control load resources of the air-conditioning user. Specifically, the temperature control load resources of the user are quantified using the following formula:

[0074] b n =r n -c n

[0075] L=∑b n

[0076] Where L is the total temperature control load resources of all users, r n is the average load of air-conditioning user n during the high temperature noon peak period, c n is the average load of air-conditioning user n at the peak of suitable temperature in the afternoon, b n is the load difference of air-conditioning user n caused by temperature.

[0077] In this embodiment, the obtained temperature control load resource mining results total 1.424 million kilowatts, of which general industrial and commercial electricity price users total 752,000 kilowatts; large industrial electricity price users total 398,000 kilowatts; and residential electricity price users total 274,000 kilowatts.

[0078] The above-mentioned embodiment provides a user-side temperature control load identification and quantification method based on power load data, which has the following beneficial effects: First, the present invention fully considers the electricity usage habits of users with different electricity price types under different temperature conditions, deeply explores the user's electricity usage characteristics, compares the load fluctuation rate of different users between high temperature and suitable temperature, and mines the user load most likely caused by temperature control load, which helps to rationally allocate power resources and ensure the safe operation of the power grid; Second, the present invention is based on power load data and does not need to obtain temperature control device data from the user side, which avoids the cost of installing controllers or monitors on temperature control products and the risk of user privacy data leakage, reduces user resistance, and promotes better promotion and application; Third, the identification and quantification of temperature control load data of the present invention is more accurate and precise. In the process of identifying and quantifying temperature control load, the present invention not only identifies and quantifies the entire region, but can accurately determine the temperature control load of each user at each electricity price type, which helps the power grid system to accurately dispatch, implement peak shaving and valley filling policies, and reduce power losses.

[0079] The embodiment of the present invention further provides a user-side temperature control load identification and quantification system driven by power load data, comprising:

[0080] Data preprocessing module, used to obtain regional power load data, temperature data and user power load data, and perform preprocessing;

[0081] The temperature analysis module is used to conduct temperature sensitivity analysis of power load based on regional power load data and temperature data, and determine the high temperature range and the appropriate temperature range;

[0082] The load fluctuation rate calculation module is used to calculate the load fluctuation rate between high temperature and suitable temperature of each user based on the user's power load data;

[0083] The electricity price type classification module is used to classify user power load data according to electricity price types, including large industrial electricity prices, general industrial and commercial electricity prices, and residential electricity prices;

[0084] The data grouping module extracts the power load data of users with high temperature ranges for each user in each electricity price type. The power load of every five consecutive working days is divided into a high temperature load group in chronological order. The boundary value calculation module utilizes the strong correlation between temperature control load and temperature to determine the temperature control load identification boundary value for each electricity price type user by optimizing the solution that maximizes the correlation coefficient between temperature control load and temperature series.

[0085] Air conditioning user identification module, used to identify air conditioning users. If the user's load fluctuation rate is greater than the temperature control load identification boundary value of the corresponding electricity price type, it is an air conditioning user;

[0086] The temperature control load resource quantification module is used to quantify the temperature control load resources of air-conditioning users and quantify the user's temperature control load resources through the benchmark comparison method.

[0087] It should be understood that the functional unit modules in various embodiments of the present invention can be concentrated in one processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated into one unit module, and can be implemented in the form of hardware or software.

[0088] An embodiment of the present invention further provides an electronic device, including:

[0089] Memory on which computer programs or instructions are stored;

[0090] The processor is used to load and execute the computer program or instructions to implement the user-side temperature control load identification and quantification method driven by power load data as described above.

[0091] An embodiment of the present invention further provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, the user-side temperature control load identification and quantification method driven by power load data as described above is implemented.

[0092] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0093] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0094] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0097] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A user-side temperature control load identification and quantification method based on power load data, characterized in that: The steps include: S1: Obtain regional power load data, temperature data, and user power load data, and perform preprocessing; S2: Conduct temperature sensitivity analysis of power load based on regional power load and temperature data to determine high temperature range and suitable temperature range; S3: Calculate the load fluctuation rate between high temperature and suitable temperature of each user based on the user's power load data; S4: Divide the user power load data by electricity price type, which includes large industrial electricity price, general industrial and commercial electricity price, and residential electricity price; S5: Data grouping: For each user in each electricity price type, extract the power load data of users in the high temperature range. In chronological order, the power load of every five consecutive working days is divided into a high temperature load group; S6: Utilizing the strong correlation between temperature control load and temperature, the identification boundary value of temperature control load for each electricity price type user is determined by maximizing the optimal solution of the correlation coefficient between temperature control load and temperature series; S7: Identify air-conditioning users. If the user's load fluctuation rate is greater than the temperature control load identification boundary value of the corresponding electricity price type, the user is an air-conditioning user. S8: Quantify the temperature control load resources of air-conditioning users and quantify the user temperature control load resources through the benchmark comparison method.

2. The user-side temperature control load identification and quantification method based on power load data drive according to claim 1 is characterized in that: The preprocessing process includes removing abnormal values and filling missing values from the user's power load data.

3. The user-side temperature control load identification and quantification method based on power load data drive according to claim 1 is characterized in that: Step S2 specifically includes: drawing a scatter plot of power load and temperature in the area to be analyzed, and determining the temperature range between high temperature and suitable temperature according to the trend of power load changing with temperature.

4. The user-side temperature control load identification and quantification method based on power load data drive according to claim 1 is characterized in that: In step S3, the load fluctuation rate between the user's high temperature and the appropriate temperature is calculated by the following formula: Where p n represents the load fluctuation rate of user n, F n represents the average load value of user n in the high temperature range, f n Indicates the average load value in user n suitable temperature range.

5. The user-side temperature control load identification and quantification method based on power load data drive according to claim 1 is characterized in that: Step S6 specifically includes: Set the value range and initial value of the temperature control load identification boundary value q, and determine the iterative step size of q; For each electricity price type user, the correlation coefficient between the temperature control load and temperature of all users whose load fluctuation rate is greater than q under this electricity price type is iteratively calculated. The q with the largest correlation coefficient among all iterative results is taken as the identification boundary value of the temperature control load under this electricity price type.

6. The user-side temperature control load identification and quantification method based on power load data drive according to claim 5 is characterized in that: The correlation coefficient is calculated by the following formula: Where, ρ X,Y Indicates the correlation coefficient between the user's temperature control load and temperature, They represent the average temperature control load value and average temperature of user i in the jth high temperature load group, They represent the average temperature control load value and average temperature of all users in the jth high temperature load group respectively; N represents the number of all users; M represents the number of high temperature load groups.

7. The method for identifying and quantifying user-side temperature control loads based on power load data according to claim 1, characterized in that: In step S8, the temperature control load resources of the air-conditioning user are quantified using the following formula: b n =r n -c n Where r n is the average load of air-conditioning user n during the high temperature noon peak period, c n is the average load of air-conditioning user n at the peak of suitable temperature in the afternoon, b n is the load difference of air-conditioning user n caused by temperature.

8. A user-side temperature control load identification and quantification system driven by power load data, characterized in that: include: Data preprocessing module, used to obtain regional power load data, temperature data and user power load data, and perform preprocessing; The temperature analysis module is used to conduct temperature sensitivity analysis of power load based on regional power load data and temperature data, and determine the high temperature range and the appropriate temperature range; The load fluctuation rate calculation module is used to calculate the load fluctuation rate between high temperature and suitable temperature of each user based on the user's power load data; The electricity price type classification module is used to classify user power load data according to electricity price types, including large industrial electricity prices, general industrial and commercial electricity prices, and residential electricity prices; The data grouping module extracts the power load data of users with high temperature ranges for each user in each electricity price type. The power load of every five consecutive working days is divided into a high temperature load group in chronological order. The boundary value calculation module utilizes the strong correlation between temperature control load and temperature to determine the temperature control load identification boundary value for each electricity price type user by optimizing the solution that maximizes the correlation coefficient between temperature control load and temperature series. Air conditioning user identification module, used to identify air conditioning users. If the user's load fluctuation rate is greater than the temperature control load identification boundary value of the corresponding electricity price type, it is an air conditioning user; The temperature control load resource quantification module is used to quantify the temperature control load resources of air-conditioning users and quantify the user's temperature control load resources through the benchmark comparison method.

9. An electronic device, characterized in that: include: Memory on which computer programs or instructions are stored; A processor is used to load and execute the computer program or instructions to implement the user-side temperature control load identification and quantification method driven by power load data as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the method for identifying and quantifying user-side temperature control loads based on power load data as described in any one of claims 1 to 7 is implemented.

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