Method for measuring and calculating sensitivity of daily electricity consumption of each region to air temperature

Abstract

The embodiment of the invention provides a method, device and equipment for measuring and calculating the sensitivity of daily electricity consumption of each region to air temperature and a storage medium, and the method comprises the steps: obtaining daily average air temperature data and daily electricity consumption data of each region; carrying out logarithm solving processing on the daily electricity consumption data; and based on a linear regression analysis method, according to the daily average temperature data and a logarithm solving result, obtaining the growth rate of the daily electricity consumption of each region. Therefore, the sensitivity of electricity utilization of each region and each industry to the air temperature can be accurately reflected, and when a power system faces peak load pressure, an accurate load prediction basis is provided, the configuration of power resources is optimized, and the reliability and the operation efficiency of the system are improved.

Description

Technical Field

[0001] This document relates to the field of power engineering technology, and in particular to a method, device, equipment and storage medium for calculating the sensitivity of daily electricity consumption in each region to temperature. Background Art

[0002] By determining how temperature changes affect daily point-of-use volumes, power companies can more accurately forecast peak electricity demand and optimize the dispatch of generators.

[0003] The prior art mainly uses simple unit heating power, that is, the increment of cooling power is determined when the temperature rises by 1°C.

[0004] However, due to the large differences in loads in different regions, even a small percentage increase in developed areas or industrial electricity consumption may appear as a large absolute increase; in remote areas or residential electricity consumption, the same percentage increase may only correspond to a small absolute increase, making it difficult to accurately measure the relationship between temperature and daily electricity consumption in each region, affecting power dispatch and making it difficult to ensure the stability and reliability of the power system. Summary of the invention

[0005] In view of the above scheme, the present application aims to propose a method, device, system and storage medium for calculating the sensitivity of daily electricity consumption in each region to temperature, so as to solve at least one of the above technical problems.

[0006] In a first aspect, one or more embodiments of this specification provide a method for calculating the sensitivity of daily electricity consumption in each region to temperature, including:

[0007] Obtain daily average temperature data and daily electricity consumption data for each region;

[0008] performing logarithmic processing on the daily power consumption data; and

[0009] Based on the linear regression analysis method, the growth rate of daily electricity consumption in each region is obtained according to the daily average temperature data and the result of logarithm calculation.

[0010] Furthermore, the calculation formula for calculating the logarithm of the daily power consumption data is as follows:

[0011] y=lnx

[0012] Where y represents the result of taking the logarithm of daily electricity consumption data; and

[0013] x represents daily electricity consumption data.

[0014] Furthermore, based on the linear regression analysis method, according to the daily average temperature data and the result of logarithm calculation, the growth rate of daily electricity consumption in each region is obtained, including:

[0015] Performing linear regression processing on the daily average temperature data and the result of calculating the logarithm to determine the correlation coefficient;

[0016] If the correlation coefficient is greater than the threshold, the growth rate is obtained.

[0017] Furthermore, the calculation formula for the growth rate of daily electricity consumption in each area is as follows:

[0018] y=k*t+b

[0019] Among them, y represents the result of finding the logarithm of daily electricity consumption data;

[0020] k represents the linear coefficient, that is, the growth rate of daily electricity consumption in each region for every 1°C rise in regional temperature;

[0021] t represents the daily mean temperature; and

[0022] b is a constant term.

[0023] In a second aspect, an embodiment of the present application provides a device for calculating the sensitivity of daily electricity consumption in each region to temperature, including:

[0024] The acquisition module is used to obtain the daily average temperature data and daily electricity consumption data of each area;

[0025] a processing module, configured to perform logarithmic processing on the daily power consumption data; and

[0026] The calculation module is used to obtain the growth rate of daily electricity consumption in each area based on the daily average temperature data and the result of logarithm calculation based on the linear regression analysis method.

[0027] Furthermore, in the processing module:

[0028] The calculation formula for the logarithm of the daily power consumption data is as follows:

[0029] y=lnx

[0030] Where y represents the result of taking the logarithm of daily electricity consumption data; and

[0031] x represents daily electricity consumption data.

[0032] Furthermore, the calculation module is configured to:

[0033] Performing linear regression processing on the daily average temperature data and the result of calculating the logarithm to determine the correlation coefficient;

[0034] If the correlation coefficient is greater than the threshold, the growth rate is obtained.

[0035] Furthermore, in the calculation module:

[0036] The calculation formula for the growth rate of daily electricity consumption in each area is as follows:

[0037] y=k*t+b

[0038] Among them, y represents the result of finding the logarithm of daily electricity consumption data;

[0039] k represents the linear coefficient, that is, the growth rate of daily electricity consumption in each area for every 1°C rise in regional temperature;

[0040] t represents the daily mean temperature; and

[0041] b is a constant term.

[0042] In a third aspect, an embodiment of the present application provides a computing device, comprising a memory, a processor, and computer instructions stored in the memory and executable on the processor, wherein when the processor executes the computer instructions, the processor performs the steps of the method for calculating the sensitivity of daily electricity consumption in each region to air temperature as described in any one of the items in the first aspect.

[0043] In a fourth aspect, an embodiment of the present application provides a storage medium for storing computer-executable instructions, characterized in that when the computer-executable instructions are executed, the steps of the method for calculating the sensitivity of daily electricity consumption in each region to air temperature as described in any one of the first aspects.

[0044] Compared with the prior art, this application can at least achieve the following technical effects:

[0045] This application logarithmizes the daily electricity consumption data and regresses the logarithmized electricity consumption data with the daily average temperature data to obtain the growth rate of daily electricity consumption in each region. The growth rate can more accurately reflect the sensitivity of electricity consumption in each region and industry to temperature. The sensitivity can provide accurate load forecasting basis when the power system faces peak load pressure, optimize the allocation of power resources, and improve the reliability and operation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1 A flow chart of a method for calculating the sensitivity of daily electricity consumption in each region to air temperature provided in one or more embodiments of this specification;

[0048] Figure 2A schematic diagram of the structure of a device for measuring the sensitivity of daily electricity consumption in each area to air temperature provided in one or more embodiments of this specification. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0050] In summer, due to the influence of high temperature, the demand for electricity increases significantly, especially when using refrigeration equipment such as air conditioners. The sensitivity of temperature to electricity in different regions varies. Therefore, measuring and comparing the sensitivity of daily electricity consumption in different regions to temperature in summer can provide an important reference for power dispatch and load forecasting. In the prior art, historical electricity data and temperature data are usually used for statistical analysis. However, due to the differences in climate characteristics, electricity usage habits and user density in different regions, it is difficult for a single method to fully reflect the sensitivity of electricity consumption in various regions.

[0051] The traditional method of calculating the sensitivity of electricity consumption to temperature is to regress the data of electricity consumption and temperature and calculate the electricity consumption per unit temperature rise, that is, the increase in cooling electricity consumption by how many kilowatt-hours for every 1°C increase in temperature. However, the scale of cooling electricity consumption varies greatly in different regions. If the increase in cooling electricity consumption is compared, it is impossible to accurately reflect the sensitivity characteristics, and it is impossible to compare between regions.

[0052] For example: During the summer of 2024, for every 1°C rise in the average daily temperature in the operating area, the cooling electricity consumption of the three industries and residents will increase by 10 million, 50 million, 200 million, and 500 million kWh, respectively. Among them, the secondary industry has a larger scale of electricity consumption, and even if the proportion of air-conditioning electricity is smaller than that of the primary industry, the increase in air-conditioning electricity is higher than that of the primary industry. But in fact, because the scale of electricity consumption in different industries and regions is different, the increase cannot accurately reflect the sensitivity.

[0053] Embodiment 1

[0054] In response to the above technical problems, this application proposes a method for calculating the sensitivity of daily electricity consumption in each region to temperature, such as Figure 1 As shown, the specific steps are as follows:

[0055] Step S1, obtaining daily average temperature data and daily electricity consumption data of each area.

[0056] In the embodiment of the present application, the real-time temperature data released by the meteorological departments of various places on the official website, for example, the daily average temperature data is obtained from the China Meteorological Administration, and the real-time daily power consumption data released on the official website of the power company or the power grid company of various places is obtained.

[0057] This application selects the most authoritative and reliable data sources to ensure the accuracy of the prediction.

[0058] Step S2, performing logarithm processing on the daily power consumption data.

[0059] In the embodiment of the present application, a logarithm with the natural constant e as the base is selected to perform logarithm processing on the daily power consumption data. The calculation formula for the logarithm of the daily power consumption data is as follows:

[0060] y=lnx

[0061] Where y represents the result of taking the logarithm of daily electricity consumption data; and

[0062] x represents daily electricity consumption data.

[0063] In this application, the daily electricity consumption data may have a large fluctuation range. Through logarithmic transformation, these data can be converted into a form closer to normal distribution, thereby improving the statistical characteristics of the data. Improving data distribution can improve the accuracy and reliability of the algorithm.

[0064] Step S3, based on a linear regression analysis method, according to the daily average temperature data and the result of calculating the logarithm, obtain the growth rate of daily electricity consumption in each area.

[0065] In the embodiment of the present application, the average temperature data is used as the independent variable, and the daily electricity consumption data after logarithmic processing is used as the dependent variable to perform linear regression analysis to determine the correlation coefficient. If the correlation coefficient is greater than the threshold, the growth rate is obtained. The calculation formula for the growth rate of daily electricity consumption in each region is as follows:

[0066] y=k*t+b

[0067] Among them, y represents the result of finding the logarithm of daily electricity consumption data;

[0068] k represents the linear coefficient, that is, the growth rate of daily electricity consumption in each region for every 1°C rise in regional temperature;

[0069] t represents the daily mean temperature; and

[0070] b is a constant term.

[0071] For example, if today's temperature is 28℃, the conclusion obtained after logarithm and linear regression calculation is that for every 1℃ rise in temperature, the growth rate of cooling power is 5%; for every 1℃ drop in temperature, the negative growth rate of cooling power is 5%, where cooling power refers to the additional power consumption caused by the increased use of cooling equipment such as air conditioners and fans due to the increase in temperature during a specific time period. If tomorrow's temperature is 25℃, the percentage of power reduction can be obtained based on the drop in temperature and growth rate, that is, when the temperature drops by 3℃ and the cooling power drops by 5%, the negative growth rate of power on the next day is predicted to be 15%.

[0072] In this application, according to econometric theory, the logarithm of the cooling electricity consumption is taken, and the logarithm of the cooling electricity consumption is regressed with the temperature. The linear coefficient represents the growth rate of the cooling electricity consumption for every 1°C increase in temperature. This makes the reflected sensitivity more accurate and can better achieve horizontal comparison of various regions. This method can also be extrapolated to the industry dimension, which makes the measurement of the sensitivity of electricity consumption in various regions and industries to temperature more accurate. At the same time, the growth rate can also optimize the optimal allocation of power resources and improve the reliability and operation efficiency of the power system.

[0073] Embodiment 2

[0074] The present invention proposes a method for calculating the sensitivity of daily electricity consumption in each region to the temperature. By acquiring temperature data and daily electricity data by region, combined with data smoothing, regression analysis and parameter optimization technology, an electricity sensitivity model suitable for each region is constructed. The model is used to quantify the relationship between temperature change and electricity response, and the sensitivity coefficient of electricity to temperature change is obtained, and the difference in sensitivity between regions is compared. This method uses the unit temperature rise growth rate to characterize the sensitivity, which is helpful to realize the sensitivity comparison between different industries and regions. It can also provide accurate load forecasting basis when the power system faces peak load pressure, thereby optimizing the allocation of power resources and improving the reliability and operation efficiency of the system.

[0075] Preferably, first, collect and sort out the daily average temperature data and daily electricity data of each region in the summer of that year from the meteorological and power departments. Each region can be a province, prefecture-level city, etc. Secondly, calculate the logarithm of the daily electricity data of each region in the summer of that year (with the constant e as the base). Set the daily electricity to x, and the logarithm of the daily electricity is y=ln x. Perform a linear regression on the daily electricity of each region in the summer of that year (after taking the logarithm) and the daily average temperature (set to t). If the correlation coefficient is above 0.5, the first-order coefficient represents the growth rate of the daily electricity in each region for every 1°C increase in temperature in each region, and is set to k, that is, the linear regression equation is: y=k t+b

[0076] Among them, y represents the result of finding the logarithm of daily electricity consumption data;

[0077] k is the coefficient of the first-order term, that is, the growth rate of daily energy in each region for every 1°C rise in regional temperature, and b is the constant term.

[0078] For example: The temperature and power data of the power grids in North China, East China, Central China, Northeast China, Northwest China, and Southwest China were sorted out, and the logarithm of the power data was calculated. In the summer of 2024, by region, for every 1°C increase in the daily average temperature in North China, East China, Central China, Northeast China, Northwest China, and Southwest China, the daily cooling power consumption increased by 22.4%, 23.6%, 23.5%, 11.2%, 16.5%, and 25.5%, respectively. By sector, for every 1°C increase in the daily maximum temperature, the daily air-conditioning power consumption of the three industries and residents' lives increased by 18.5%, 11.3%, 15.8%, and 33.7%, respectively.

[0079] Finally, the daily electricity growth rates of each region are compared. A higher growth rate indicates that the daily electricity consumption in this region is more sensitive to temperature. The daily electricity growth of each region is sorted from high to low; a higher growth rate indicates that the daily electricity consumption in this region is more sensitive to temperature; a lower growth rate indicates that the daily electricity consumption in this region is less sensitive to temperature.

[0080] For example, in terms of regions, the growth of cooling electricity consumption in Southwest China, North China, East China, and Central China is more sensitive to temperature, while that in Northwest China and Northeast China is lower; residents are significantly more sensitive to high temperatures than the three industries, with residents being the most sensitive to high temperatures and the secondary industry being the least sensitive. When allocating power resources, more accurate resource allocation can be made based on these growth rates, thus improving the reliability and operating efficiency of the power system.

[0081] This application can accurately identify the subtle differences in electricity consumption in each region as the temperature changes, provide a scientific decision-making basis for the power sector, optimize resource allocation, and ensure the stability and security of power supply. Secondly, through sensitivity comparison analysis, the degree of response of different regions to temperature changes can be intuitively displayed, which helps to discover potential space for improving energy efficiency and promote energy conservation and emission reduction work in a refined and intelligent direction. In addition, this method can also be applied to urban planning, architectural design and other fields to guide related industries to take reasonable measures during high temperature periods in summer to reduce electricity consumption and improve urban energy utilization efficiency and the quality of life of residents.

[0082] Embodiment 3

[0083] The present application embodiment provides a device for calculating the sensitivity of daily electricity consumption in each area to temperature. Figure 2 As shown, including:

[0084] The acquisition module 101 is used to acquire the daily average temperature data and daily electricity consumption data of each area;

[0085] A processing module 102 is used to perform logarithmic processing on the daily power consumption data; and

[0086] The calculation module 103 is used to obtain the growth rate of daily electricity consumption in each area based on the daily average temperature data and the result of logarithm calculation based on the linear regression analysis method.

[0087] Furthermore, in the processing module:

[0088] The calculation formula for the logarithm of the daily power consumption data is as follows:

[0089] y=lnx

[0090] Where y represents the result of taking the logarithm of daily electricity consumption data; and

[0091] x represents daily electricity consumption data.

[0092] Furthermore, the calculation module is configured to:

[0093] Performing linear regression processing on the daily average temperature data and the result of calculating the logarithm to determine the correlation coefficient;

[0094] If the correlation coefficient is greater than the threshold, the growth rate is obtained.

[0095] Furthermore, in the calculation module:

[0096] The calculation formula for the growth rate of daily electricity consumption in each area is as follows:

[0097] y=k*t+b

[0098] Among them, y represents the result of finding the logarithm of daily electricity consumption data;

[0099] k represents the linear coefficient, that is, the growth rate of daily electricity consumption in each area for every 1°C rise in regional temperature;

[0100] t represents the daily mean temperature; and

[0101] b is a constant term.

[0102] An embodiment of the present application provides a computing device, including a memory, a processor, and computer instructions stored in the memory and executable on the processor, wherein when the processor executes the computer instructions, the processor implements the steps of the method for calculating the sensitivity of daily electricity consumption in each region to air temperature as described in any one of the above embodiments.

[0103] An embodiment of the present application provides a storage medium for storing computer-executable instructions, characterized in that when the computer-executable instructions are executed, the steps of the method for calculating the sensitivity of daily electricity consumption in each region to air temperature described in any one of the above embodiments are implemented.

[0104] It should be noted that the embodiments of the storage medium in this specification and the embodiments of the blockchain-based service provision method in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can refer to the aforementioned corresponding implementation of the blockchain-based service provision method, and the repeated parts will not be repeated.

[0105] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0106] In the 1930s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0107] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0108] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0109] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0110] It should be understood by those skilled in the art that one or more embodiments of this specification may be provided as a method, system or computer program product. Therefore, one or more embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] This specification 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 this specification. 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 processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0114] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0115] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0116] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0117] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0118] One or more embodiments of the present specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. One or more embodiments of the present specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0119] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0120] The above description is only an embodiment of this document and is not intended to limit this document. For those skilled in the art, this document may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document should be included in the scope of the claims of this document.

Claims

1. A method for calculating the sensitivity of daily electricity consumption in each region to temperature, characterized in that include: Obtain daily average temperature data and daily electricity consumption data for each region; Performing logarithmic processing on the daily electricity consumption data; as well as Based on the linear regression analysis method, the growth rate of daily electricity consumption in each region is obtained according to the daily average temperature data and the result of logarithm calculation.

2. The method according to claim 1, characterized in that The calculation formula for the logarithm of the daily power consumption data is as follows: y=lnx Where y represents the result of taking the logarithm of daily electricity consumption data; and x represents daily electricity consumption data.

3. The method according to claim 1, characterized in that Based on the linear regression analysis method, according to the daily average temperature data and the result of logarithm calculation, the growth rate of daily electricity consumption in each region is obtained as follows: Performing linear regression processing on the daily average temperature data and the result of calculating the logarithm to determine the correlation coefficient; If the correlation coefficient is greater than the threshold, the growth rate is obtained.

4. The method according to claim 3, characterized in that The calculation formula for the growth rate of daily electricity consumption in each area is as follows: y=k*t+b Among them, y represents the result of finding the logarithm of daily electricity consumption data; k represents the linear coefficient, that is, the growth rate of daily electricity consumption in each region for every 1°C rise in regional temperature; t represents the daily mean temperature; and b is a constant term.

5. A device for calculating the sensitivity of daily electricity consumption in each area to temperature, characterized in that include: The acquisition module is used to obtain the daily average temperature data and daily electricity consumption data of each area; A processing module, used for performing logarithmic processing on the daily power consumption data; as well as The calculation module is used to obtain the growth rate of daily electricity consumption in each area based on the daily average temperature data and the result of logarithm calculation based on the linear regression analysis method.

6. The device according to claim 5, characterized in that In the processing module: The calculation formula for the logarithm of the daily power consumption data is as follows: y=lnx Where y represents the result of taking the logarithm of daily electricity consumption data; and x represents daily electricity consumption data.

7. The device according to claim 5, characterized in that The computing module is configured as follows: Performing linear regression processing on the daily average temperature data and the result of calculating the logarithm to determine the correlation coefficient; If the correlation coefficient is greater than the threshold, the growth rate is obtained.

8. The device according to claim 5, characterized in that In the calculation module: The calculation formula for the growth rate of daily electricity consumption in each area is as follows: y=k*t+b Among them, y represents the result of finding the logarithm of daily electricity consumption data; k represents the linear coefficient, that is, the growth rate of daily electricity consumption in each area for every 1°C rise in regional temperature; t represents the daily mean temperature; and b is a constant term.

9. A computing device comprising a memory, a processor, and computer instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer instructions, the steps of the method for calculating the sensitivity of daily electricity consumption in each area to air temperature as described in any one of claims 1-4 are implemented.

10. A storage medium for storing computer executable instructions, characterized in that: When the computer executable instructions are executed, the steps of the method for calculating the sensitivity of daily electricity consumption in each area to air temperature described in any one of claims 1 to 4 are implemented.

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