Methods, apparatus, computer equipment, and media for analyzing the relationship between temperature and electricity consumption.

Abstract

This invention relates to the field of electricity, providing a method, apparatus, computer equipment, and medium for analyzing the relationship between temperature and electricity consumption. The method for analyzing the relationship between temperature and electricity consumption includes: acquiring first electricity consumption data at multiple times and corresponding temperature data for each first electricity consumption; performing mode decomposition on each first electricity consumption and each temperature data to obtain multiple first electricity consumption mode data and multiple first temperature mode data; filtering the multiple first electricity consumption mode data and the multiple first temperature mode data to obtain second electricity consumption mode data and second temperature mode data; and determining the correlation between electricity consumption and temperature based on the second electricity consumption mode data and the second temperature mode data. Through this invention, the relationship between temperature and electricity consumption is analyzed using the filtered mode data, allowing for a direct assessment of the degree of influence of temperature on electricity consumption and an accurate determination of the correlation between temperature and electricity consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electric power, and particularly relates to a method and device for analyzing the relationship between air temperature and power consumption, computer equipment and a medium. BACKGROUND

[0002] Air temperature is an important factor affecting the power consumption of the whole society. The peak period of the power consumption of the whole society throughout the year usually appears in the time when high temperature in summer or low temperature in winter occurs, which is also the most tense period of power supply and demand balance. Therefore, it is of great significance to study the quantitative influence of air temperature on the power consumption of the whole society for predicting the power consumption and load peak of the whole society and ensuring the balance of power supply and demand.

[0003] In the existing research on the relationship between air temperature and power consumption, a prediction model of temperature and power consumption is established to realize the prediction of power consumption. However, in the analysis method of the prediction model, the explicit relationship between air temperature and power consumption cannot be obtained, that is, the influence degree of air temperature on power consumption cannot be explained, and the correlation between air temperature and power consumption cannot be intuitively obtained. SUMMARY

[0004] In order to analyze the relationship between air temperature and power consumption and obtain the influence degree of air temperature on power consumption, the present application provides a method and device for analyzing the relationship between air temperature and power consumption, computer equipment and a medium.

[0005] In the first aspect, the present application provides a method for analyzing the relationship between air temperature and power consumption, which comprises the following steps:

[0006] obtaining a plurality of first power consumptions at different times and air temperature data corresponding to each first power consumption respectively;

[0007] performing modal decomposition on each first power consumption and each air temperature data respectively to obtain a plurality of first power consumption modal data and a plurality of first air temperature modal data;

[0008] performing screening on the plurality of first power consumption modal data and the plurality of first air temperature modal data respectively to obtain second power consumption modal data and second air temperature modal data;

[0009] determining the correlation between power consumption and air temperature according to the second power consumption modal data and the second air temperature modal data.

[0010] Using the above method, multiple first electricity consumption and temperature data are subjected to modal decomposition. Then, the first electricity consumption modal data and the first temperature modal data are filtered to remove modal data that are irrelevant to the temperature-electricity consumption relationship and to remove electricity consumption data affected by other factors, thereby obtaining second electricity consumption modal data and second temperature modal data. This allows for the acquisition of the correlation between temperature and electricity consumption. The method provided by this invention, when analyzing the relationship between electricity consumption and temperature, first filters the electricity consumption modal data and temperature modal data to avoid the influence of irrelevant data on the analysis of the temperature-electricity consumption relationship. This makes the correlation between temperature and electricity consumption calculated based on the second electricity consumption modal data and the second temperature modal data more accurate. Simultaneously, it can intuitively obtain the degree of influence of temperature on electricity consumption, providing a basis for power dispatch and ensuring power reliability.

[0011] In one optional implementation, the step of obtaining each first power consumption includes:

[0012] Get the second electricity consumption and the first electricity consumption growth at multiple times within the current time period. The second electricity consumption is the actual electricity consumption at each time within the current time period, and the first electricity consumption growth is the electricity consumption growth at each time within the current time period.

[0013] The first electricity consumption is obtained based on the increase in each second electricity consumption and each first electricity consumption.

[0014] In one optional implementation, the first electricity consumption is obtained based on the second electricity consumption and the first electricity consumption increase, including:

[0015] The first electricity consumption is obtained by subtracting the second electricity consumption from the first electricity consumption increase.

[0016] Considering that socio-economic development will lead to a potential increase in electricity consumption, in order to avoid the potential increase in electricity consumption affecting the analysis of the relationship between temperature and electricity consumption, the above implementation method subtracts the potential increase in electricity consumption from the actual electricity consumption to eliminate the impact of the increase in electricity consumption brought about by socio-economic development.

[0017] In one alternative implementation, the step of obtaining the first increase in electricity consumption includes:

[0018] Obtain the third electricity consumption at multiple times within the previous period corresponding to each second electricity consumption. The third electricity consumption is the actual electricity consumption at multiple times within the previous period.

[0019] Sum the electricity consumption of each third party to obtain the total electricity consumption of the previous period.

[0020] Obtain the electricity consumption growth rate, which is the average electricity consumption growth rate over multiple time periods;

[0021] Calculate the second electricity consumption growth based on the total electricity consumption and the electricity consumption growth rate;

[0022] Divide the second increase in electricity consumption by the amount of the second increase in electricity consumption to obtain the first increase in electricity consumption.

[0023] In one optional implementation, modal decomposition is performed on each of the first electricity consumption and each temperature data to obtain multiple first electricity consumption modal data and multiple first temperature modal data, including:

[0024] According to the first preset number of modes, each first power consumption is decomposed into variational modes to obtain multiple first power consumption mode data;

[0025] According to the second preset number of modes, variational mode decomposition is performed on each temperature data to obtain multiple first temperature mode data.

[0026] Through the above implementation method, variational mode decomposition is used to decompose the first electricity consumption data and temperature data respectively, resulting in multiple first electricity consumption mode data and multiple first temperature mode data. Different first electricity consumption mode data have different change frequencies, and different first temperature mode data have different change frequencies.

[0027] In one optional implementation, multiple first electricity consumption mode data and multiple first temperature mode data are filtered to obtain second electricity consumption mode data and second temperature mode data, including:

[0028] Obtain the frequency of change of each first electricity consumption mode data and the frequency of change of each first temperature mode data;

[0029] The first power consumption mode data that meets the first preset change frequency is used as the second power consumption mode data;

[0030] The first temperature mode data that meets the second preset change frequency is used as the second temperature mode data.

[0031] Through the above implementation method, the first power consumption mode data and the first temperature mode data that meet the preset change frequency are respectively used as the second power consumption mode data and the second temperature mode data. The mode data with a higher change frequency are regarded as abrupt data and are removed to avoid the influence of abrupt data on the analysis of the relationship between temperature and power consumption.

[0032] In one optional implementation, determining the correlation between electricity consumption and temperature based on second electricity consumption mode data and second temperature mode data includes:

[0033] Piecewise linear regression was performed on the second electricity consumption modal data and the second temperature modal data to obtain regression coefficients corresponding to multiple temperature ranges. The regression coefficients are used to characterize the correlation between electricity consumption and temperature.

[0034] Through the above implementation method, piecewise linear regression is used to calculate the second electricity consumption mode data and the second temperature mode data. The obtained regression coefficients characterize the correlation between electricity consumption and temperature, intuitively obtain the explicit relationship between electricity consumption and temperature, and analyze the degree of influence of temperature on electricity consumption.

[0035] In one optional implementation, the step of determining the second electricity consumption at multiple times within the current time period includes:

[0036] Get the fourth electricity consumption at all times within the current time period;

[0037] In each of the fourth electricity consumption data, the electricity consumption at a preset time is deleted to obtain the second electricity consumption data at multiple times within the current time period.

[0038] Considering that holidays and other times may affect electricity consumption, in order to avoid the influence of holidays and other factors on the analysis of the relationship between electricity consumption and temperature, this invention excludes electricity consumption data during holidays and other times when calculating the correlation between temperature and electricity consumption.

[0039] Secondly, the present invention also provides an analytical device for the relationship between temperature and electricity consumption, the device comprising:

[0040] The acquisition module is used to acquire the first electricity consumption at multiple times and the temperature data corresponding to each first electricity consumption.

[0041] The decomposition module is used to perform modal decomposition on each first power consumption and each temperature data to obtain multiple first power consumption modal data and multiple first temperature modal data.

[0042] The filtering module is used to filter multiple first electricity consumption mode data and multiple first temperature mode data respectively to obtain second electricity consumption mode data and second temperature mode data;

[0043] The determination module is used to determine the correlation between electricity consumption and temperature based on the second electricity consumption mode data and the second temperature mode data.

[0044] Using the aforementioned device, multiple first electricity consumption and temperature data are subjected to modal decomposition. Then, the first electricity consumption modal data and the first temperature modal data are filtered to remove modal data unrelated to the temperature-electricity consumption relationship, and electricity consumption data affected by other factors are also removed, resulting in second electricity consumption modal data and second temperature modal data. This allows for the acquisition of the correlation between temperature and electricity consumption. When analyzing the relationship between electricity consumption and temperature, the device provided by this invention first filters the electricity consumption modal data and temperature modal data to avoid irrelevant data affecting the analysis of the temperature-electricity consumption relationship. This makes the correlation between temperature and electricity consumption calculated based on the second electricity consumption modal data and the second temperature modal data more accurate. Simultaneously, it allows for a direct understanding of the degree of influence of temperature on electricity consumption, providing a basis for power dispatch and ensuring power reliability.

[0045] In one optional implementation, the acquisition module includes:

[0046] The first acquisition submodule is used to acquire the second electricity consumption and the first electricity consumption growth at multiple times within the current time period. The second electricity consumption is the actual electricity consumption at each time within the current time period, and the first electricity consumption growth is the electricity consumption growth corresponding to each time within the current time period.

[0047] The first determining submodule is used to obtain each first electricity consumption based on each second electricity consumption and each first electricity consumption increase.

[0048] In one alternative implementation, the first determining submodule includes:

[0049] The difference unit is used to calculate the difference between the second electricity consumption and the first electricity consumption increase to obtain the first electricity consumption.

[0050] In one optional implementation, the first acquisition submodule includes:

[0051] The first acquisition unit is used to acquire the third electricity consumption at multiple times in the previous period corresponding to each second electricity consumption, wherein the third electricity consumption is the actual electricity consumption at multiple times in the previous period.

[0052] The summation unit is used to sum the electricity consumption of each third party to obtain the total electricity consumption of the previous period.

[0053] The second acquisition unit is used to acquire the electricity consumption growth rate, which is the average electricity consumption growth rate over multiple time periods.

[0054] The first calculation unit is used to calculate the second electricity consumption growth based on the total electricity consumption and the electricity consumption growth rate.

[0055] The second calculation unit is used to divide the second electricity consumption increase by the amount of the second electricity consumption to obtain the first electricity consumption increase.

[0056] In one alternative implementation, the decomposition module includes:

[0057] The first decomposition submodule is used to perform variational mode decomposition on each first power consumption according to the first preset number of modes, so as to obtain multiple first power consumption mode data.

[0058] The second decomposition submodule is used to perform variational mode decomposition on each temperature data according to the second preset number of modes, so as to obtain multiple first temperature mode data.

[0059] Thirdly, the present invention also provides a computer device, including a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the steps of the method for analyzing the relationship between temperature and electricity consumption according to the first aspect or any embodiment of the first aspect.

[0060] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for analyzing the relationship between temperature and electricity consumption as described in the first aspect or any embodiment of the first aspect. Attached Figure Description

[0061] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0062] Figure 1 This is a flowchart of an analysis method for the relationship between temperature and electricity consumption, based on an exemplary embodiment.

[0063] Figure 2 This is an example of a graph showing the relationship between electricity consumption and daily temperature in a certain region over a period of time.

[0064] Figure 3 This is a comparison chart of the electricity consumption curve simulated using linear fitting and the actual electricity consumption curve in one example.

[0065] Figure 4 This is a comparison chart of the power consumption curve simulated by the method provided in this embodiment of the invention and the actual power consumption curve;

[0066] Figure 5 This is a schematic diagram of the structure of an analysis device for the relationship between temperature and electricity consumption according to an exemplary embodiment;

[0067] Figure 6 This is a schematic diagram of the hardware structure of a computer device according to an exemplary embodiment. Detailed Implementation

[0068] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0070] To analyze the relationship between temperature and electricity consumption and to obtain the degree of influence of temperature on electricity consumption, this invention proposes a method, apparatus, computer equipment, and medium for analyzing the relationship between temperature and electricity consumption.

[0071] Figure 1 This is a flowchart illustrating an analytical method for the relationship between temperature and electricity consumption, based on an exemplary embodiment. Figure 1 As shown, the analysis method for the relationship between temperature and electricity consumption includes the following steps S101 to S104.

[0072] Step S101: Obtain the first power consumption at multiple times and the temperature data corresponding to each first power consumption.

[0073] In one alternative embodiment, the first electricity consumption at multiple moments can be the electricity consumption for each day of the year.

[0074] In one optional embodiment, the temperature data corresponding to the first electricity consumption refers to the temperature data at the same moment as the first electricity consumption. When the first electricity consumption is the daily electricity consumption throughout the year, the temperature data corresponding to the first electricity consumption refers to the daily temperature. For example, the daily temperature can be the average of the daily maximum temperature and the daily minimum temperature.

[0075] Step S102: Perform modal decomposition on each first power consumption and each temperature data to obtain multiple first power consumption modal data and multiple first temperature modal data.

[0076] In one optional embodiment, mode decomposition is a frequency domain processing method used to divide multiple data according to their variation frequency. In this embodiment of the invention, variational mode decomposition is used to divide the first electricity consumption mode data, resulting in multiple first electricity consumption mode data. The variation frequencies of the first electricity consumption mode data belonging to different modes are different, and the variation frequencies of the first electricity consumption mode data belonging to the same mode are the same. Similarly, variational mode decomposition is used to divide the first temperature mode data, resulting in multiple first temperature mode data. The variation frequencies of the first temperature mode data belonging to different modes are different, and the variation frequencies of the first temperature mode data belonging to the same mode are the same.

[0077] Step S103: Filter the multiple first power consumption mode data and multiple first temperature mode data respectively to obtain the second power consumption mode data and the second temperature mode data.

[0078] In one optional embodiment, mode decomposition yields multiple modal data with different frequency variations. Therefore, in this embodiment of the invention, the multiple modal data with different frequency variations can be filtered by preset frequency variations to obtain second electricity consumption modal data and second temperature modal data. Modal data with high frequency variations are considered outliers. Therefore, it is necessary to remove high-frequency modal data from the multiple first electricity consumption modal data and the multiple first temperature modal data to ensure accuracy in analyzing the relationship between temperature and electricity consumption.

[0079] Step S104: Determine the correlation between electricity consumption and temperature based on the second electricity consumption mode data and the second temperature mode data.

[0080] In an optional embodiment, piecewise linear regression can be used to calculate the regression coefficient between the second electricity consumption modal data and the second temperature modal data. The regression coefficient characterizes the correlation between electricity consumption and temperature. The larger the regression coefficient, the greater the influence of temperature on electricity consumption; the smaller the regression coefficient, the smaller the influence of temperature on electricity consumption.

[0081] Using the above method, multiple first electricity consumption and temperature data are subjected to modal decomposition. Then, the first electricity consumption modal data and the first temperature modal data are filtered to remove modal data that are irrelevant to the temperature-electricity consumption relationship and to remove electricity consumption data affected by other factors, thereby obtaining second electricity consumption modal data and second temperature modal data. This allows for the acquisition of the correlation between temperature and electricity consumption. The method provided by this invention, when analyzing the relationship between electricity consumption and temperature, first filters the electricity consumption modal data and temperature modal data to avoid the influence of irrelevant data on the analysis of the temperature-electricity consumption relationship. This makes the correlation between temperature and electricity consumption calculated based on the second electricity consumption modal data and the second temperature modal data more accurate. Simultaneously, it can intuitively obtain the degree of influence of temperature on electricity consumption, providing a basis for power dispatch and ensuring power reliability.

[0082] In one example, considering the potential increase in electricity consumption due to socio-economic development, to avoid the potential increase in electricity consumption affecting the analysis of the relationship between temperature and electricity consumption, this embodiment of the invention subtracts the potential increase in electricity consumption from the actual electricity consumption, thus eliminating the impact of the increase in electricity consumption brought about by socio-economic development. In step S101 above, each first electricity consumption is obtained through the following steps:

[0083] Step a1: Obtain the second electricity consumption and the first electricity consumption increase at multiple times within the current time period. The second electricity consumption is the actual electricity consumption at each time within the current time period, and the first electricity consumption increase is the electricity consumption increase at each time within the current time period.

[0084] Step a2: Obtain each first electricity consumption based on each second electricity consumption and each first electricity consumption increase. In this embodiment of the invention, the first electricity consumption is obtained by subtracting the second electricity consumption from the first electricity consumption increase.

[0085] In an optional embodiment, considering that holidays can also affect electricity consumption, to avoid the impact of holidays on the analysis of the relationship between temperature and electricity consumption, in this embodiment of the invention, electricity consumption data during holidays and other times need to be excluded when calculating the correlation between temperature and electricity consumption. Therefore, in step a1 above, the second electricity consumption at multiple times within the current period is obtained in the following way:

[0086] First, obtain the fourth electricity consumption data for all moments within the current time period. For example, the current time period can be 2023, and all moments can be each day of 2023.

[0087] Then, in each of the fourth electricity consumption data, the electricity consumption at a preset time is deleted to obtain the second electricity consumption data at multiple times within the current time period. For example, the preset time can be a period of time before or after a holiday such as the Spring Festival.

[0088] In an optional embodiment, in step a1 above, the first increase in electricity consumption is obtained by means of:

[0089] First, obtain the third electricity consumption for multiple moments within the previous period corresponding to each second electricity consumption. The third electricity consumption is the actual electricity consumption at multiple moments within the previous period. For example, if the current period is 2023, the multiple moments within the current period are the moments in 2023 after removing holidays, and the previous period is 2022, the multiple moments within the previous period are the moments in 2022 after removing holidays.

[0090] Secondly, sum up the electricity consumption of each third party to obtain the total electricity consumption of the previous period.

[0091] Next, obtain the electricity consumption growth rate, which is the average electricity consumption growth rate over multiple time periods. For example, when using one year as a time period, the average annual electricity consumption growth rate over the past five years can be used as the electricity consumption growth rate.

[0092] Then, based on the total electricity consumption and the electricity consumption growth rate, the second electricity consumption growth is calculated. For example, the total electricity consumption is multiplied by the electricity consumption growth rate to obtain the second electricity consumption growth.

[0093] Finally, the second electricity consumption increase is divided by the amount of the second electricity consumption to obtain the first electricity consumption increase. The first electricity consumption increase is the potential increase brought about by socio-economic development that needs to be removed at each point in the current time period.

[0094] In one example, in step S102 above, mode decomposition is performed on the first electricity consumption and temperature data as follows:

[0095] First, according to the first preset number of modes, variational mode decomposition (VMD) is performed on each first power consumption mode to obtain multiple first power consumption mode data. Different first power consumption mode data correspond to different change frequencies.

[0096] Then, according to the second preset number of modes, variational mode decomposition is performed on each temperature data to obtain multiple first temperature mode data. Different first temperature mode data correspond to different frequencies of change.

[0097] The goal of the VMD method is to minimize the sum of the bandwidths while satisfying the constraint that the sum of the decomposed modes equals the original input signal. This goal and constraint can be expressed as follows:

[0098]

[0099]

[0100] Where δ(t) represents the Dirac distribution, f(t) represents the original signal, and y k (t) represents the decomposition result of the k-th mode, ω k The center frequency (frequency of change) of each modality data is represented by t, and time t represents time.

[0101] To find the optimal solution, Lagrange multipliers and a penalty parameter are introduced, transforming the constrained problem into an unconstrained one. Therefore, this unconstrained problem can be represented by the following formula:

[0102]

[0103] Where L is the constraint function, λ is the Lagrange multiplier, and α is the penalty parameter.

[0104] Then, the Alternating Direction Method of Multipliers (ADMM) is applied to solve the dual unconstrained problem, through iterative updates. and λ n+1 (n is the number of iterations) to find the saddle point of the equation. The updates can be performed using the following formulas:

[0105]

[0106]

[0107] in, and They are y i The Fourier transforms of f(t), f(t), and λ(t) can be calculated using the following formula:

[0108]

[0109] Where τ is a constant coefficient.

[0110] The iteration termination condition is expressed by the following formula:

[0111]

[0112] Where ε represents the standard for evaluating accuracy.

[0113] In one example, in step S103 above, multiple first electricity consumption mode data and multiple first temperature mode data are filtered in the following manner:

[0114] First, the frequency of change of each first power consumption mode data and the frequency of change of each first temperature mode data are obtained.

[0115] Then, the first power consumption mode data that meets the first preset change frequency is used as the second power consumption mode data.

[0116] Finally, the first temperature mode data that meets the second preset change frequency is used as the second temperature mode data.

[0117] In this embodiment of the invention, the number of the first preset modes is set to 3, meaning that variational mode decomposition of the first electricity consumption yields three first electricity consumption modal data items. These three first electricity consumption modal data items are named electricity consumption trend item, electricity consumption cycle item, and electricity consumption fluctuation item, respectively. The change frequencies of the three first electricity consumption modal data items, from low to high, are electricity consumption trend item, electricity consumption cycle item, and electricity consumption fluctuation item. For the first electricity consumption modal data, the electricity consumption fluctuation item has the highest change frequency, indicating large fluctuations in electricity consumption, which is abnormal data. Therefore, the fluctuation item is not used for analyzing the relationship between temperature and electricity consumption. The electricity consumption cycle item represents electricity consumption data affected by patterns such as non-working days; therefore, it is also not used for analyzing the relationship between temperature and electricity consumption. Therefore, in this embodiment of the invention, the electricity consumption trend item represents electricity consumption data affected by temperature, and the relationship between temperature and electricity consumption is analyzed using the electricity consumption trend item.

[0118] In this embodiment of the invention, the number of second preset modes is set to 2, and the two first temperature mode data are named temperature trend and temperature fluctuation, respectively. The change frequencies of the two first temperature mode data, from low to high, are temperature trend and temperature fluctuation, respectively. For the first temperature mode data, the change frequency of the temperature fluctuation item is higher than that of the temperature trend item, indicating that the temperature fluctuation item is a sudden temperature change. In this case, the temperature fluctuation item is regarded as abnormal data, and the temperature trend item is used as the second temperature mode data to analyze the relationship between temperature and electricity consumption.

[0119] Through the above implementation method, the first electricity consumption mode data that meets the first preset frequency of change is used as the second electricity consumption mode data, and the first temperature mode data that meets the second preset frequency of change is used as the second temperature mode data. Mode data with high frequency of change are considered abrupt changes and are removed to avoid the influence of abrupt changes on the analysis of the relationship between temperature and electricity consumption. At the same time, the periodic electricity consumption term affected by non-working days and other regularities is removed to avoid the influence of factors other than temperature on the analysis results, making the calculated relationship between temperature and electricity consumption more accurate.

[0120] In one example, in step S104 above, piecewise linear regression is performed on the second electricity consumption modal data and the second temperature modal data to obtain regression coefficients corresponding to multiple temperature intervals. These regression coefficients characterize the correlation between electricity consumption and temperature. A larger regression coefficient indicates a greater influence of temperature on electricity consumption. For example, a piecewise linear function (PiecewiseLinFit function) can be used to perform piecewise linear regression on the second electricity consumption modal data and the second temperature modal data.

[0121] Through the embodiments of the present invention, piecewise linear regression is used to calculate the second electricity consumption modal data and the second temperature modal data. The obtained regression coefficients characterize the correlation between electricity consumption and temperature, intuitively obtain the explicit relationship between electricity consumption and temperature, and analyze the degree of influence of temperature on electricity consumption.

[0122] In one example, the method provided by this embodiment of the invention further includes: performing piecewise linear regression on the second electricity consumption modal data and the second temperature modal data to obtain a quantitative relationship between temperature and electricity consumption. Here, the quantitative relationship refers to the specific functional relationship between temperature and electricity consumption. Through the quantitative relationship between temperature and electricity consumption, and the predicted temperature for the future, it is possible to predict and simulate future electricity consumption.

[0123] Figure 2 This is a graph showing the relationship between electricity consumption and daily temperature in a certain region over a period of time. Figure 3 This is a comparison chart of the electricity consumption curve simulated using the linear fitting method and the actual electricity consumption curve. Figure 4 This is a comparison chart of the electricity consumption curve simulated by the method provided in this embodiment of the invention and the actual electricity consumption curve. Calculations show that the root mean square error (RMSE) between the electricity consumption simulated using the linear fitting method and the actual electricity consumption is 19186, meaning that for every degree Celsius increase in temperature, daily electricity consumption increases by 28.25 million kWh. In contrast, the root mean square error (RMSE) between the electricity consumption simulated by the method provided in this embodiment of the invention and the actual electricity consumption is 2651, meaning that for every degree Celsius increase in temperature within the range of 25.7℃-33.5℃, daily electricity consumption increases by 37.74 million kWh. Compared to the linear fitting method, the accuracy is significantly improved.

[0124] Based on the same inventive concept, embodiments of the present invention also provide an analytical device for the relationship between temperature and electricity consumption, such as... Figure 5 As shown, the device includes:

[0125] The acquisition module 501 is used to acquire the first power consumption at multiple times and the temperature data corresponding to each first power consumption; for details, please refer to the description of step S101 in the above embodiment, which will not be repeated here.

[0126] The decomposition module 502 is used to perform modal decomposition on each first power consumption and each temperature data to obtain multiple first power consumption modal data and multiple first temperature modal data; for details, please refer to the description of step S102 in the above embodiment, which will not be repeated here.

[0127] The filtering module 503 is used to filter multiple first power consumption mode data and multiple first temperature mode data respectively to obtain second power consumption mode data and second temperature mode data; for details, please refer to the description of step S103 in the above embodiment, which will not be repeated here.

[0128] The determination module 504 is used to determine the correlation between electricity consumption and temperature based on the second electricity consumption mode data and the second temperature mode data. For details, please refer to the description of step S104 in the above embodiments, which will not be repeated here.

[0129] Using the aforementioned device, multiple first electricity consumption and temperature data are subjected to modal decomposition. Then, the first electricity consumption modal data and the first temperature modal data are filtered to remove modal data unrelated to the temperature-electricity consumption relationship, and electricity consumption data affected by other factors are also removed, resulting in second electricity consumption modal data and second temperature modal data. This allows for the acquisition of the correlation between temperature and electricity consumption. When analyzing the relationship between electricity consumption and temperature, the device provided by this invention first filters the electricity consumption modal data and temperature modal data to avoid irrelevant data affecting the analysis of the temperature-electricity consumption relationship. This makes the correlation between temperature and electricity consumption calculated based on the second electricity consumption modal data and the second temperature modal data more accurate. Simultaneously, it allows for a direct understanding of the degree of influence of temperature on electricity consumption, providing a basis for power dispatch and ensuring power reliability.

[0130] In one example, module 501 includes:

[0131] The first acquisition submodule is used to acquire the second electricity consumption and the first electricity consumption increase at multiple times within the current time period. The second electricity consumption is the actual electricity consumption at each time within the current time period, and the first electricity consumption increase is the electricity consumption increase at each time within the current time period. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0132] The first determining submodule is used to obtain each first electricity consumption based on each second electricity consumption and each first electricity consumption increase. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0133] In one example, the first determined submodule includes:

[0134] The difference unit is used to calculate the difference between the second electricity consumption and the first electricity consumption increase to obtain the first electricity consumption. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0135] In one example, the first acquisition submodule includes:

[0136] The first acquisition unit is used to acquire the third electricity consumption at multiple times within the previous period corresponding to each second electricity consumption. The third electricity consumption is the actual electricity consumption at multiple times within the previous period. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0137] The summation unit is used to sum the electricity consumption of each third party to obtain the total electricity consumption of the previous period; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0138] The second acquisition unit is used to acquire the electricity consumption growth rate, which is the average electricity consumption growth rate over multiple time periods; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0139] The first calculation unit is used to calculate the second electricity consumption growth based on the total electricity consumption and the electricity consumption growth rate; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0140] The second calculation unit is used to divide the second electricity consumption increase by the amount of the second electricity consumption to obtain the first electricity consumption increase. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0141] In one example, the decomposition module 502 includes:

[0142] The first decomposition submodule is used to perform variational mode decomposition on each first power consumption according to the first preset number of modes to obtain multiple first power consumption mode data; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0143] The second decomposition submodule is used to perform variational mode decomposition on each temperature data according to a second preset number of modes, to obtain multiple first temperature mode data. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0144] In one example, the filtering module 503 includes:

[0145] The second acquisition submodule acquires the change frequency of each first power consumption mode data and the change frequency of each first temperature mode data; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0146] The first filtering submodule is used to select the first power consumption mode data that meets the first preset change frequency as the second power consumption mode data; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0147] The second filtering submodule is used to select the first temperature mode data that meets the second preset change frequency as the second temperature mode data. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0148] In one example, module 504 is determined to include:

[0149] The calculation submodule is used to perform piecewise linear regression on the second electricity consumption modal data and the second temperature modal data to obtain regression coefficients corresponding to multiple temperature intervals. The regression coefficients are used to characterize the correlation between electricity consumption and temperature. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0150] In one example, the first acquisition submodule includes:

[0151] The third acquisition unit is used to acquire the fourth electricity consumption at all times within the current time period; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0152] The deletion unit is used to delete the electricity consumption at a preset time from each of the fourth electricity consumption data, thereby obtaining the second electricity consumption at multiple times within the current time period. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0153] The specific limitations and beneficial effects of the aforementioned device can be found in the analysis method for the relationship between temperature and electricity consumption described above, and will not be repeated here. Each of the above modules can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0154] Figure 6 This is a schematic diagram of the hardware structure of a computer device according to an exemplary embodiment. For example... Figure 6 As shown, the device includes one or more processors 610 and a memory 620, the memory 620 including persistent memory, volatile memory, and a hard disk. Figure 6 Taking a processor 610 as an example, the device may also include an input device 630 and an output device 640.

[0155] The processor 610, memory 620, input device 630, and output device 640 can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0156] Processor 610 can be a Central Processing Unit (CPU). Processor 610 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0157] The memory 620, as a non-transitory computer-readable storage medium, includes persistent memory, volatile memory, and a hard disk. It can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the method for analyzing the relationship between temperature and electricity consumption in this embodiment. The processor 610 executes various server functions and data processing by running the non-transitory software programs, instructions, and modules stored in the memory 620, thereby implementing any of the above-mentioned methods for analyzing the relationship between temperature and electricity consumption.

[0158] The memory 620 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data as needed. Furthermore, the memory 620 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 620 may optionally include memory remotely located relative to the processor 610, and these remote memories can be connected to the data processing device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0159] Input device 630 can receive input digital or character information, and generate signal inputs related to user settings and function control. Output device 640 may include display devices such as a display screen.

[0160] One or more modules are stored in memory 620, and when executed by one or more processors 610, they perform actions such as... Figure 1 The method shown.

[0161] The above-described product can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in [reference 1]. Figure 1 The relevant descriptions in the illustrated embodiments.

[0162] This invention also provides a non-transitory computer storage medium storing computer-executable instructions that can execute the methods described in any of the above method embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0163] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0164] The above are merely specific embodiments of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method of analyzing the relationship between air temperature and power consumption, characterized by, The method comprises: obtaining a plurality of time instants of first power consumption and the corresponding air temperature data of each of the first power consumption; respectively, modal decomposition is performed on each of the first power consumption and the air temperature data to obtain a plurality of first power consumption modal data and a plurality of first air temperature modal data; respectively, the plurality of first power consumption modal data and the plurality of first air temperature modal data are screened to obtain second power consumption modal data and second air temperature modal data; determine the correlation between power consumption and air temperature according to the second power consumption modal data and the second air temperature modal data; The step of obtaining each of the first power consumption comprises: obtaining a plurality of time instants of second power consumption and first power consumption growth in the current period, the second power consumption is the actual power consumption at each of the time instants in the current period, and the first power consumption growth is the power consumption growth corresponding to each of the time instants in the current period; obtaining each of the first power consumption according to each of the second power consumption and each of the first power consumption growth; The step of obtaining the first power consumption growth comprises: obtaining a plurality of time instants of third power consumption in the previous period corresponding to each of the second power consumption, the third power consumption is the actual power consumption at a plurality of time instants in the previous period; summing each of the third power consumption to obtain the total power consumption in the previous period; obtaining the power consumption growth rate, the power consumption growth rate is the average power consumption growth rate of a plurality of periods; calculate the second power consumption growth according to the total power consumption and the power consumption growth rate; divide the second power consumption growth by the number of the second power consumption to obtain the first power consumption growth.

2. The method of claim 1, wherein, According to the second power consumption and the first power consumption growth, the first power consumption is obtained, comprising: differencing the second power consumption and the first power consumption growth to obtain the first power consumption.

3. The method of claim 1, wherein, Respectively, modal decomposition is performed on each of the first power consumption and the air temperature data to obtain a plurality of first power consumption modal data and a plurality of first air temperature modal data, comprising: According to the first preset modal number, the first power consumption is decomposed into a plurality of first power consumption modal data by variational modal decomposition; According to the second preset modal number, the air temperature data is decomposed into a plurality of first air temperature modal data by variational modal decomposition.

4. The method of claim 1, wherein, Respectively, the plurality of first power consumption modal data and the plurality of first air temperature modal data are screened to obtain second power consumption modal data and second air temperature modal data, comprising: obtaining the change frequency of each of the first power consumption modal data and the change frequency of each of the first air temperature modal data; the first power consumption modal data meeting the first preset change frequency is taken as the second power consumption modal data; the first air temperature modal data meeting the second preset change frequency is taken as the second air temperature modal data.

5. The method of claim 1, wherein, According to the second power consumption modal data and the second air temperature modal data, the correlation between power consumption and air temperature is determined, comprising: segmented linear regression is performed on the second power consumption modal data and the second air temperature modal data to obtain regression coefficients corresponding to a plurality of temperature intervals, the regression coefficients are used to represent the correlation between power consumption and air temperature.

6. The method of claim 1, wherein, The step of obtaining a plurality of time instants of second power consumption in the current period comprises: obtaining fourth power consumptions at all time points in a current period; in the fourth power consumptions, deleting power consumptions at preset time points to obtain second power consumptions at multiple time points in the current period.

7. An analytical device for the relationship between air temperature and electricity consumption, characterized in that, The device comprises: an obtaining module, configured to obtain first power consumptions at multiple time points and air temperature data corresponding to the first power consumptions respectively; a decomposition module, configured to perform modal decomposition on the first power consumptions and the air temperature data respectively to obtain first power consumption modal data and first air temperature modal data; a screening module, configured to screen the first power consumption modal data and the first air temperature modal data respectively to obtain second power consumption modal data and second air temperature modal data; a determining module, configured to determine a correlation between power consumption and air temperature according to the second power consumption modal data and the second air temperature modal data; a first obtaining sub-module, configured to obtain second power consumptions at multiple time points in a current period and first power consumption increases, the second power consumptions being actual power consumptions at the time points in the current period, and the first power consumption increases being power consumption increases corresponding to the time points in the current period; a first determining sub-module, configured to obtain the first power consumptions according to the second power consumptions and the first power consumption increases; the first obtaining sub-module comprises: a first obtaining unit, configured to obtain third power consumptions at multiple time points in a previous period corresponding to the second power consumptions, the third power consumptions being actual power consumptions at the time points in the previous period; a summing unit, configured to sum the third power consumptions to obtain a total power consumption in the previous period; a second obtaining unit, configured to obtain a power consumption increase rate, the power consumption increase rate being an average power consumption increase rate of multiple periods; a first calculating unit, configured to calculate a second power consumption increase according to the total power consumption and the power consumption increase rate; a second calculating unit, configured to divide the second power consumption increase by a quantity of the second power consumptions to obtain the first power consumption increases.

8. The apparatus of claim 7, wherein, the first determining sub-module comprises: a difference unit, configured to subtract the first power consumption increases from the second power consumptions to obtain the first power consumptions.

9. The apparatus of claim 7, wherein, the decomposition module comprises: a first decomposition sub-module, configured to perform variational modal decomposition on the first power consumptions according to a first preset modal quantity to obtain the first power consumption modal data; a second decomposition sub-module, configured to perform variational modal decomposition on the air temperature data according to a second preset modal quantity to obtain the first air temperature modal data.

10. A computer device, comprising: a memory and a processor, which are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the steps of the method for analyzing the correlation between air temperature and power consumption according to any one of claims 1-6.

11. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method for analyzing the correlation between air temperature and power consumption according to any one of claims 1-6. The computer program, when executed by the processor, implements the steps of the method for analyzing the correlation between air temperature and power consumption according to any one of claims 1-6.

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