Resident energy-saving effect evolution prediction method and system

Abstract

The invention discloses a resident energy-saving effect evolution prediction method and system, and the method comprises the steps: obtaining user preferences, user energy-saving knowledge levels and user education levels in a resident community, obtaining the number of household electric appliances of a user, and obtaining the data of a power transmission line in the resident community; on the basis of an entropy weight method, entropy values of user preferences, user energy-saving knowledge levels, user education levels and user household electric appliance numbers are calculated, and user load data are obtained through calculation according to the entropy values; performing transformation processing on the user load data and the power transmission line data based on Fourier transform, and analyzing correlation characteristics between the user load data and the power transmission line data to obtain a load correlation factor and a power transmission line correlation factor; and performing association prediction analysis on the user load data based on the load association factor and the power transmission line association factor to obtain load characteristic data. According to the resident energy-saving effect evolution prediction method provided by the embodiment of the invention, accurate detection of load prediction is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of load forecasting, and in particular to a method and system for predicting the evolution of residents' energy-saving effects. Background Art

[0002] The strong random characteristics of modern power systems are becoming increasingly prominent, and accurate load forecasting plays an increasingly important role in the planning and operation of future power grids.

[0003] However, traditional load forecasting methods are often based on fixed statistical laws and cannot accurately predict future electricity loads. Improving load forecasting accuracy is conducive to planned electricity management.

[0004] How to accurately perform load forecasting has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0005] The present invention provides a method and system for predicting the evolution of energy-saving effects of residents, which solve the technical problem of inaccurate load prediction and realize accurate detection of load prediction.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a method for predicting the evolution of energy-saving effects of residents, comprising:

[0007] Obtain user preferences, user energy-saving knowledge and user education level in the residential community, as well as the number of household electrical appliances used by users and data on power transmission lines in the residential community;

[0008] Calculate the entropy value of the user preference, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user based on the entropy weight method, and obtain the user load data according to the entropy value;

[0009] Transforming the user load data and the transmission line data based on Fourier transform, respectively analyzing the correlation characteristics between the user load data and the transmission line data to obtain a load correlation factor and a transmission line correlation factor;

[0010] Based on the load correlation factor and the transmission line correlation factor, the user load data is subjected to correlation prediction analysis to obtain load characteristic data, wherein the correlation prediction analysis is configured to use the load correlation factor and the transmission line correlation factor as parameters of a recurrent neural network based on a gated cyclic unit, and to optimize the parameters using an adaptive estimation prediction algorithm to obtain the load characteristic data.

[0011] As one of the preferred solutions, the recurrent neural network based on the gated recurrent unit includes a convolutional layer, a pooling layer, a fully connected layer and an output layer;

[0012] Among them, the convolution layer performs convolution processing on the user load data to obtain filtered data, the pooling layer performs pooling processing on the filtered data to obtain feature mapping data, the fully connected layer performs nonlinear transformation processing on the feature mapping data to obtain a load feature vector, and the output layer processes the load feature vector to obtain the load feature data.

[0013] As one of the preferred solutions, before calculating the entropy values ​​of the user preferences, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user based on the entropy weight method, the method for predicting the evolution of the energy-saving effect of residents also includes:

[0014] Data preprocessing is performed on the user preferences, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user, and the preprocessing steps include data cleaning, data integration, data transformation and data reduction.

[0015] As one of the preferred solutions, the entropy value of the user preference, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user is calculated based on the entropy weight method, and the user load data is obtained according to the entropy value calculation, including:

[0016] Based on the entropy weight method, the weights corresponding to the user preference, the user's energy-saving knowledge level, the user's education level and the number of household appliances in the user are respectively determined; based on the weights, the entropy value of the user preference, the entropy value of the user's energy-saving knowledge level, the entropy value of the user's education level and the entropy value of the number of household appliances in the user, the user load data is obtained.

[0017] As one of the preferred solutions, the analysis of the correlation characteristics between the user load data and the transmission line data to obtain the load correlation factor and the transmission line correlation factor includes:

[0018] Analyzing the correlation characteristics between the user load data and the transmission line data based on cross-correlation function analysis to obtain the load correlation factor;

[0019] Based on the frequency domain coherence analysis, the correlation characteristics between the user load data and the transmission line data are analyzed to obtain the transmission line correlation factor.

[0020] Another embodiment of the present invention provides a system for predicting the evolution of energy-saving effects of residents, including:

[0021] The acquisition module is used to obtain user preferences, user energy-saving knowledge levels, and user education levels in the residential community, as well as the number of household electrical appliances used by users and the data of power transmission lines in the residential community;

[0022] A calculation module, used to calculate the entropy value of the user preference, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user based on the entropy weight method, and obtain the user load data according to the entropy value calculation;

[0023] An analysis module, used for transforming the user load data and the transmission line data based on Fourier transform, respectively analyzing the correlation characteristics between the user load data and the transmission line data, and obtaining a load correlation factor and a transmission line correlation factor;

[0024] A prediction module is used to perform correlation prediction analysis on the user load data based on the load correlation factor and the transmission line correlation factor to obtain load characteristic data, wherein the correlation prediction analysis is configured to use the load correlation factor and the transmission line correlation factor as parameters of a recurrent neural network based on a gated cyclic unit, and use an adaptive estimation prediction algorithm to optimize the parameters to obtain the load characteristic data.

[0025] As one of the preferred solutions, the recurrent neural network based on the gated recurrent unit includes a convolutional layer, a pooling layer, a fully connected layer and an output layer;

[0026] Among them, the convolution layer performs convolution processing on the user load data to obtain filtered data, the pooling layer performs pooling processing on the filtered data to obtain feature mapping data, the fully connected layer performs nonlinear transformation processing on the feature mapping data to obtain a load feature vector, and the output layer processes the load feature vector to obtain the load feature data.

[0027] As one of the preferred solutions, before calculating the entropy values ​​of the user preferences, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user based on the entropy weight method, the resident energy-saving effect evolution prediction system also includes:

[0028] The preprocessing module is used to perform data preprocessing on the user preferences, the user's energy-saving knowledge level, the user's education level and the number of household appliances of the user, wherein the preprocessing steps include data cleaning, data integration, data transformation and data reduction.

[0029] As one of the preferred solutions, the entropy value of the user preference, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user is calculated based on the entropy weight method, and the user load data is obtained according to the entropy value calculation, including:

[0030] Based on the entropy weight method, the weights corresponding to the user preference, the user's energy-saving knowledge level, the user's education level and the number of household appliances in the user are respectively determined; based on the weights, the entropy value of the user preference, the entropy value of the user's energy-saving knowledge level, the entropy value of the user's education level and the entropy value of the number of household appliances in the user, the user load data is obtained.

[0031] As one of the preferred solutions, the analysis of the correlation characteristics between the user load data and the transmission line data to obtain the load correlation factor and the transmission line correlation factor includes:

[0032] Analyzing the correlation characteristics between the user load data and the transmission line data based on cross-correlation function analysis to obtain the load correlation factor;

[0033] Based on the frequency domain coherence analysis, the correlation characteristics between the user load data and the transmission line data are analyzed to obtain the transmission line correlation factor.

[0034] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0035] This embodiment adopts a multi-dimensional data analysis method, integrates data resources in residential communities, and uses the potential correlation between user preferences, energy-saving knowledge level, education level, number of household appliances and transmission line data to accurately quantify user-related factors through the entropy weight method to generate user load data, and then uses Fourier transform to analyze data correlation to obtain correlation factors, and finally uses advanced gated recurrent unit neural network and adaptive estimation prediction algorithm to optimize parameters for load forecasting. This embodiment can not only significantly improve the accuracy of load forecasting, but also provide a comprehensive and detailed decision-making basis for power management, rationally plan power distribution, and accurately formulate energy-saving plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of a flow chart of a method for predicting the evolution of energy-saving effects of residents in one embodiment of the present invention;

[0037] Figure 2 It is a schematic diagram of the structure of a system for predicting the evolution of energy-saving effects of residents in one embodiment of the present invention;

[0038] Figure 3 It is a schematic diagram of a recurrent neural network based on a gated recurrent unit in one embodiment of the present invention.

[0039] Reference numerals:

[0040] Among them, 11, acquisition module; 12, calculation module; 13, analysis module; 14, prediction module; 21, input layer; 22, convolution layer; 23, pooling layer; 24, fully connected layer; 25, output layer. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0043] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0044] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0045] An embodiment of the present invention provides a method for predicting the evolution of energy-saving effects of residents. For details, see Figure 1 , Figure 1 The flowchart of the method for predicting the evolution of energy-saving effect of residents in one embodiment of the present invention is shown as follows:

[0046] S1: Obtain user preferences, user energy-saving knowledge level, and user education level in the residential community, as well as the number of household appliances used by users and the data of power transmission lines in the residential community;

[0047] S2: Calculate the entropy value of the user preference, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user based on the entropy weight method, and obtain the user load data according to the entropy value;

[0048] S3: transforming the user load data and the transmission line data based on Fourier transform, respectively analyzing the correlation characteristics between the user load data and the transmission line data to obtain a load correlation factor and a transmission line correlation factor;

[0049] S4: Based on the load correlation factor and the transmission line correlation factor, the user load data is subjected to correlation prediction analysis to obtain load characteristic data, wherein the correlation prediction analysis is configured to use the load correlation factor and the transmission line correlation factor as parameters of a recurrent neural network based on a gated cyclic unit, and an adaptive estimation prediction algorithm is used to optimize the parameters to obtain the load characteristic data.

[0050] In step S1, user preferences, user energy-saving knowledge level, user education level and the number of household electrical appliances of the user are preprocessed. The preprocessing steps include data cleaning, data integration, data transformation and data reduction.

[0051] Specifically, it includes not only user preferences, user energy-saving knowledge level, user education level and the number of household electrical appliances, but also factors that may affect user electricity usage, such as family income level and the age of family members, which will not be elaborated here.

[0052] The preprocessing step can improve data quality and reduce computing and storage requirements, which is conducive to improving data analysis and prediction efficiency.

[0053] In step S2, weights corresponding to user preferences, user energy-saving knowledge level, user education level and the number of household appliances in the user are determined based on the entropy weight method, and user load data is obtained based on the weights, entropy values ​​of user preferences, entropy values ​​of user energy-saving knowledge level, entropy values ​​of user education level and entropy values ​​of the number of household appliances in the user.

[0054] Among them, the entropy weight method is a comprehensive evaluation method for multiple objects and multiple indicators based on information theory, and is used to evaluate indicator weights in the present invention.

[0055] In step S3, Fourier transform is a mathematical tool that converts time domain signals into frequency domain representation. It is very effective in processing data with time series characteristics such as user load data and transmission line data. It can decompose the different frequency components hidden in the data, thereby gaining insight into the intrinsic characteristics of the data such as periodicity and volatility.

[0056] For example, user load data may show periodic changes in peak power consumption in the morning and evening and low power consumption at noon in a day. The Fourier transform can clearly show the frequency components corresponding to this periodicity, and transform the complex load data that changes with time into an expression consisting of different frequency amplitudes and phases in the frequency domain. Similarly, transmission line data (such as the changes in line current, voltage, etc. over time) can also be transformed in this way.

[0057] After completing Fourier transform on user load data and transmission line data respectively, the correlation characteristics between them are analyzed. Commonly used correlation analysis methods include calculating the cross-correlation function and calculating the coherence in the frequency domain.

[0058] The cross-correlation function analysis is to find their correlation in time delay by calculating the cross-correlation function of the two in the time domain, that is, to determine how long a certain change trend of the transmission line data will affect the user load data after a delay, and the strength of the impact, etc., and extract the key features reflecting this correlation as part of the load correlation factor.

[0059] Frequency domain coherence analysis examines the coherence of the two in the frequency domain, that is, to see the strength of the correlation between user load data and transmission line data on different frequency components.

[0060] Through two correlation feature analyses, namely, cross-correlation function analysis in the time domain and coherence analysis in the frequency domain, the intrinsic relationship between user load data and transmission line data can be explored, providing a strong basis for power load forecasting.

[0061] The load correlation factor and transmission line correlation factor obtained are used as parameters of the recurrent neural network based on the gated recurrent unit because the recurrent neural network based on the gated recurrent unit has excellent performance in processing time series data and capturing long-term dependencies in sequence data. In this model, these correlation factors are used as input parameters, and the network gradually learns and models the changing trend of user load data through the internal hidden state update mechanism. For example, at each time step, according to the correlation factor input at the current moment and the hidden state at the previous moment, the recurrent neural network based on the gated recurrent unit can decide which information to update and which information to forget, and then generate the prediction results of the load data at the next moment. The whole process can well adapt to the characteristics of dynamic changes of load data over time.

[0062] The recurrent neural network based on the gated recurrent unit includes an input layer 21, a convolutional layer 22, a pooling layer 23, a fully connected layer 24 and an output layer 25, such as Figure 3 As shown, Figure 3 Schematic diagram of a recurrent neural network based on a gated recurrent unit in one embodiment of the present invention.

[0063] Among them, the convolution layer performs convolution processing on the user load data to obtain filtered data, the pooling layer performs pooling processing on the filtered data to obtain feature mapping data, the fully connected layer performs nonlinear transformation processing on the feature mapping data to obtain the load feature vector, and the output layer processes the load feature vector to obtain load feature data.

[0064] The adaptive estimation and prediction algorithm is used to optimize the model parameters, that is, the weights of the correlation factors in the network and other forms of expression. Its core advantage is that it can dynamically adjust the parameters according to the error between the model prediction results and the actual data.

[0065] For example, if in a certain round of prediction, the predicted load characteristic data has a large error compared with the actual load data, the adaptive algorithm will adjust the network weights corresponding to the correlation factors according to certain update rules based on error feedback, so that the model can reduce the error in the next round of prediction.

[0066] Through continuous iterative optimization, the model can capture the complex relationship between load data and related factors more and more accurately, and ultimately obtain more accurate load characteristic data, thereby improving the accuracy and reliability of load forecasting.

[0067] Another embodiment of the present invention provides a system for predicting the evolution of energy-saving effects of residents. For details, see Figure 2 , Figure 2 The structure diagram of the system for predicting the evolution of energy-saving effects of residents in one embodiment of the present invention is shown, including:

[0068] The acquisition module 11 is used to obtain user preferences, user energy-saving knowledge levels and user education levels in the residential community, as well as the number of household electrical appliances used by users and the data of power transmission lines in the residential community;

[0069] A calculation module 12 is used to calculate the entropy value of the user preference, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user based on the entropy weight method, and obtain the user load data according to the entropy value calculation;

[0070] An analysis module 13 is used to transform the user load data and the transmission line data based on Fourier transform, analyze the correlation characteristics between the user load data and the transmission line data, and obtain a load correlation factor and a transmission line correlation factor;

[0071] The prediction module 14 is used to perform correlation prediction analysis on the user load data based on the load correlation factor and the transmission line correlation factor to obtain load characteristic data, wherein the correlation prediction analysis is configured to use the load correlation factor and the transmission line correlation factor as parameters of a recurrent neural network based on a gated cyclic unit, and use the adaptive estimation prediction algorithm to optimize the parameters to obtain the load characteristic data.

[0072] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0073] This embodiment adopts a multi-dimensional data analysis method to integrate data resources within the residential community, utilizes the potential correlation between user preferences, energy-saving knowledge level, education level, number of household appliances and transmission line data, and accurately quantifies user-related factors through the entropy weight method to generate user load data. Then, the Fourier transform is used to analyze the data correlation to obtain the correlation factor, and finally, the advanced gated recurrent unit neural network and adaptive estimation prediction algorithm are used to optimize the parameters for load prediction.

[0074] This embodiment can not only significantly improve the load forecasting accuracy, but also provide a comprehensive and detailed decision-making basis for power management, rationally plan power distribution, and accurately formulate energy-saving plans.

[0075] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for predicting the evolution of residents' energy-saving effects, characterized in that: include: Obtain user preferences, user energy-saving knowledge and user education level in the residential community, as well as the number of household electrical appliances used by users and data on power transmission lines in the residential community; Calculate the entropy value of the user preference, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user based on the entropy weight method, and obtain the user load data according to the entropy value; Transforming the user load data and the transmission line data based on Fourier transform, respectively analyzing the correlation characteristics between the user load data and the transmission line data to obtain a load correlation factor and a transmission line correlation factor; Based on the load correlation factor and the transmission line correlation factor, the user load data is subjected to correlation prediction analysis to obtain load characteristic data, wherein the correlation prediction analysis is configured to use the load correlation factor and the transmission line correlation factor as parameters of a recurrent neural network based on a gated cyclic unit, and to optimize the parameters using an adaptive estimation prediction algorithm to obtain the load characteristic data.

2. The method for predicting the evolution of residents' energy-saving effects according to claim 1, characterized in that: The gated recurrent unit-based recurrent neural network includes a convolutional layer, a pooling layer, a fully connected layer and an output layer; Among them, the convolution layer performs convolution processing on the user load data to obtain filtered data, the pooling layer performs pooling processing on the filtered data to obtain feature mapping data, the fully connected layer performs nonlinear transformation processing on the feature mapping data to obtain a load feature vector, and the output layer processes the load feature vector to obtain the load feature data.

3. The method for predicting the evolution of residents' energy-saving effects according to claim 1, characterized in that: Before calculating the entropy values ​​of the user preferences, the user's energy-saving knowledge level, the user's education level, and the number of household electrical appliances of the user based on the entropy weight method, the method for predicting the evolution of the resident energy-saving effect further includes: Data preprocessing is performed on the user preferences, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user, and the preprocessing steps include data cleaning, data integration, data transformation and data reduction.

4. The method for predicting the evolution of residents' energy-saving effects according to claim 1, characterized in that: The entropy weight method is used to calculate the entropy value of the user preference, the user's energy-saving knowledge level, the user's education level, and the number of household electrical appliances of the user, and the user load data is obtained according to the entropy value calculation, including: Based on the entropy weight method, the weights corresponding to the user preference, the user's energy-saving knowledge level, the user's education level and the number of household appliances in the user are respectively determined; based on the weights, the entropy value of the user preference, the entropy value of the user's energy-saving knowledge level, the entropy value of the user's education level and the entropy value of the number of household appliances in the user, the user load data is obtained.

5. The method for predicting the evolution of residents' energy-saving effects according to claim 1, characterized in that: The step of analyzing the correlation characteristics between the user load data and the transmission line data to obtain a load correlation factor and a transmission line correlation factor includes: Analyzing the correlation characteristics between the user load data and the transmission line data based on cross-correlation function analysis to obtain the load correlation factor; Based on the frequency domain coherence analysis, the correlation characteristics between the user load data and the transmission line data are analyzed to obtain the transmission line correlation factor.

6. A system for predicting the evolution of residents' energy-saving effects, characterized in that: include: The acquisition module is used to obtain user preferences, user energy-saving knowledge levels, and user education levels in the residential community, as well as the number of household electrical appliances used by users and the data of power transmission lines in the residential community; A calculation module, used to calculate the entropy value of the user preference, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user based on the entropy weight method, and obtain the user load data according to the entropy value calculation; An analysis module, configured to transform the user load data and the transmission line data based on Fourier transform, respectively analyze the correlation characteristics between the user load data and the transmission line data, and obtain a load correlation factor and a transmission line correlation factor; A prediction module is used to perform correlation prediction analysis on the user load data based on the load correlation factor and the transmission line correlation factor to obtain load characteristic data, wherein the correlation prediction analysis is configured to use the load correlation factor and the transmission line correlation factor as parameters of a recurrent neural network based on a gated cyclic unit, and use an adaptive estimation prediction algorithm to optimize the parameters to obtain the load characteristic data.

7. The resident energy-saving effect evolution prediction system according to claim 6, characterized in that: The gated recurrent unit-based recurrent neural network includes a convolutional layer, a pooling layer, a fully connected layer and an output layer; Among them, the convolution layer performs convolution processing on the user load data to obtain filtered data, the pooling layer performs pooling processing on the filtered data to obtain feature mapping data, the fully connected layer performs nonlinear transformation processing on the feature mapping data to obtain a load feature vector, and the output layer processes the load feature vector to obtain the load feature data.

8. The resident energy saving effect evolution prediction system according to claim 6, characterized in that: Before calculating the entropy values ​​of the user preferences, the user's energy-saving knowledge level, the user's education level, and the number of household electrical appliances of the user based on the entropy weight method, the resident energy-saving effect evolution prediction system further includes: The preprocessing module is used to perform data preprocessing on the user preferences, the user's energy-saving knowledge level, the user's education level and the number of household electrical appliances of the user, wherein the preprocessing steps include data cleaning, data integration, data transformation and data reduction.

9. The resident energy-saving effect evolution prediction system according to claim 6, characterized in that: The entropy weight method is used to calculate the entropy value of the user preference, the user's energy-saving knowledge level, the user's education level, and the number of household electrical appliances of the user, and the user load data is obtained according to the entropy value calculation, including: Based on the entropy weight method, the weights corresponding to the user preference, the user's energy-saving knowledge level, the user's education level and the number of household appliances in the user are respectively determined; based on the weights, the entropy value of the user preference, the entropy value of the user's energy-saving knowledge level, the entropy value of the user's education level and the entropy value of the number of household appliances in the user, the user load data is obtained.

10. The resident energy saving effect evolution prediction system according to claim 6, characterized in that: The step of analyzing the correlation characteristics between the user load data and the transmission line data to obtain a load correlation factor and a transmission line correlation factor includes: Analyzing the correlation characteristics between the user load data and the transmission line data based on cross-correlation function analysis to obtain the load correlation factor; Based on the frequency domain coherence analysis, the correlation characteristics between the user load data and the transmission line data are analyzed to obtain the transmission line correlation factor.

Images