Multi-region-oriented wind power, load and electricity price combined prediction method and system

Abstract

The invention relates to a multi-region-oriented wind power, load and electricity price combined prediction method and a multi-region-oriented wind power, load and electricity price combined prediction system. The method comprises the following steps: firstly, collecting wind power, load and electricity price data of a plurality of regions, obtaining related meteorological and geographic position information, and processing the obtained data to form a feature input channel; then, performing frequency decomposition on a Laplacian matrix of the graph, constructing multi-scale graph convolution, and capturing multi-scale information in a feature channel; defining a new loss function and a parameter updating rule to optimize model training; and then, the shared intermediate feature layer integrates the intermediate features of all the target variables together to form a shared feature matrix, inputs the shared feature matrix to the model for training, and finally outputs wind power, load and electricity price target variables at the same time. The method can extract the unique features of each region, captures the interdependence between different regions and target variables, remarkably improves the prediction precision, and is suitable for the scenes of wind power output, load demands, electricity price prediction and the like of multiple regions.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system prediction, and more specifically, to a method and system for joint prediction of wind power, load and electricity price for multiple regions. Background Art

[0002] With the transformation of the global energy structure, the integrated energy system has gradually become an important research direction in the energy field. However, the complexity and uncertainty of the integrated energy system have brought many technical challenges. The integrated energy system covers the production, transmission, storage and consumption of energy. In this process, the prediction of key factors such as wind power, load and electricity price becomes particularly important. Wind power is affected by meteorological and geographical factors, and has obvious volatility and uncertainty; load demand is driven by seasonal changes, climate conditions, social and economic activities and other factors, and it is difficult to accurately predict; electricity prices are affected by the combined effects of market supply and demand, policy adjustments and external factors, and have a high degree of uncertainty. These factors are intertwined, resulting in uncertainty in the integrated energy system, making it difficult for the traditional method of predicting each factor separately to meet actual needs. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art in the poor joint prediction accuracy of wind power, load and electricity price in multiple regions, and to provide a method and system for joint prediction of wind power, load and electricity price in multiple regions, which is suitable for scenarios such as wind power output, load demand and electricity price prediction in multiple regions, and improves the prediction accuracy of the joint prediction of wind power, load and electricity price.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for joint forecasting of wind power, load and electricity price for multiple regions is provided, comprising the following steps: S1. Data collection: Collect wind power, load demand and electricity price data from multiple regions, obtain relevant meteorological data and geographic location information, and pre-process the collected data; S2. Constructing a feature input channel matrix: constructing the processed wind power, load demand, electricity price data, related meteorological data and geographic location information into a feature input channel matrix; and dividing the feature input channel matrix into a training set and a test set; S3. Construct a graph convolutional neural network model based on spatial topological structure: Construct a graph convolutional neural network based on spatial topological structure, and construct a multi-scale graph convolution through Laplace frequency decomposition to capture the multi-scale information in the feature input channel, and obtain the output intermediate feature matrix; S4. Establish a shared intermediate feature layer: Integrate the output intermediate feature matrix of each target variable after multi-scale graph convolution into a shared matrix to generate a joint feature representation, where the target variables include wind power, load demand, and electricity price data; S5. Optimize the model: Construct the loss function by combining the intra-region loss and the cross-region interaction, design the parameter update rules, and introduce the task coordination gradient term to reduce the probability of the loss function falling into the local optimum; S6. Model training: Use the training set to train the graph convolutional neural network model based on spatial topology structure; S7. Prediction: The test set is input into the trained graph convolutional neural network model based on spatial topology structure to make a joint prediction of wind power, load and electricity price in multiple regions and output the predicted value.

[0005] The present invention proposes a method for joint prediction of wind power, load and electricity price for multiple regions. First, wind power, load demand and electricity price data of multiple regions are collected, and relevant meteorological data and geographical location information are obtained. The acquired data are processed to form a feature input channel; then, the Laplace matrix of the graph is frequency decomposed to construct a multi-scale graph convolution to capture the multi-scale information in the feature channel; secondly, a new loss function and parameter update rule are defined to optimize model training; then, the shared intermediate feature layer integrates the intermediate features of all target variables together to form a shared feature matrix, which is input into the model for training, and finally the wind power, load and electricity price target variables are output simultaneously. The present invention can extract the unique characteristics of each region, capture the mutual dependence between different regions and target variables, significantly improve the prediction accuracy, and is suitable for scenarios such as wind power output, load demand and electricity price prediction in multiple regions, and has extremely high practical application value and promotion potential.

[0006] Preferably, in step S1, the wind power sequence, load sequence, electricity price sequence, wind speed sequence and wind direction sequence are processed by min-max normalization to obtain the processed power sequence P, load sequence L, electricity price sequence EP and wind speed sequence WS, wherein the wind direction sequence is processed by sine and cosine to obtain the wind direction sine SWD and wind direction cosine CWD.

[0007] Preferably, the step S2 includes: S21. After processing the data collected in step S1, a feature input channel matrix of R regions is obtained. The feature input channel matrix is ​​expressed as follows:

[0008]

[0009]

[0010] In the formula, H ws represents the wind speed input channel matrix, H SWD represents the wind direction sinusoidal input channel matrix, H CWD represents the wind direction cosine input channel matrix, H P represents the wind power input channel matrix, H L represents the load demand input channel matrix, H EP represents the electricity price input channel matrix; , , , , and Respectively represent r In the area tn Wind power, wind speed, wind direction sine, wind direction cosine, load demand and electricity price at the moment; S22. Construct a complex weight matrix based on the collected geographic location information , used to capture distance and direction information at the same time, the specific expression is:

[0011] In the formula, For Node i To Node j The direction angle, is the standard deviation parameter of the Gaussian kernel function, is the threshold. When the weight of the distance mapping between regions is less than the threshold, the two relationships are considered small and are ignored. r Indicates r Regions, r' Indicates r' Regions, drr' Indicates the distance between regions; S23. Divide the data processed in step S1 into a training set and a test set.

[0012] Preferably, step S3 includes: S31. Establish the graph convolution formula, expressed as follows:

[0013] In the formula, is the graph convolution kernel function, is the graph convolution operation, is the Laplace matrix of the graph, and its normalized form and eigenvalue decomposition form are and , is a diagonal matrix, U is the Laplace matrix L The normalized feature vector, is the complex weight matrix, is the identity matrix, The degree matrix is ​​a diagonal matrix composed of node degrees composition; S32. Laplacian matrix of a graph L Perform eigenvalue decomposition and project node features into different frequency ranges to capture the multi-scale characteristics of node information:

[0014] In the formula, is a diagonal matrix containing L The eigenvalue of ; S33. For step S32 L The eigenvalues ​​of are clustered using a clustering algorithm to cluster the eigenvalues ​​into three clusters. The eigenvalue range of each cluster is used as a frequency range to form three intervals, which correspond to low frequency, medium frequency and high frequency respectively. S34. Design a corresponding graph convolution kernel for each frequency band , and applied to the feature input channel matrix on each channel to obtain the output intermediate feature matrix. The specific formula is:

[0015]

[0016]

[0017] In the formula, Indicates l The feature input channel matrix of the layer, which contains the multi-dimensional input features corresponding to each target variable; Indicates that the wind power target variable is l +1 layer output intermediate feature matrix, wind power target variables include wind speed, wind direction, and wind power; Indicates that the load target variable is l +1 layer output intermediate feature matrix; Indicates that the target variable of electricity price is l The output intermediate feature matrix of layer +1.

[0018] Preferably, the step S5 comprises: S51. The loss function combines the intra-region loss and the cross-region interaction loss to achieve joint optimization. The formula is as follows:

[0019]

[0020]

[0021] In the formula, is the weight, Represent the loss within the region and the interaction loss between regions, respectively. For the r Region No. i variables t The predicted value at time, No. r Region No. i variables t The true value of the moment; is a constant close to 0, used to prevent the zero value problem in the logarithmic function; Used to control the mutual influence between regions. The distance between regions, It is a distance-sensitive value, used to adjust the sensitivity of the distance-weighted term; S52. The parameter updating and optimization process includes:

[0022]

[0023] In the formula, is the update amount of the model parameters, and are the regularization gradient term and the coordination gradient term, respectively. It is i and j The Euclidean distance between the intermediate features of the targets; η represents the learning rate; λ represents the coordination gradient term coefficient, β Represents the regularization gradient term coefficient.

[0024] Preferably, step S4 includes: integrating the output intermediate feature matrices generated by each frequency band for wind power, load and electricity price, and obtaining the output feature matrices corresponding to the target variables respectively. , the three output feature matrices are passed through Concat Splicing to get: .

[0025] Preferably, in step S7, the test set is input into the trained graph convolutional neural network model based on spatial topology structure to obtain the predicted value :

[0026] In the formula, Represents the predicted value of wind power output using the test set; Represents the predicted value of the load output using the test set; Represents the predicted value of the electricity price output using the test set.

[0027] The present invention also provides a wind power, load and electricity price joint forecasting system for multiple regions, comprising: Data acquisition module: used to collect wind power, load demand and electricity price data in multiple regions, obtain relevant meteorological data and geographical location information, and pre-process the collected data; Data processing module: used to construct a feature input channel matrix, which is composed of the processed wind power, load demand, electricity price data, related meteorological data and geographic location information; and the feature input channel matrix is ​​divided into a training set and a test set; Graph convolutional neural network module: used to build a graph convolutional neural network model based on spatial topological structure, and construct multi-scale graph convolution through Laplace frequency decomposition to capture multi-scale information in the feature input channel, and obtain the output intermediate feature matrix; Shared intermediate feature layer module: used to establish a shared intermediate feature layer, integrate the output intermediate feature matrix of each target variable after multi-scale graph convolution into the shared matrix, and generate a joint feature representation, where the target variables include wind power, load demand and electricity price data; Model optimization module: used to construct the loss function by combining the intra-region loss and cross-region interaction, design parameter update rules, and introduce task coordination gradient terms to reduce the probability of the loss function falling into the local optimum; Model training module: used to train the graph convolutional neural network model based on spatial topology structure using the training set; Prediction output module: used to input the test set into the trained graph convolutional neural network model based on spatial topology structure, conduct joint prediction of wind power, load and electricity price in multiple regions, and output the predicted value.

[0028] The present invention also provides a computer device, including a processor, a memory, and a computer program stored in the memory, wherein the computer program is configured to be executed by the processor, and when the computer program is executed by the processor, the above-mentioned multi-regional wind power, load and electricity price joint prediction method is implemented.

[0029] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned multi-regional wind power, load and electricity price joint forecasting method is implemented.

[0030] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a multi-regional wind power, load and electricity price joint forecasting method and system, which can extract the unique characteristics of each region and capture the interdependence between different regions and target variables, significantly improving the forecasting accuracy. It is suitable for scenarios such as multi-regional wind power output, load demand and electricity price forecasting, and has extremely high practical application value and promotion potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a schematic diagram of the method flow in Example 1; Figures 2 to 4 This is a schematic diagram of the joint prediction effect of wind power, load and electricity price for area 1 in Example 6; Figures 5 to 7 The figure is a schematic diagram of the joint prediction effect of wind power, load and electricity price for area 2 in Example 6; Figures 8 to 10 This is a schematic diagram of the joint prediction effect of wind power, load and electricity price for area 3 in Example 6. DETAILED DESCRIPTION

[0032] The present invention is further described below in conjunction with specific implementation methods. The accompanying drawings are only for illustrative purposes and are only schematic diagrams, not actual drawings, and should not be construed as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the size of the actual product. It is understandable to those skilled in the art that some well-known structures and their descriptions in the accompanying drawings may be omitted.

[0033] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, it is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limitations on the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0034] Embodiment 1 This embodiment is a first embodiment of a method for joint prediction of wind power, load and electricity price for multiple regions, including the following steps: S1. Data collection: Collect wind power, load demand and electricity price data from multiple regions, obtain relevant meteorological data and geographic location information, and pre-process the collected data; S2. Constructing a feature input channel matrix: constructing the processed wind power, load demand, electricity price data, related meteorological data and geographic location information into a feature input channel matrix; and dividing the feature input channel matrix into a training set and a test set; S3. Construct a graph convolutional neural network model based on spatial topological structure: Construct a graph convolutional neural network based on spatial topological structure, and construct a multi-scale graph convolution through Laplace frequency decomposition to capture the multi-scale information in the feature input channel, and obtain the output intermediate feature matrix; S4. Establish a shared intermediate feature layer: Integrate the output intermediate feature matrix of each target variable after multi-scale graph convolution into a shared matrix to generate a joint feature representation, where the target variables include wind power, load demand, and electricity price data; S5. Optimize the model: Construct the loss function by combining the intra-region loss and the cross-region interaction, design the parameter update rules, and introduce the task coordination gradient term to reduce the probability of the loss function falling into the local optimum; S6. Model training: Use the training set to train the graph convolutional neural network model based on spatial topology structure; S7. Prediction: The test set is input into the trained graph convolutional neural network model based on spatial topology structure to make a joint prediction of wind power, load and electricity price in multiple regions and output the predicted value.

[0035] The present embodiment proposes a method for joint prediction of wind power, load and electricity price for multiple regions. First, wind power, load demand and electricity price data of multiple regions are collected, and relevant meteorological data and geographical location information are obtained. The acquired data are processed to form a feature input channel; then, by frequency decomposing the Laplace matrix of the graph, a multi-scale graph convolution is constructed to capture the multi-scale information in the feature channel; secondly, a new loss function and parameter update rule are defined to optimize model training; then, the shared intermediate feature layer integrates the intermediate features of all target variables together to form a shared feature matrix, and inputs it into the model for training, and finally outputs the wind power, load and electricity price target variables simultaneously. The present invention can extract the unique characteristics of each region, capture the mutual dependence between different regions and target variables, significantly improve the prediction accuracy, and is suitable for scenarios such as wind power output, load demand and electricity price prediction in multiple regions, and has extremely high practical application value and promotion potential.

[0036] Embodiment 2 This embodiment is a second embodiment of a method for joint forecasting of wind power, load and electricity price for multiple regions. This embodiment is similar to the embodiment, except that, in this embodiment, preprocessing of the collected data includes: applying min-max normalization to the wind power sequence, load sequence, electricity price sequence, wind speed sequence and wind direction sequence to obtain the processed power sequence P, load sequence L, electricity price sequence EP and wind speed sequence WS, wherein the wind direction sequence is processed using sine and cosine to obtain wind direction sine SWD and wind direction cosine CWD.

[0037] Embodiment 3 This embodiment is a third embodiment of a method for joint prediction of wind power, load and electricity price for multiple regions. This embodiment is similar to the embodiment, except that in this embodiment, detailed steps for constructing a feature input channel are proposed, specifically including: S21. After processing the data collected in step S1, a feature input channel matrix of R regions is obtained. The feature input channel matrix is ​​expressed as follows:

[0038]

[0039]

[0040] In the formula, H ws represents the wind speed input channel matrix, H SWD represents the wind direction sinusoidal input channel matrix, H CWD represents the wind direction cosine input channel matrix, H P represents the wind power input channel matrix, H L represents the load demand input channel matrix, H EP represents the electricity price input channel matrix; , , , , and Respectively represent r In the area tn Wind power, wind speed, wind direction sine, wind direction cosine, load demand and electricity price at the moment; S22. Construct a complex weight matrix based on the collected geographic location information , used to capture distance and direction information at the same time, the specific expression is:

[0041] In the formula, For Node i To Node j The direction angle, is the standard deviation parameter of the Gaussian kernel function, is the threshold. When the weight of the distance mapping between regions is less than the threshold, the two relationships are considered small and are ignored. r Indicates r Regions, r' Indicates r' Regions, drr' Indicates the distance between regions; S23. Divide the data processed in step S1 into a training set and a test set.

[0042] Embodiment 4 This embodiment is a fourth embodiment of a method for joint prediction of wind power, load and electricity price for multiple regions. This embodiment is similar to the embodiment, except that in this embodiment, detailed steps for constructing a graph convolutional neural network model based on a spatial topological structure are proposed, specifically including: S31. Establish the graph convolution formula, expressed as follows:

[0043] In the formula, is the graph convolution kernel function, is the graph convolution operation, is the Laplace matrix of the graph, and its normalized form and eigenvalue decomposition form are and , is a diagonal matrix, U is the Laplace matrix L The normalized feature vector, is the complex weight matrix, is the identity matrix, The degree matrix is ​​a diagonal matrix composed of node degrees composition; S32. Laplacian matrix of graph L Perform eigenvalue decomposition and project node features into different frequency ranges to capture the multi-scale characteristics of node information:

[0044] In the formula, is a diagonal matrix containing L The eigenvalue of ; S33. For step S32L The eigenvalues ​​of are clustered using a clustering algorithm to cluster the eigenvalues ​​into three clusters. The eigenvalue range of each cluster is used as a frequency range to form three intervals, which correspond to low frequency, medium frequency and high frequency respectively. S34. Design a corresponding graph convolution kernel for each frequency band , and applied to the feature input channel matrix on each channel to obtain the output intermediate feature matrix. The specific formula is:

[0045]

[0046]

[0047] In the formula, Indicates l The feature input channel matrix of the layer, which contains the multi-dimensional input features corresponding to each target variable; Indicates that the wind power target variable is l +1 layer output intermediate feature matrix, wind power target variables include wind speed, wind direction, and wind power; Indicates that the load target variable is l +1 layer output intermediate feature matrix; Indicates that the target variable of electricity price is l The output intermediate feature matrix of layer +1.

[0048] In step S4, for wind power, load and electricity price, the output intermediate feature matrix generated by each frequency band is integrated to obtain the output feature matrix corresponding to the target variable , the three output feature matrices are passed through Concat Splicing to get: . Input the test set into the trained graph convolutional neural network model based on spatial topology structure to obtain the predicted value :

[0049] In the formula, Represents the predicted value of wind power output using the test set; Represents the predicted value of the load output using the test set; Represents the predicted value of the electricity price output using the test set.

[0050] In this embodiment, In fact, it corresponds to the feature input channel matrix in the third embodiment. For example, there are four factors affecting Wind (wind power, wind speed, wind direction sine, wind direction cosine), which means there are four feature input channel matrices ( Hws , H SWD , H CWD as well as H P ),So There are 4; similarly, Load and Price have only one. The output intermediate feature matrix actually means: because the present invention has three intervals, there will be three k, then for Wind, there will be 3*4=12 output intermediate feature matrices, and then the 12 output intermediate feature matrices about Wind are integrated to obtain the output feature matrix H wind Similarly, for Load and Price, there will be three output intermediate feature matrices, which are then integrated to obtain H load and H price , and finally spliced ​​together to output the wind power, load and electricity price target variables simultaneously, expressed as .

[0051] Embodiment 5 This embodiment is a fifth embodiment of a method for joint prediction of wind power, load and electricity price for multiple regions. This embodiment is similar to the embodiment, except that in this embodiment, detailed steps for optimizing the model are proposed, specifically including: S51. The loss function combines the intra-region loss and the cross-region interaction loss to achieve joint optimization. The formula is as follows:

[0052]

[0053]

[0054] In the formula, is the weight, Represent the loss within the region and the interaction loss between regions, respectively. For the r Region No. i variables t The predicted value at time, No. r Region No. i variables t The true value of the moment; is a constant close to 0, used to prevent the zero value problem in the logarithmic function; Used to control the mutual influence between regions. The distance between regions, It is a distance-sensitive value, used to adjust the sensitivity of the distance-weighted term; S52. The parameter updating and optimization process includes:

[0055]

[0056] In the formula, is the update amount of the model parameters, and are the regularization gradient term and the coordination gradient term, respectively. It is i and j The Euclidean distance between the intermediate features of the targets; η represents the learning rate; λ represents the coordination gradient term coefficient, β Represents the regularization gradient term coefficient.

[0057] Embodiment 6 In order to verify the effectiveness of the multi-regional wind power, load and electricity price joint prediction method proposed in the present invention, step S1 first obtains the wind power sequence, load sequence, electricity price sequence, wind speed sequence, wind direction sequence and geographic location information of 30 regions in a certain area from 2020 / 1 / 1 / 00:00 to 2020 / 12 / 31 / 23:00, and generates a geographic location matrix containing direction information through the geographic location information. Then, a graph convolutional neural network based on the spatial topological structure is constructed, and the multi-scale graph convolution of Laplace frequency decomposition is used to capture the multi-scale information in the feature channel, and a shared intermediate feature layer is designed. Finally, the error is coordinated through the shared gradient and loss function to jointly predict wind power-load-electricity price. Due to the large number of regions, this case takes the prediction results of 3 regions for verification, among which Figures 2 to 10 The figure shows the prediction effect of the wind power, load and electricity price joint prediction method and the separate prediction method applied to the graph convolutional neural network. It can be clearly seen that the joint prediction method of the present invention can effectively improve the prediction accuracy of wind power-load-electricity price.

[0058] Embodiment 7 This embodiment is an embodiment of a wind power, load and electricity price joint prediction system for multiple regions. Based on the prediction methods provided in Embodiments 1 to 5, a wind power, load and electricity price joint prediction system for multiple regions is provided, including: Data acquisition module: used to collect wind power, load demand and electricity price data in multiple regions, obtain relevant meteorological data and geographical location information, and pre-process the collected data; Data processing module: used to construct feature input channels and geographic location matrices, and construct feature input channels from processed wind power, load demand, electricity price data, related meteorological data, and geographic location information; and divide the feature input channels into training sets and test sets; Graph convolutional neural network module: used to build a graph convolutional neural network model based on spatial topological structure, and construct multi-scale graph convolution through Laplace frequency decomposition to capture multi-scale information in the feature input channel, and obtain the output intermediate feature matrix; Shared intermediate feature layer module: used to establish a shared intermediate feature layer, integrate the output intermediate feature matrix of each target variable after multi-scale graph convolution into the shared matrix, and generate a joint feature representation, where the target variables include wind power, load demand and electricity price data; Model optimization module: used to construct the loss function by combining the intra-region loss and cross-region interaction, design parameter update rules, and introduce task coordination gradient terms to reduce the probability of the loss function falling into the local optimum; Model training module: used to train the graph convolutional neural network model based on spatial topology structure using the training set; Prediction output module: used to input the test set into the trained graph convolutional neural network model based on spatial topology structure, conduct joint prediction of wind power, load and electricity price in multiple regions, and output the predicted value.

[0059] Embodiment 8 This embodiment is a computer device, including a processor, a memory, and a computer program stored in the memory, wherein the computer program is configured to be executed by the processor, and when the computer program is executed by the processor, the multi-regional wind power, load and electricity price joint forecasting method described in any one of the above embodiments 1 to 5 is implemented.

[0060] Embodiment 9 This embodiment is a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the multi-regional wind power, load and electricity price joint forecasting method described in any one of the above embodiments 1 to 5 is implemented.

[0061] In the specific contents of the above-mentioned specific implementation methods, the various technical features can be combined in any non-contradictory manner. In order to make the description concise, not all possible combinations of the above-mentioned technical features are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0062] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for joint forecasting of wind power, load and electricity price for multiple regions, characterized in that: The following steps are involved: S1. Data collection: Collect wind power, load demand and electricity price data from multiple regions, obtain relevant meteorological data and geographic location information, and pre-process the collected data; S2. Constructing a feature input channel matrix: constructing the processed wind power, load demand, electricity price data, related meteorological data and geographic location information into a feature input channel matrix; and dividing the feature input channel matrix into a training set and a test set; S3. Construct a graph convolutional neural network model based on spatial topological structure: Construct a graph convolutional neural network based on spatial topological structure, and construct a multi-scale graph convolution through Laplace frequency decomposition to capture the multi-scale information in the feature input channel, and obtain the output intermediate feature matrix; S4. Optimize the model: Construct the loss function by combining the intra-region loss and the cross-region interaction, design the parameter update rules, and introduce the task coordination gradient term to reduce the probability of the loss function falling into the local optimum; S5. Establish a shared intermediate feature layer: Integrate the output intermediate feature matrix of each target variable after multi-scale graph convolution into a shared matrix to generate a joint feature representation, where the target variables include wind power, load demand, and electricity price data; S6. Model training: Use the training set to train the graph convolutional neural network model based on spatial topology structure; S7. Prediction: The test set is input into the trained graph convolutional neural network model based on spatial topology structure to make a joint prediction of wind power, load and electricity price in multiple regions and output the predicted value.

2. The multi-regional wind power, load and electricity price joint forecasting method according to claim 1 is characterized in that: In step S1, the wind power sequence, load sequence, electricity price sequence, wind speed sequence and wind direction sequence are normalized by min-max to obtain the processed power sequence P, load sequence L, electricity price sequence EP and wind speed sequence WS, among which the wind direction sequence is processed by sine and cosine to obtain wind direction sine SWD and wind direction cosine CWD.

3. The multi-regional wind power, load and electricity price joint forecasting method according to claim 1 is characterized in that: The step S2 includes: S21. After processing the data collected in step S1, a feature input channel matrix of R regions is obtained. The feature input channel matrix is ​​expressed as follows: In the formula, H ws represents the wind speed input channel matrix, H SWD represents the wind direction sinusoidal input channel matrix, H CWD represents the wind direction cosine input channel matrix, H P represents the wind power input channel matrix, H L represents the load demand input channel matrix, H EP represents the electricity price input channel matrix; , , , , and Respectively represent r In the area tn Wind power, wind speed, wind direction sine, wind direction cosine, load demand and electricity price at the moment; S22. Construct a complex weight matrix based on the collected geographic location information , used to capture distance and direction information at the same time, the specific expression is: In the formula, For Node i To Node j The direction angle, is the standard deviation parameter of the Gaussian kernel function, is the threshold. When the weight of the distance mapping between regions is less than the threshold, the two relationships are considered small and are ignored. r Indicates r Regions, r' Indicates r' Regions, drr' Indicates the distance between regions; S23. Divide the data processed in step S1 into a training set and a test set.

4. The method for joint forecasting of wind power, load and electricity price for multiple regions according to claim 3 is characterized in that: The step S3 comprises: S31. Establish the graph convolution formula, expressed as follows: In the formula, is the graph convolution kernel function, is the graph convolution operation, is the Laplace matrix of the graph, and its normalized form and eigenvalue decomposition form are and , is a diagonal matrix, U is the Laplace matrix L The normalized feature vector, is the complex weight matrix, is the identity matrix, The degree matrix is ​​a diagonal matrix composed of node degrees composition; S32. Laplacian matrix of graph L Perform eigenvalue decomposition and project node features into different frequency ranges to capture the multi-scale characteristics of node information: In the formula, is a diagonal matrix containing L The eigenvalue of ; S33. For step S32 L The eigenvalues ​​of are clustered using a clustering algorithm to cluster the eigenvalues ​​into three clusters. The eigenvalue range of each cluster is used as a frequency range to form three intervals, which correspond to low frequency, medium frequency and high frequency respectively. S34. Design a corresponding graph convolution kernel for each frequency band , and applied to the feature input channel matrix on each channel to obtain the output intermediate feature matrix. The specific formula is: In the formula, Indicates l The feature input channel matrix of the layer, which contains the multi-dimensional input features corresponding to each target variable; Indicates that the wind power target variable is l +1 layer output intermediate feature matrix, wind power target variables include wind speed, wind direction, and wind power; Indicates that the load target variable is l +1 layer output intermediate feature matrix; Indicates that the target variable of electricity price is l The output intermediate feature matrix of layer +1.

5. The method for joint forecasting of wind power, load and electricity price for multiple regions according to claim 4 is characterized in that: The step S4 comprises: S41. The loss function combines the intra-region loss and the cross-region interaction loss to achieve joint optimization. The formula is as follows: In the formula, is the weight, Represent the loss within the region and the interaction loss between regions, respectively. For the r Region No. i variables t The predicted value at time, No. r Region No. i variables t The true value of the moment; is a constant close to 0, used to prevent the zero value problem in the logarithmic function; Used to control the mutual influence between regions. The distance between regions, It is a distance-sensitive value, used to adjust the sensitivity of the distance-weighted term; S42. The parameter updating and optimization process includes: In the formula, is the update amount of the model parameters, and are the regularization gradient term and the coordination gradient term, respectively. It is i and j The Euclidean distance between the intermediate features of the targets; η represents the learning rate; λ represents the coordination gradient term coefficient, β Represents the regularization gradient term coefficient.

6. The multi-regional wind power, load and electricity price joint forecasting method according to claim 4 is characterized in that: The step S5 includes: integrating the output intermediate feature matrices generated by each frequency band for wind power, load and electricity price, and obtaining the output feature matrices corresponding to the target variables respectively. , the three output feature matrices are passed through Concat Splicing to get: .

7. The multi-regional wind power, load and electricity price joint forecasting method according to claim 5 is characterized in that: In step S7, the test set is input into the trained graph convolutional neural network model based on spatial topology structure to obtain the predicted value : In the formula, Represents the predicted value of wind power output using the test set; Represents the predicted value of the load output using the test set; Represents the predicted value of the electricity price output using the test set.

8. A wind power, load and electricity price joint forecasting system for multiple regions, characterized in that: include: Data acquisition module: used to collect wind power, load demand and electricity price data in multiple regions, obtain relevant meteorological data and geographical location information, and pre-process the collected data; Data processing module: used to construct a feature input channel matrix, which is composed of the processed wind power, load demand, electricity price data, related meteorological data and geographic location information; And divide the feature input channel matrix into training set and test set; Graph convolutional neural network module: used to build a graph convolutional neural network model based on spatial topological structure, and construct multi-scale graph convolution through Laplace frequency decomposition to capture multi-scale information in the feature input channel, and obtain the output intermediate feature matrix; Shared intermediate feature layer module: used to establish a shared intermediate feature layer, integrate the output intermediate feature matrix of each target variable after multi-scale graph convolution into the shared matrix, and generate a joint feature representation, where the target variables include wind power, load demand and electricity price data; Model optimization module: used to construct the loss function by combining the intra-region loss and cross-region interaction, design parameter update rules, and introduce task coordination gradient terms to reduce the probability of the loss function falling into the local optimum; Model training module: used to train the graph convolutional neural network model based on spatial topology structure using the training set; Prediction output module: used to input the test set into the trained graph convolutional neural network model based on spatial topology structure, conduct joint prediction of wind power, load and electricity price in multiple regions, and output the predicted value.

9. A computer device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory, wherein the computer program is configured to be executed by the processor, and when the computer program is executed by the processor, the method for joint forecasting of wind power, load and electricity price for multiple regions as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-regional wind power, load and electricity price joint forecasting method as described in any one of claims 1 to 7 is implemented.

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