Offshore wind power prediction method and system with multilevel space-time perception

Abstract

The invention provides an offshore wind power prediction method and system with multi-level space-time perception, and relates to the technical field of offshore wind power prediction.The method comprises the steps that firstly, the space relation of fans is determined, two-dimensional static plane features are constructed, and multi-dimensional time sequence wind power meteorological features under the multi-level altitude are obtained; carrying out dimension reduction on the multi-dimensional time sequence wind power meteorological characteristics under each level of altitude, distributing weights for the multi-dimensional time sequence wind power meteorological characteristics after dimension reduction, obtaining a characteristic matrix after dimension reduction of each level of altitude, constructing local graph characteristics based on the characteristic matrix, and obtaining a local graph; and combining the local graph features and the two-dimensional static plane features into global space graph features, and finally, performing wind power prediction by using a linear regression prediction model. According to the method, the geographic position information and the meteorological information of the fans of the wind power plant are fully utilized, multiple meteorological factors at different altitudes are considered at the same time, prediction errors caused by wind power generation uncertainty are effectively reduced, and the accuracy of offshore wind power prediction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind power prediction, and more specifically, to an offshore wind power prediction method and system with multi-level spatiotemporal perception. Background Art

[0002] As an important direction for the development of clean energy, offshore wind power is developing rapidly and becoming an important part of the power system due to its advantages of abundant and stable wind resources, high wind speed, and little impact on terrain, as well as the characteristics of wind energy itself being clean, abundant, and environmentally friendly. Compared with onshore wind farms, offshore wind farms have a higher wind energy utilization rate, with an average annual utilization hour of about 500 hours higher than onshore wind power. However, the output of offshore wind power is significantly affected by natural conditions, especially changes in wind speed, wind direction, and weather conditions. This uncertainty makes offshore wind power forecasting challenging, and also poses a challenge to the stable operation of the power grid and power dispatching, directly affecting the economic benefits and operational efficiency of wind farms. Therefore, accurate offshore wind power forecasting is of great significance to the safe and stable operation of the power system.

[0003] In the prior art, a method for offshore wind power prediction based on RCNN and meteorological time series features is proposed. First, the historical numerical weather forecast and historical output power data of the wind farm are extracted, and the historical data are divided into a training set and a validation set. RCNN is constructed as a preliminary prediction model, and the training set is used to train RCNN to optimize the weights. Then, the trained RCNN is used to predict the wind power in the validation set, and the RCNN prediction result is subtracted from the historical wind power in the validation set to obtain the historical prediction power error. Then, the Xgboost algorithm is used to calculate the correlation between the numerical weather forecast in the validation set and the historical prediction power error, and the strongly correlated numerical weather forecast is selected as the meteorological time series feature. Then, a bidirectional recurrent neural network is constructed, and the network is trained using meteorological time series features and historical prediction power errors. Finally, the prediction results and errors of RCNN and the bidirectional recurrent neural network are superimposed to obtain the offshore wind power prediction, which ensures timeliness while improving the overall prediction performance of the model. However, this method only relies on the historical power series of offshore wind power for prediction, without considering the geographical location information of wind turbines and the impact of complex and changeable meteorological factors at sea on offshore wind power generation. As a result, the final trained wind power prediction model has poor generalization and low accuracy in predicting offshore wind power under complex conditions.

[0004] In practical applications, the installation location of wind turbines in a certain area of ​​the wind farm will have a mutual impact on the power generation of surrounding wind turbines. Therefore, the geographical location information of the wind turbine itself is also an important feature that cannot be ignored. Offshore meteorological factors are complex and changeable, especially at high altitudes, where there are more significant changes. Considering multiple meteorological factors at different altitudes can help improve prediction accuracy. Summary of the invention

[0005] In order to solve the problem of low prediction accuracy of the current offshore wind power prediction method, the present invention proposes an offshore wind power prediction method and system with multi-level spatiotemporal perception, which reduces the prediction error caused by the uncertainty of wind power generation and improves the prediction accuracy of wind power in offshore wind farms.

[0006] In order to achieve the above technical effects, the technical solution of the present invention is as follows: In the first aspect, the present application proposes a method for offshore wind power prediction with multi-level spatiotemporal perception, comprising the following steps: S1: Consider each wind turbine in the offshore wind farm in the region as a node, determine the spatial relationship of the wind turbines based on the geographical location information of each wind turbine and the distance between the wind turbines, and construct a two-dimensional static plane feature; S2: Obtain multi-dimensional time series wind power meteorological characteristics at multiple levels of altitude, and reduce the dimension of the multi-dimensional time series wind power meteorological characteristics at each level of altitude; S3: Assign weights to the multi-dimensional time-series wind power meteorological features after dimensionality reduction, obtain the feature matrix after dimensionality reduction at each level of altitude, construct local graph features based on the feature matrix after dimensionality reduction, and merge the local graph features and the two-dimensional static plane features into a global space graph feature; S4: Input the global spatial graph features into the graph convolutional neural network model to generate the final latent feature vector; S5: The final potential feature vector is used to train the constructed linear regression prediction model, and the trained linear regression prediction model is used for wind power prediction of offshore wind farms.

[0007] Preferably, the geographical location information of each wind turbine includes: the longitude, latitude and altitude of each wind turbine; the geographical information of the wind turbines in the offshore wind farm in the region is represented as ,in P Indicates the number of fans, M Indicates the longitude, latitude and altitude of the wind turbine; A collection representing geographic information of wind turbines; The process of constructing a 2D static plane feature is: S11: Calculate wind turbines in offshore wind farms in the region i With fan j The normalized distance ND between them is calculated as follows:

[0008]

[0009]

[0010]

[0011]

[0012] in, Indicates fan i The longitude, Indicates fan i The latitude, Indicates fan i The altitude, have () is the intermediate operation function. For any x , , R represents the radius of the earth, Indicates fan i With fan j The distance between ; S12: The fan i Wind power Expressed as I , fan j Wind power Expressed as J , calculate the fan i and fan j The normalized mutual information NMI between is expressed as:

[0013]

[0014]

[0015]

[0016] in, It is a fan i and fan j The joint distribution of It is a fan i The marginal distribution of Indicates fan i The information entropy of Indicates fan j The information entropy of Indicates fan i and fan j The joint information entropy of Indicates fan i and fan j The mutual information of S13: Constructing 2D static plane features based on normalized distance and normalized mutual information between wind turbine pairs .

[0017] Preferably, meteorological data in the area is collected, and the meteorological data includes: wind speed V ,wind direction D ,temperature T ,humidity H and air pressure P a , the meteorological data changes with time, and the meteorological data is preprocessed; The altitude of offshore wind farms in the region L Divide into n levels, expressed as: , No. i Levels L i The interval of altitude z is fixed and satisfies: ,in, Indicates i Levels L i The lower limit of the altitude range is Indicates i Levels L i The upper limit of the altitude range; At each altitude z, the meteorological data features M ( z ) is expressed as a multidimensional vector, and the expression is: ; No. i Levels L i Meteorological data characteristics M ( L i ) is expressed as: .

[0018] Preferably, the process of reducing the dimension of the multi-dimensional time series wind power meteorological characteristics at each level of altitude is as follows: S21: Set a fixed sliding window Q and select Q consecutive sampling points from the time series to form a subsequence ,in, t is the current time point, x represents any one of the meteorological variables, including wind speed V ,wind direction D ,temperature T ,humidity H and air pressure P a ; S22: In each subsequence q A time series feature vector is constructed for each meteorological variable. :

[0019] Each row represents the time series characteristics of a single meteorological variable at a single node at each level of altitude. represents the time series mean of the acquisition point q, represents the time series variance of the acquisition point q, represents the time autocorrelation coefficient of the acquisition point q, Represents the spatial autocorrelation of the collection point q; the nodes within each level of altitude u All meteorological feature vectors are:

[0020] Integrate all node features to obtain the feature matrix of all nodes at each level of altitude f :

[0021] S23: Feature matrix f Perform principal component analysis to obtain a new feature matrix after dimensionality reduction ,right Calculate the covariance matrix C and perform eigenvalue decomposition on it to obtain the eigenvalues ​​and corresponding eigenvectors and , select the eigenvectors corresponding to the first k largest eigenvalues ​​to form the projection matrix W, and obtain the feature matrix after the dimensionality reduction of each level The expression is:

[0022] in, The size is n * k , 2<k<m, n is the number of meteorological variables, m is the total number of eigenvalues ​​after decomposition.

[0023] Preferably, , , and The calculation expressions are:

[0024]

[0025]

[0026]

[0027] in, For fans i Location and fan j The spatial weights between the locations.

[0028] Preferably, the process of step S3 is: S31: Using dynamic feature selection, set the weight allocation strategy so that the feature matrix weight of the high altitude layer is larger and the sum of all weights is 1; the weight allocation strategy of the single altitude layer is:

[0029] in, z For the L i The height of the altitude layer, It is an adjustable parameter used to control the sensitivity of weights to different levels of altitude, and to achieve differential sensitivity control for high altitude and low altitude. is a growth index used to amplify the weight of high altitude layers. is the smoothing control coefficient; Through weighted summation, the feature matrix after all the altitudes of each level are reduced in dimension Merge into a local feature matrix :

[0030] in, N is the number of single altitude layers, For the The weight of each altitude layer; S32: Use multi-layer perceptron MLP to transform two-dimensional static plane features The dimension expansion makes the two-dimensional static plane feature The dimension and local feature matrix The dimension of the two-dimensional static plane feature matrix after the dimension expansion is the same as and the local feature matrix Vertical splicing into global spatial graph features F .

[0031] Preferably, a multi-layer perceptron MLP is used to transform the two-dimensional static plane features The process of dimensional expansion is: For 2D static plane features Perform pre-processing; The preprocessed two-dimensional static plane features Input to the trained multi-layer perceptron MLP; Calculate the one-dimensional vector output by the multi-layer perceptron output layer through the forward propagation algorithm of the multi-layer perceptron MLP; Convert the one-dimensional vector of the output layer into the local feature matrix Matrix form of the same dimensions.

[0032] Preferably, the process of generating the final potential feature vector in step S4 is: S41: Based on the global spatial graph features F The cosine similarity between the nodes of the wind machine is used to construct the adjacency matrix A h , the expression is: ; S42: In a graph convolutional neural network model with several layers, global spatial graph features F and the adjacency matrix A h Perform a forward convolution operation; the convolution operation of any layer in the graph convolutional neural network model satisfies:

[0033] Where E is the degree matrix, is the weight matrix of the first graph convolutional neural network model layer, is the feature matrix after the first layer of graph convolution operation, It is p A weight matrix of a layer graph convolution; after performing a multi-layer graph convolution operation, extracting the output features of the last layer of the graph convolution neural network model, wherein the output features include the final potential feature vector of the offshore wind farm wind turbine.

[0034] Preferably, the process of step S5 is: The potential feature vector and the corresponding historical wind power data are used as training data to train the linear regression prediction model through forward propagation; The prediction error is calculated using the loss function, and the model parameter weights of the linear regression prediction model are updated using the optimizer. After the loss function converges or reaches the predetermined number of training rounds, the training is terminated to obtain a trained linear regression prediction model. Execute steps S1 to S4 to obtain a new potential feature vector, input the new potential feature vector into the trained linear regression prediction model, and output the wind power of the offshore wind farm.

[0035] In the second aspect, the present application proposes an offshore wind power prediction system with multi-level spatiotemporal perception, characterized in that the system is used to implement the offshore wind power prediction method with multi-level spatiotemporal perception, including: The two-dimensional static plane feature module is used to regard each wind turbine in the offshore wind farm in the region as a node, determine the spatial relationship of the wind turbines according to the geographical location information of each wind turbine and the distance between the wind turbines, and construct the two-dimensional static plane feature; A dimension reduction module is used to obtain multi-dimensional time series wind power meteorological characteristics at multiple levels of altitude, and to reduce the dimension of the multi-dimensional time series wind power meteorological characteristics at each level of altitude; The feature integration and merging module is used to assign weights to the multi-dimensional time-series wind power meteorological features after dimensionality reduction, obtain the feature matrix after dimensionality reduction at each level of altitude, construct local graph features based on the feature matrix after dimensionality reduction, and merge the local graph features and the two-dimensional static plane features into a global spatial graph feature; The extraction module inputs the global spatial graph features into the graph convolutional neural network model to generate the final latent feature vector; The prediction module uses the final potential feature vector to train the constructed linear regression prediction model, and uses the trained linear regression prediction model for wind power prediction of offshore wind farms.

[0036] Compared with the prior art, the technical solution of the present invention has the following beneficial effects: The present invention proposes a method and system for predicting offshore wind power with multi-level spatiotemporal perception. First, according to the geographical location information of each wind turbine and the distance between the wind turbines, the spatial relationship of the wind turbines is determined, and two-dimensional static plane features are constructed. Then, multi-dimensional time-series wind power meteorological features at multi-level altitudes are obtained to capture the meteorological features of different altitudes that change over time. In order to reduce data redundancy and highlight key features, the present invention reduces the dimension of the multi-dimensional time-series wind power meteorological features at each level of altitude, compresses the complexity of high-dimensional data, retains the main change trend, assigns weights to the multi-dimensional time-series wind power meteorological features after dimensionality reduction, obtains the feature matrix after dimensionality reduction at each level of altitude, constructs local graph features based on the feature matrix after dimensionality reduction, merges the local graph features and the two-dimensional static plane features into a global spatial graph feature, and finally, inputs the global spatial graph features into a graph convolutional neural network model to generate the final potential feature vector, and uses a linear regression prediction model to predict wind power. The present invention makes full use of the geographical location information and meteorological information of wind turbines in wind farms, and at the same time considers a variety of meteorological factors at different altitudes to effectively reduce the prediction error caused by the uncertainty of wind power generation, and improves the accuracy of offshore wind power prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram showing a flow chart of an offshore wind power prediction method with multi-level spatiotemporal perception proposed in an embodiment of the present invention; Figure 2 A diagram showing the effect of using the offshore wind power prediction method with multi-level spatiotemporal perception proposed in an embodiment of the present invention to predict wind power; Figure 3 A structural diagram of an offshore wind power prediction system with multi-level spatiotemporal perception proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The accompanying drawings are only used for illustrative purposes and are not to be construed as limiting the present application; In order to better illustrate the present embodiment, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the actual size; It is understandable to those skilled in the art that descriptions of certain well-known contents in the drawings may be omitted.

[0039] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0040] The positional relationships described in the drawings are only for illustrative purposes and should not be construed as limiting the present application; Example 1 This embodiment proposes a method for predicting offshore wind power with multi-level spatiotemporal perception. The implementation process diagram of the method is shown in Figure 1 ,like Figure 1 As shown, the following steps are included: S1: Consider each wind turbine in the offshore wind farm in the region as a node, determine the spatial relationship of the wind turbines based on the geographical location information of each wind turbine and the distance between the wind turbines, and construct a two-dimensional static plane feature; S2: Obtain multi-dimensional time series wind power meteorological characteristics at multiple levels of altitude, and reduce the dimension of the multi-dimensional time series wind power meteorological characteristics at each level of altitude; S3: Assign weights to the multi-dimensional time-series wind power meteorological features after dimensionality reduction, obtain the feature matrix after dimensionality reduction at each level of altitude, construct local graph features based on the feature matrix after dimensionality reduction, and merge the local graph features and the two-dimensional static plane features into a global space graph feature; S4: Input the global spatial graph features into the graph convolutional neural network model to generate the final latent feature vector; S5: The final potential feature vector is used to train the constructed linear regression prediction model, and the trained linear regression prediction model is used for wind power prediction of offshore wind farms.

[0041] In this embodiment, firstly, according to the geographical location information of each wind turbine and the distance between the wind turbines, the spatial relationship of the wind turbines is determined, and a two-dimensional static plane feature is constructed. Then, the multi-dimensional time series wind power meteorological features at multiple levels of altitude are obtained to capture the meteorological features of different altitude layers that change over time. In order to reduce data redundancy and highlight key features, the present invention reduces the dimension of the multi-dimensional time series wind power meteorological features at each level of altitude, compresses the complexity of high-dimensional data, retains the main change trend, assigns weights to the multi-dimensional time series wind power meteorological features after dimensionality reduction, obtains the feature matrix after dimensionality reduction at each level of altitude, constructs local graph features based on the feature matrix after dimensionality reduction, merges the local graph features and the two-dimensional static plane features into a global spatial graph feature, and finally, inputs the global spatial graph features into the graph convolutional neural network model, generates the final potential feature vector, and uses the linear regression prediction model to predict wind power. The present invention makes full use of the geographical location information and meteorological information of wind turbines in wind farms, and at the same time considers various meteorological factors at different altitudes to effectively reduce the prediction error caused by the uncertainty of wind power generation, and improves the accuracy of offshore wind power prediction.

[0042] Example 2 In this embodiment, the geographical location information of each wind turbine includes: the longitude, latitude and altitude of each wind turbine; the geographical information of the wind turbines in the offshore wind farm in the region is represented as ,in P Indicates the number of fans, M Indicates the longitude, latitude and altitude of the wind turbine; A collection representing geographic information of wind turbines; The process of constructing a 2D static plane feature is: S11: Calculate wind turbines in offshore wind farms in the region i With fan j The normalized distance ND between them is calculated as follows:

[0043]

[0044]

[0045]

[0046]

[0047] in, Indicates fan i The longitude, Indicates fan i The latitude, Indicates fan i The altitude, have() is the intermediate operation function. For any x , , R represents the radius of the earth, Indicates fan i With fan j The distance between ; S12: The fan i Wind power Expressed as I , fan j Wind power Expressed as J , calculate the fan i and fan j The normalized mutual information NMI between is expressed as:

[0048]

[0049]

[0050]

[0051] in, It is a fan i and fan j The joint distribution of It is a fan i The marginal distribution of Indicates fan i The information entropy of Indicates fan j The information entropy of Indicates fan i and fan j The joint information entropy of Indicates fan i and fan j The mutual information of S13: Constructing 2D static plane features based on normalized distance and normalized mutual information between wind turbine pairs Specifically, a two-dimensional static plane feature space is constructed with the normalized distance as the horizontal axis and the normalized mutual information as the vertical axis, and the values ​​of the wind turbine pairs in these two feature dimensions are plotted in the feature space to form a feature map. Each wind turbine pair corresponds to a point on the feature map, and its horizontal and vertical coordinates are the normalized distance and normalized mutual information of the wind turbine pair, respectively.

[0052] In this embodiment, meteorological data in the area is collected, and the meteorological data includes: wind speed V ,wind direction D ,temperature T ,humidityH and air pressure P a The meteorological data changes with time, and the meteorological data is preprocessed. In this embodiment, the data is processed by min - max Normalization is performed as a preliminary process.

[0053] The altitude of offshore wind farms in the region L Divide into n levels, expressed as: , No. i Levels L i The interval of altitude z is fixed and satisfies: ,in, Indicates i Levels L i The lower limit of the altitude range is Indicates i Levels L i The upper limit of the altitude range; At each altitude z, the meteorological data features M ( z ) is expressed as a multidimensional vector, and the expression is: ; No. i Levels L i Meteorological data characteristics M ( L i ) is expressed as: .

[0054] In this embodiment, the process of reducing the dimension of the multi-dimensional time series wind power meteorological characteristics at each level of altitude is as follows: S21: Set a fixed sliding window Q and select Q consecutive sampling points from the time series to form a subsequence ,in, t is the current time point, x represents any one of the meteorological variables, including wind speed V ,wind direction D ,temperature T ,humidity H and air pressure P a ; S22: In each subsequence q A time series feature vector is constructed for each meteorological variable. :

[0055] Each row represents the time series characteristics of a single meteorological variable at a single node at each level of altitude. represents the time series mean of the acquisition point q, represents the time series variance of the acquisition point q, represents the time autocorrelation coefficient of the acquisition point q, Represents the spatial autocorrelation of the collection point q; the nodes within each level of altitude u All meteorological feature vectors are:

[0056] Integrate all node features to obtain the feature matrix of all nodes at each level of altitude f :

[0057] S23: Feature matrix f Perform principal component analysis to obtain a new feature matrix after dimensionality reduction ,right Calculate the covariance matrix C and perform eigenvalue decomposition on it to obtain the eigenvalues ​​and corresponding eigenvectors and , select the eigenvectors corresponding to the first k largest eigenvalues ​​to form the projection matrix W, and obtain the feature matrix after the dimensionality reduction of each level The expression is:

[0058] in, The size is n * k , 2<k<m, n is the number of meteorological variables, m is the total number of eigenvalues ​​after decomposition.

[0059] , , and The calculation expressions are:

[0060]

[0061]

[0062]

[0063] in, For fans iLocation and fan j The spatial weights between the locations.

[0064] The process of step S3 is: S31: Using dynamic feature selection, set the weight allocation strategy so that the feature matrix weight of the high altitude layer is larger and the sum of all weights is 1; the weight allocation strategy of the single altitude layer is:

[0065] in, z For the L i The height of the altitude layer, It is an adjustable parameter used to control the sensitivity of weights to different levels of altitude, and to achieve differential sensitivity control for high altitude and low altitude. is a growth index used to amplify the weight of high altitude layers. is the smoothing control coefficient; Through weighted summation, the feature matrix after all the altitudes of each level are reduced in dimension Merge into a local feature matrix :

[0066] in, N is the number of single altitude layers, For the The weight of each altitude layer; S32: Use multi-layer perceptron MLP to transform two-dimensional static plane features The dimension expansion makes the two-dimensional static plane feature The dimension and local feature matrix The dimension of the two-dimensional static plane feature matrix after the dimension expansion is the same as and the local feature matrix Vertical splicing into global spatial graph features F .

[0067] In this embodiment, the multilayer perceptron MLP includes an input layer, a hidden layer and an output layer. When designing the multilayer perceptron MLP, the number of nodes in the input layer matches the number of two-dimensional static plane features, that is, 2 nodes. In this embodiment, one or more hidden layers are designed, each hidden layer contains a certain number of nodes, and the number of nodes can be adjusted according to the complexity of the task and the dimension of the required output. The hidden layer uses an activation function (such as ReLU, Sigmoid or Tanh) to introduce nonlinear factors, so that the MLP can approximate complex nonlinear relationships.

[0068] Multi-layer perceptron MLP is used to transform the two-dimensional static plane features The process of dimensional expansion is: For 2D static plane features Perform pre-processing; The preprocessed two-dimensional static plane features Input to the trained multi-layer perceptron MLP; Calculate the one-dimensional vector output by the multi-layer perceptron output layer through the forward propagation algorithm of the multi-layer perceptron MLP; Convert the one-dimensional vector of the output layer into the local feature matrix Matrix form of the same dimensions.

[0069] In this embodiment, the process of generating the final potential feature vector described in step S4 is: S41: Based on the global spatial graph features F The cosine similarity between the nodes of the wind machine is used to construct the adjacency matrix A h , the expression is: ; S42: In a graph convolutional neural network model with several layers, global spatial graph features F and the adjacency matrix A h Perform a forward convolution operation; the convolution operation of any layer in the graph convolutional neural network model satisfies:

[0070] Where E is the degree matrix, is the weight matrix of the first graph convolutional neural network model layer, is the feature matrix after the first layer of graph convolution operation, It is p weight matrix of layer graph convolution; in this embodiment, PyTorch and PyTorch Geometric libraries are used to implement the convolution operation of the graph convolution neural network model. First, graph data is defined, including global spatial graph features and adjacency matrices. The torch_geometric.nn.conv.GCNConv class in PyTorch Geometric is used to define the graph convolution neural network model layer, and then the graph convolution neural network model layer is stacked to create a multi-layer graph convolution neural network model, and then forward propagation is performed. After performing the multi-layer graph convolution operation, the output features of the last layer of the graph convolution neural network model are extracted, and the output features include the final potential feature vector of the offshore wind farm wind turbine.

[0071] The process of step S5 is: The potential feature vector and the corresponding historical wind power data are used as training data to train the linear regression prediction model through forward propagation; The prediction error is calculated using the loss function, and the model parameter weights of the linear regression prediction model are updated using the optimizer. After the loss function converges or reaches the predetermined number of training rounds, the training is terminated to obtain a trained linear regression prediction model. Execute steps S1 to S4 to obtain a new potential feature vector, input the new potential feature vector into the trained linear regression prediction model, and output the wind power of the offshore wind farm.

[0072] Example 2 This embodiment verifies the effectiveness of the offshore wind power prediction method with multi-level spatiotemporal perception proposed in the present invention. In step S1, the geographical location information of a wind turbine in an offshore wind farm is obtained and the data is preprocessed; In step S2, the measured meteorological data of an offshore wind farm is obtained, including the power of the offshore wind farm, wind speed, wind direction, temperature, humidity, and air pressure at each altitude, and the data is preprocessed. The altitude is divided into 8 levels: 0m, 50m, 100m, 150m, 200m, 250m, 300m, and 350m; The offshore wind power prediction method with multi-level spatiotemporal perception proposed in the present invention is used for prediction, and the following is obtained: Figure 2 The predicted effect of offshore wind power is shown in Figure 2 In the figure, the dotted trend line represents the predicted value of offshore wind power, and the solid trend line represents the actual value of offshore wind power. Figure 2 It can be seen from the two curves shown in that the error between the actual value of wind power and the predicted value of wind power is very small, indicating that the method proposed in the present invention can effectively improve the accuracy of offshore wind power prediction.

[0073] Example 3 like Figure 3 As shown, the present application proposes an offshore wind power prediction system with multi-level spatiotemporal perception, characterized in that the system is used to implement the offshore wind power prediction method with multi-level spatiotemporal perception, including: The two-dimensional static plane feature module is used to regard each wind turbine in the offshore wind farm in the region as a node, determine the spatial relationship of the wind turbines according to the geographical location information of each wind turbine and the distance between the wind turbines, and construct the two-dimensional static plane feature; A dimension reduction module is used to obtain multi-dimensional time series wind power meteorological characteristics at multiple levels of altitude, and to reduce the dimension of the multi-dimensional time series wind power meteorological characteristics at each level of altitude; The feature integration and merging module is used to assign weights to the multi-dimensional time-series wind power meteorological features after dimensionality reduction, obtain the feature matrix after dimensionality reduction at each level of altitude, construct local graph features based on the feature matrix after dimensionality reduction, and merge the local graph features and the two-dimensional static plane features into a global spatial graph feature; The extraction module inputs the global spatial graph features into the graph convolutional neural network model to generate the final latent feature vector; The prediction module uses the final potential feature vector to train the constructed linear regression prediction model, and uses the trained linear regression prediction model for wind power prediction of offshore wind farms.

[0074] The embodiments 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 offshore wind power prediction with multi-level spatiotemporal perception, characterized in that: The following steps are involved: S1: Consider each wind turbine in the offshore wind farm in the region as a node, determine the spatial relationship of the wind turbines based on the geographical location information of each wind turbine and the distance between the wind turbines, and construct a two-dimensional static plane feature; S2: Obtain multi-dimensional time series wind power meteorological characteristics at multiple levels of altitude, and reduce the dimension of the multi-dimensional time series wind power meteorological characteristics at each level of altitude; The process of reducing the dimension of multi-dimensional time series wind power meteorological characteristics at each level of altitude is as follows: S21: Set a fixed sliding window Q and select Q consecutive sampling points from the time series to form a subsequence ,in, t is the current time point, x represents any one of the meteorological variables, including wind speed V ,wind direction D ,temperature T ,humidity H and air pressure P a ; S22: In each subsequence q A time series feature vector is constructed for each meteorological variable. : Each row represents the time series characteristics of a single meteorological variable at a single node at each level of altitude. represents the time series mean of the acquisition point q, represents the time series variance of the acquisition point q, represents the time autocorrelation coefficient of the acquisition point q, Represents the spatial autocorrelation of the collection point q; the nodes within each level of altitude u All meteorological feature vectors are: Integrate all node features to obtain the feature matrix of all nodes at each level of altitude f : S23: Feature matrix f Perform principal component analysis to obtain a new feature matrix after dimensionality reduction ,right Calculate the covariance matrix C and perform eigenvalue decomposition on it to obtain the eigenvalues ​​and corresponding eigenvectors and , select before k The eigenvectors corresponding to the largest eigenvalues ​​form the projection matrix W , and obtain the feature matrix after the dimension reduction of each level altitude The expression is: in, The size is n * k , 2<k<m, n is the number of meteorological variables, m is the total number of eigenvalues ​​after decomposition; S3: Assign weights to the multi-dimensional time-series wind power meteorological features after dimensionality reduction, obtain the feature matrix after dimensionality reduction at each level of altitude, construct local graph features based on the feature matrix after dimensionality reduction, and merge the local graph features and the two-dimensional static plane features into a global space graph feature; S4: Input the global spatial graph features into the graph convolutional neural network model to generate the final latent feature vector; S5: The final potential feature vector is used to train the constructed linear regression prediction model, and the trained linear regression prediction model is used for wind power prediction of offshore wind farms.

2. The offshore wind power prediction method with multi-level spatiotemporal perception according to claim 1 is characterized in that: The geographical location information of each wind turbine includes: the longitude, latitude and altitude of each wind turbine; the geographical information of the wind turbines in the offshore wind farm in the region is represented as ,in P Indicates the number of fans, M Indicates the longitude, latitude and altitude of the wind turbine; A collection representing geographic information of wind turbines; The process of constructing a 2D static plane feature is: S11: Calculate wind turbines in offshore wind farms in the region i With fan j The normalized distance ND between them is calculated as follows: in, Indicates fan i The longitude, Indicates fan i The latitude, Indicates fan i The altitude, have () is the intermediate operation function. For any x , , R represents the radius of the earth, Indicates fan i With fan j The distance between ; S12: The fan i Wind power Expressed as I , fan j Wind power Expressed as J , calculate the fan i and fan j The normalized mutual information NMI between is expressed as: in, It is a fan i and fan j The joint distribution of It is a fan i The marginal distribution of Indicates fan i The information entropy of Indicates fan j The information entropy of Indicates fan i and fan j The joint information entropy of Indicates fan i and fan j The mutual information of S13: Constructing 2D static plane features based on normalized distance and normalized mutual information between wind turbine pairs .

3. The offshore wind power prediction method with multi-level spatiotemporal perception according to claim 2 is characterized in that: Collect meteorological data in the area, including: wind speed V ,wind direction D ,temperature T ,humidity H and air pressure P a , the meteorological data changes with time, and the meteorological data is preprocessed; The altitude of offshore wind farms in the region L Divide into n levels, expressed as: , No. i Levels L i The interval of altitude z is fixed and satisfies: ,in, Indicates i Levels L i The lower limit of the altitude range is Indicates i Levels L i The upper limit of the altitude range; At each altitude z, the meteorological data features M ( z ) is expressed as a multidimensional vector, and the expression is: ； No. i Levels L i Meteorological data characteristics M ( L i ) is expressed as: 。 4. The offshore wind power prediction method with multi-level spatiotemporal perception according to claim 1 is characterized in that: , , and The calculation expressions are: in, For fans i Location and fan j The spatial weights between the locations.

5. The offshore wind power prediction method with multi-level spatiotemporal perception according to claim 4 is characterized in that: The process of step S3 is: S31: Using dynamic feature selection, set the weight allocation strategy so that the feature matrix weight of the high altitude layer is larger and the sum of all weights is 1; the weight allocation strategy of the single altitude layer is: in, z For the L i The height of the altitude layer, It is an adjustable parameter used to control the sensitivity of weights to different levels of altitude, and to achieve differential sensitivity control for high altitude and low altitude. is a growth index used to amplify the weight of high altitude layers. is the smoothing control coefficient; Through weighted summation, the feature matrix after all the altitudes of each level are reduced in dimension Merge into a local feature matrix : in, N is the number of single altitude layers, For the The weight of each altitude layer; S32: Use multi-layer perceptron MLP to transform two-dimensional static plane features The dimension expansion makes the two-dimensional static plane feature The dimension and local feature matrix The dimension of the two-dimensional static plane feature matrix after the dimension expansion is the same as and the local feature matrix Vertical splicing into global spatial graph features F .

6. The offshore wind power prediction method with multi-level spatiotemporal perception according to claim 5 is characterized in that: Multi-layer perceptron MLP is used to transform the two-dimensional static plane features The process of dimensional expansion is: For 2D static plane features Perform pre-processing; The preprocessed two-dimensional static plane features Input to the trained multi-layer perceptron MLP; Calculate the one-dimensional vector output by the multi-layer perceptron output layer through the forward propagation algorithm of the multi-layer perceptron MLP; Convert the one-dimensional vector of the output layer into the local feature matrix Matrix form of the same dimensions.

7. The offshore wind power prediction method with multi-level spatiotemporal perception according to claim 6 is characterized in that: The process of generating the final potential feature vector described in step S4 is: S41: Based on the global spatial graph features F The cosine similarity between the nodes of the wind machine is used to construct the adjacency matrix A h , the expression is: ； S42: In a graph convolutional neural network model with several layers, global spatial graph features F and the adjacency matrix A h Perform a forward convolution operation; wherein the convolution operation of any layer in the graph convolutional neural network model satisfies: Where E is the degree matrix, is the weight matrix of the first graph convolutional neural network model layer, is the feature matrix after the first layer of graph convolution operation, It is p A weight matrix of a layer graph convolution; after performing a multi-layer graph convolution operation, extracting the output features of the last layer of the graph convolution neural network model, wherein the output features include the final potential feature vector of the offshore wind farm wind turbine.

8. The offshore wind power prediction method with multi-level spatiotemporal perception according to claim 7 is characterized in that: The process of step S5 is: The potential feature vector and the corresponding historical wind power data are used as training data to train the linear regression prediction model through forward propagation; The prediction error is calculated using the loss function, and the model parameter weights of the linear regression prediction model are updated using the optimizer. After the loss function converges or reaches the predetermined number of training rounds, the training is terminated to obtain a trained linear regression prediction model. Execute steps S1 to S4 to obtain a new potential feature vector, input the new potential feature vector into the trained linear regression prediction model, and output the wind power of the offshore wind farm.

9. An offshore wind power prediction system with multi-level spatiotemporal perception, characterized in that: The system is used to implement the offshore wind power prediction method with multi-level time and space perception as described in any one of claims 1 to 8, comprising: The two-dimensional static plane feature module is used to regard each wind turbine in the offshore wind farm in the region as a node, determine the spatial relationship of the wind turbines according to the geographical location information of each wind turbine and the distance between the wind turbines, and construct the two-dimensional static plane feature; A dimension reduction module is used to obtain multi-dimensional time series wind power meteorological characteristics at multiple levels of altitude, and to reduce the dimension of the multi-dimensional time series wind power meteorological characteristics at each level of altitude; The feature integration and merging module is used to assign weights to the multi-dimensional time-series wind power meteorological features after dimensionality reduction, obtain the feature matrix after dimensionality reduction at each level of altitude, construct local graph features based on the feature matrix after dimensionality reduction, and merge the local graph features and the two-dimensional static plane features into a global spatial graph feature; The extraction module inputs the global spatial graph features into the graph convolutional neural network model to generate the final latent feature vector; The prediction module uses the final potential feature vector to train the constructed linear regression prediction model, and uses the trained linear regression prediction model for wind power prediction of offshore wind farms.

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