CN114004152A - Multi-wind-field wind speed space-time prediction method based on graph convolution and recurrent neural network - Google Patents
Multi-wind-field wind speed space-time prediction method based on graph convolution and recurrent neural network Download PDFInfo
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Abstract
The invention discloses a multi-wind-field wind speed space-time prediction method based on a graph convolution and a cyclic neural network, and provides a graph convolution long-short-term memory neural network for processing data, aiming at the problem that a convolution neural network commonly used in the conventional wind speed space-time prediction method is difficult to effectively analyze wind speed data of a multi-wind field showing non-grid distribution in reality. Firstly, carrying out graph modeling on wind speed data of a plurality of wind fields based on a Pearson correlation coefficient, and constructing a wind speed graph signal sequence; then, the convolution of the graph is used for replacing the multiplication operation in the long-term and short-term memory neural network to construct the convolution of the graph and the long-term and short-term memory neural network; and finally, constructing a multi-wind-field wind speed space-time prediction model based on the graph convolution long-term short-term memory neural network and the transfer learning principle. The space-time prediction model provided by the invention has better point prediction and probability prediction performance, verifies that the fusion of the historical wind speed information of the adjacent wind field can be helpful for improving the accuracy of wind speed point prediction and probability prediction, and provides a new idea for short-term wind speed prediction of a multi-wind field.
Description
Technical Field
The invention relates to a wind speed prediction method, in particular to a multi-wind-field wind speed space-time prediction method based on a graph volume and a recurrent neural network.
Background
The wind energy is a clean and pollution-free renewable energy source, and has the characteristics of rich resources, clean power generation process and the like, so that the wind energy is widely popularized and applied. However, due to the fluctuation and intermittency of wind speed, wind power has strong fluctuation and randomness. At present, wind speed spatio-temporal prediction methods generally include spatio-temporal prediction models based on correlation analysis and spatio-temporal prediction models based on convolutional neural networks. The time-space prediction model based on the correlation analysis judges the correlation degree between the historical wind speed of the adjacent wind field and the historical wind speed of the wind field to be predicted by using correlation analysis methods such as Pearson coefficients, mutual information and the like, extracts important features in the historical wind speed data of the adjacent wind field by setting a threshold value, and sends the important features and the historical wind speed data of the wind field to be predicted into the prediction model together for learning training. The method is simple to implement, but the space-time structure of the original multi-wind-field wind speed data is destroyed, so that the space-time characteristics in the original wind speed data cannot be extracted more fully. The time-space prediction model based on the convolutional neural network is used for analyzing and learning wind speed data of multiple wind fields by using the convolutional neural network, the wind speed data of the multiple wind fields are constructed into a two-dimensional map with channels through grid division, grid points on the two-dimensional map represent the relative positions of the wind fields, the longitudinal channel of each grid point is historical wind speed data of each wind field, the convolutional neural network simultaneously extracts the time-space characteristics of the wind speeds of the multiple wind fields from two layers of space and time through a convolutional kernel, and the extracted time-space characteristics can be directly sent to other models (such as a long-short term memory neural network) to be trained and learned, so that the time-space prediction of the wind speeds is realized. The time-space prediction model based on the convolutional neural network is applied to wind speed prediction, but the time-space prediction model has a problem that wind fields in actual engineering are not necessarily distributed in a grid shape, and the relative positions of the wind fields in a two-dimensional map are difficult to determine.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a multi-wind-field wind speed space-time prediction method based on a graph convolution and a cyclic neural network, aiming at the problem that the conventional convolutional neural network can only process data in a Euclidean space and is difficult to process data in a non-Euclidean space. The multi-wind-field prediction model based on the graph convolution cyclic neural network has strong feature extraction capability, can effectively process non-European data, and further improves the prediction accuracy of the wind speed of the multi-wind-field.
The technical scheme is as follows: the invention provides a multi-wind-field wind speed space-time prediction method based on a graph volume and a cyclic neural network, which comprises the following steps of:
step (1): obtaining position information and wind speed data of a plurality of wind fields in a certain area, carrying out graph modeling by using an undirected weighted graph based on the position information and the wind speed data, and carrying out spectrogram convolution calculation on the established undirected weighted graph to construct a wind speed graph signal sequence;
the anemogram signal sequence modeling method comprises the following steps:
for M wind fields in a region, its data is modeled using an undirected weighted graph G ═ (V, E, a), where V ═ V1，v2，…，vMAnd E is a set of edges, and A is an adjacency matrix reflecting the connection relation and the weight among all the vertexes. For each Aij∈A，Its definition is as follows:
wherein d (v)i，vj) Representing a vertex viAnd vjThe distance between the ith wind field and the jth wind field, i.e. the geographical distance between the ith wind field and the jth wind field,
representing the standard deviation of all pairs of vertex distances for removing the effect of dimension, PCC (v)i，vj) Representing a Pearson correlation coefficient calculated based on the ith wind field and the jth wind field historical wind speed data, wherein gamma is a threshold value of the Pearson coefficient when PCC (vi，vj) When gamma is greater than or equal to gamma, then viAnd vjWith edges eijE is connected to E and has a weight of
When PCC (v)i，vj) When less than gamma, then viAnd vjNo edges are connected. The purpose of setting the gamma threshold is to connect wind field vertexes with strong correlation in order to obtain a sparse wind field image, so that a part of priori knowledge is provided for training and learning of a subsequent model, and the training time of the model is reduced.
Will be located at vertex viThe wind speed of the wind field at time t is recorded as
The anemogram signal at time t can be recorded as
The space-time prediction to be realized by the invention is based on historical anemogram signal sequence x at a plurality of momentst-K+1，xt-K+2，…，xtTo predict the anemogram signal xt+1Wherein K is a natural number.
Further, in the step (1), a spectrogram convolution calculation is used for the established undirected weighted graph, wherein a calculation formula of the spectrogram convolution layer is as follows:
wherein Q represents the number of convolution kernels, Y：，qRepresenting the output of the qth convolution kernel, X：，pRepresenting the p-th channel map signal input, f is a convolution kernelgRepresenting a graph convolution operation, Wq，p，：The parameter representing the qth convolution kernel corresponding to the pth channel map signal input, a (-) represents the activation function.
Step (2): the multiplication operation in the long and short term memory neural network is replaced by graph convolution calculation, a graph convolution long and short term memory neural network is constructed, and a multi-wind-field wind speed space-time point prediction model based on the graph convolution long and short term memory neural network is established on the basis; the multiplication operation in the long-short term memory neural unit is replaced by spectrogram convolution operation, and the method is improved into a Graph convolution long-short term memory unit (GCLSTM cell), and the calculation formula is as follows:
It＝σ(Wxi*g Xt+Whi*g Ht-1+Bi)
Ft＝σ(Wxf*g Xt+Whf*g Ht-1+Bf)
Ot＝σ(Wxo*g Xt+Who*g Ht-1+Bo)
Ct＝Ft⊙Ct-1+It⊙tanh(Wxc*g Xt+Whc*g Ht-1+Bc)
Ht＝Ot⊙tanh(Ct)
wherein, It、Ft、OtRespectively representing the input gate, the forgetting gate and the output gate matrix at the time t, CtAnd HtRespectively represent t timesMatrix of states of cells and hidden layers in the cell, XtInput matrix, W, representing long and short term memory cells at time txi、Wxf、Wxo、WxcWeight tensors W of input gate, forgetting gate, output gate and cell state in input layer neuron of long and short term memory networkhi、Whf、Who、WhcWeight tensors of input gate, forget gate, output gate and cell state in neuron of hidden layer of long-short term memory network, respectively, Bi、Bf、BoAnd BcBias matrices representing input gate, forgetting gate, output gate and cell state, respectivelygThe graph convolution operation is shown.
And (3): training parameters of a multi-wind-field wind speed space-time point prediction model based on historical wind speed data of the multi-wind-field to obtain the multi-wind-field wind speed space-time point prediction model; the multi-wind-field wind speed space-time point prediction model based on the graph convolution long-term memory neural network is characterized in that a trained error loss formula is as follows:
wherein M represents the number of samples in each batch of training set, M is the number of wind fields,
representing the actual value of the output of the s-th wind farm in the ith sample of each batch of training sets,
and (4) representing the predicted value of the output of the s-th wind field in the ith sample in each batch of training sets.
And (4): based on a transfer learning principle, transferring the point prediction model parameters trained in the step (3), constructing a multi-wind-field wind speed space-time probability prediction model, and further training to obtain the multi-wind-field wind speed space-time probability prediction model; the multi-wind-field wind speed space-time probability prediction model based on the graph convolution long-term memory neural network comprises a quantile loss formula of model training, wherein the quantile loss formula comprises the following components:
wherein q represents the number of quantiles,
denotes the loss of quantile at the jth quantile, fs(W(τj)，b(τj)，Xi) Tau of the s wind field representing the output of the ith sample model in each batch of training setsjWind speed prediction in quantile, W (τ)j) And b (τ)j) Is and quantile τjThe relevant model parameters, i.e. W and b parameters, X, of the fully-connected layer in the probabilistic predictive modeliRepresenting the input of the ith sample in each batch of training sets. Here, based on the migration learning principle, the parameters of the GCLSTM layer in the point prediction model are migrated to the GCLSTM layer in the probabilistic prediction model, in order to reduce the time required for training the probabilistic prediction model.
And (5): and (3) constructing wind field information needing wind speed prediction into a wind speed diagram signal sequence through the step (1), sequentially inputting the wind speed diagram signal sequence to be predicted into the models obtained by training in the step (3) and the step (4), and obtaining the point prediction and probability prediction results of the wind speed of the multi-wind field.
Has the advantages that: compared with the prior art, the technical scheme of the invention is based on the position information and the wind speed data of multiple wind fields in a certain area, the data are preprocessed by adopting graph modeling and spectrogram convolution calculation, different pooling combinations and corresponding training data pools are constructed by pooling, and an improved graph convolution wind speed prediction model is input, so that the time-space structure and the time-space characteristics of the wind speed data of the multiple wind fields can be deeply extracted, guidance is provided for the implementation of scheduling and demand response of a wind power system, and the safe, stable and economic operation of the wind power generation system is ensured.
Drawings
FIG. 1 is a schematic flow chart of a multi-wind-field wind speed space-time prediction method based on a graph convolution and a recurrent neural network;
FIG. 2 is a schematic diagram of the modeling results of a wind speed data graph according to the present invention;
FIG. 3 is a diagram illustrating a convolutional long short term memory cell structure according to the present invention;
FIG. 4 is a diagram of a GCLSTM-based multi-wind-field wind speed space-time prediction model structure;
FIG. 5 is a comparison graph of annual point prediction indexes of different models of each wind farm in the embodiment of the present invention, including corresponding graphs of the RMSE index, the MAE index, and the NSE index;
fig. 6 is a graph of the probability prediction effect of different models in each wind field during (a) spring, (b) summer, (c) autumn, and (d) winter periods, respectively, according to an embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.
The invention provides a multi-wind-field wind speed space-time prediction method based on a graph convolution and a recurrent neural network, as shown in figure 1, the method comprises the following steps:
step (1): obtaining position information and wind speed data of a plurality of wind fields in a certain area, carrying out graph modeling on the data by using the undirected weighted graph, carrying out spectrogram convolution calculation on the established undirected weighted graph, realizing preliminary analysis processing of wind speed graph signals, and constructing a wind speed graph signal sequence.
Step (2): substituting the multiplication operation in the long-short term memory neural network for Graph convolution calculation, constructing a Graph convolution long-short term memory neural network, and building a multi-wind-field wind speed time-space-point prediction model based on the Graph convolution long-short term memory neural network (GCLSTM);
and (3): training parameters of a multi-wind-field wind speed space-time point prediction model based on historical wind speed data of the multi-wind-field to obtain the multi-wind-field wind speed space-time point prediction model;
and (4): based on a transfer learning principle, transferring the point prediction model parameters trained in the step (3), constructing a multi-wind-field wind speed space-time probability prediction model, and further training to obtain the multi-wind-field wind speed probability prediction model;
and (5): and (3) constructing wind field information needing wind speed prediction into a wind speed diagram signal sequence through the step (1), sequentially inputting the wind speed diagram signal sequence to be predicted into the models obtained by training in the step (3) and the step (4), and obtaining the point prediction and probability prediction results of the wind speed of the multi-wind field.
The following describes in detail a specific implementation process of multi-wind field wind speed prediction using the method of the present invention with reference to specific embodiments. Taking 76 wind field wind speed data near Michigan lake in America as an example, wind speed data from 1 month and 1 day in 2004 to 12 months and 31 days in 2006 are acquired according to a sampling frequency of 1 hour, and geographical position information of the 76 wind fields is shown in Table 1. In order to test the generalization capability and the prediction accuracy of the model, the data of the last three days of each month are used as a test set, the rest data are used as a training set, the wind speed data in 76 wind fields are predicted in advance for 6 hours, and the used prediction error evaluation indexes are a Nash efficiency coefficient ANSE, an average absolute error MAE and a root mean square error RMSE. The specific implementation steps are as follows:
in the step (1), the anemogram signal sequence modeling method and the graph convolution calculation method are as follows:
1.1 for M wind farms in a region, its data is modeled using an undirected weighted graph G ═ (V, E, a), where V ═ V1，v2，…，vMAnd E is a set of edges, and A is an adjacency matrix reflecting the connection relation and the weight among all the vertexes. For each Aije.A, the definition of which is shown below:
wherein d (v)i，vj) Representing a vertex viAnd vjThe distance between the ith wind field and the jth wind field, i.e. the geographical distance between the ith wind field and the jth wind field,
representing the standard deviation of all pairs of vertex distances for removing the effect of dimension, PCC (v)i，vj) Representing a Pearson correlation coefficient calculated based on the ith wind field and the jth wind field historical wind speed data, wherein gamma is a threshold value of the Pearson coefficient when PCC (vi，vj) When gamma is greater than or equal to gamma, then viAnd vjWith edges eijE is connected to E and has a weight of
When PCC (v)i，vj) When less than gamma, then viAnd vjNo edges are connected. The purpose of setting the gamma threshold is to connect wind field vertexes with strong correlation in order to obtain a sparse wind field image, so that a part of priori knowledge is provided for training and learning of a subsequent model, and the training time of the model is reduced.
Will be located at vertex viThe wind speed of the wind field at time t is recorded as
The anemogram signal at time t can be recorded as
The space-time prediction to be realized by the invention is based on historical anemogram signal sequence x at a plurality of momentst-K+1，xt-K+2，…，xtTo predict the anemogram signal xt+1In the present invention, K is set to 6.
1.2, performing spectrogram convolution calculation on the established undirected weighted graph, wherein the calculation formula of the spectrogram convolution layer is as follows:
wherein Q represents the number of convolution kernels, Y：，qRepresenting the output of the qth convolution kernel, X：，pRepresenting the p-th channel map signal input, f is a convolution kernelgRepresenting a graph convolution operation, Wq，p，：The parameters representing the q-th convolution kernel corresponding to the P-th channel map signal input, a (-) represents the activation function, and P represents the number of channels of the map signal input, i.e., having P vertex features.
This patent uses wind speed data from 1/2004 to 31/2005 to set the γ threshold value to 0.85, and the finally obtained wind field graph contains 76 nodes, 1305 edges, as shown in fig. 2, which is drawn in an elastic Layout (Spring Layout). In addition, the data are also used for training and verifying the model, wherein the ratio of the training set to the verifying set is 9: 1, and wind speed data from 1 month and 1 day to 12 months and 31 days in 2006 are used as a testing set of the model.
The long and short term memory network is improved in the step (2), and the principle of the graph convolution long and short term memory neural network is as follows:
2.1 the long-short term memory neural network is a variant of the recurrent neural network, the long-short term memory unit is used to replace the hidden unit in the original recurrent neural network, the long-short term memory unit has three gate structures, which are the input gate, the forgetting gate and the output gate, and can effectively control the updating of the cell state and the hidden layer state, and the internal calculation formula is as follows:
it＝σ(ωxixt+ωhiht-1+bi)
ft＝σ(ωxfxt+ωhfht-1+bf)
ot＝σ(ωxoxt+ωhoht-1+bo)
ct＝ft⊙ct-1+it⊙tanh(ωxcxt+ωhcht-1+bc)
ht＝ot⊙tanh(ct)
wherein it、ft、otRespectively representVector of input gate, forget gate and output gate at time t, ctAnd htRespectively representing the cell state and hidden layer state vectors at time t, xiInput vector, ω, representing long-short term memory unit at time txi、ωhi、ωxf、ωhf、ωxo、ωho、ωxcAnd ωhcAs a weight matrix, bi、bf、boAnd bcFor the bias vector, σ (-) represents a sigmoid function, and tanh (-) represents a hyperbolic tangent function.
2.2 the patent replaces the multiplication operations in the long and short term memory neural unit with spectrogram convolution operations, and improves the multiplication operations into Graph convolution long and short term memory units (GCLSTM cells), and the basic calculation formula is as follows:
It＝σ(Wxi*g Xt+Whi*g Ht-1+Bi)
Ft＝σ(Wxf*g Xt+Whf*g Ht-1+Bf)
Ot＝σ(Wxo*g Xt+Who*g Ht-1+Bo)
Ct＝Ft⊙Ct-1+It⊙tanh(Wxc*g Xt+Whc*g Ht-1+Bc)
Ht＝Ot⊙tanh(Ct)
wherein, It、Ft、OtRespectively representing the input gate, the forgetting gate and the output gate matrix at the time t, CtAnd HtMatrix representing the state of the cell and the state of the hidden layer at time t, xtInput matrix, W, representing long and short term memory cells at time txi、Wxf、Wxo、WxcWeight tensors W of input gate, forgetting gate, output gate and cell state in input layer neuron of long and short term memory networkhi、Whf、Who、WhcWeight tensors of input gate, forget gate, output gate and cell state in neuron of hidden layer of long-short term memory network, respectively, Bi、Bf、BoAnd BcBias matrices representing input gate, forgetting gate, output gate and cell state, respectivelygThe graph convolution operation is shown. The basic structure of the convolution long short term memory cell proposed in this patent is shown in FIG. 3.
Building a multi-wind-field wind speed space-time point prediction model based on a graph convolution long-term short-term memory neural network in the steps (2) and (3), building a probability prediction model based on a transfer learning principle in the step (4), wherein the basic structure of the model is shown in FIG. 4, and the error loss formula of the multi-wind-field wind speed space-time point prediction model training is as follows:
wherein M represents the number of samples in each batch of training set, M is the number of wind fields,
representing the actual value of the output of the s-th wind farm in the ith sample of each batch of training sets,
and (4) representing the predicted value of the output of the s-th wind field in the ith sample in each batch of training sets.
The quantile loss formula of the multi-wind field wind speed space-time probability prediction model training is as follows:
wherein q represents the number of quantiles, and the number of quantiles is 21 in this embodiment, i.e. 0.01, 0.05, 0.10iRepresenting the input of the ith sample in each batch of training sets, fs(W(τj)，b(τj)，Xi) Tau of the s wind field representing the output of the ith sample model in each batch of training setsjWind speed prediction in quantile, W (τ)j) And b (τ)j) Is and quantile τjAnd relevant model parameters, namely parameters of a full connection layer in the probability prediction model. Based on the transfer learning principle, the parameters of the GCLSTM layer in the point prediction model are transferred to the GCLSTM layer in the probability prediction model, so that the time required by the training of the probability prediction model is reduced.
The method uses the Average Root Mean Square Error (ARMSE), the Nash efficiency coefficient (ANSE) and the Average absolute error (AMAE) of a plurality of wind fields to evaluate the effect of the time-space point prediction of the wind speed of the wind fields, and the expression is as follows:
wherein N is the number of samples in the test set.
This patent uses the Average Quantile Score (Average Quantile Score, AQS) of a plurality of wind fields to come the comprehensive evaluation model probability density prediction effect, and its expression is as follows:
wherein the content of the first and second substances,
representing the s-th wind field τ corresponding to the i-th sample model outputjAnd (5) predicting the wind speed under quantiles. The smaller the score of the average fractional number is, the more the probability prediction effect isGood results are obtained.
In order to verify the effectiveness of the point prediction model, ANN and LSTM are selected as comparison models, and because the ANN and LSTM cannot process image signal samples, an ANN and LSTM model is established for each wind field independently. The method selects multi-wind-field wind speed data of 1 month, 4 months, 7 months and 10 months in 2006 to compare the prediction effects of the model in different seasons. The prediction indexes of different models in different seasons and all the year are shown in table 2. As can be seen from the table, the model provided by the patent has the optimal point prediction indexes in different seasons and all the year, which shows that the accuracy of wind speed prediction can be effectively improved by fully utilizing the historical wind speed information of the adjacent wind field. In addition, as can be seen from the dimensionless ANSE indexes, the model provided by the patent has the best predicted performance in winter, the ANSE index reaches 0.9335, and the predicted performance in summer is the worst, the ANSE index is only 0.7753, and other comparative models have similar characteristics, which may be that the wind speed fluctuation and randomness in summer are stronger than those in other seasons due to climate reasons, so that the predictability is relatively weak.
FIG. 5 illustrates the point prediction index for the year for different models for each wind farm. In the graph, the RMSE index curve and the MAE index curve of the model provided by the patent are obviously lower than those of the other two comparison models, and the NSE index curve is obviously higher than those of the two comparison models, so that the prediction accuracy of each wind field is improved by fusing the historical wind speed information of the adjacent wind fields.
In order to verify the effectiveness of the proposed probabilistic predictive model, the patent also chooses to use quantile loss optimized ANN and LSTM networks as comparison models. Table 3 shows the probability predictors for different models in different seasons and throughout the year. As can be seen from the table, the average probability indexes AQS of the model provided by the method in different seasons are optimal, and the model shows better probability prediction performance, so that the method is combined with the historical wind speed information of the adjacent wind field and is also beneficial to the probability prediction of the wind speed. It can also be seen from the table that the probabilistic prediction performance of the model proposed herein is optimal in the winter, while the probabilistic prediction performance in the summer is relatively poor.
Fig. 6 shows, by way of example, a wind field No. 1, a probabilistic prediction result of the model provided by the patent representing 5 days before a month in different seasons. As can be seen from the figure, the black dashed line representing the actual value mostly falls within the confidence interval in different seasons, which indicates that the model provided by the invention has better prediction reliability.
Geographical position information of 76 wind fields in area of table 1
TABLE 2 Point prediction indexes for different models in different seasons
TABLE 3 probability prediction index for different models in different Seasons (AQs)
Claims (8)
1. A multi-wind-field wind speed space-time prediction method based on a graph volume and a recurrent neural network is characterized by comprising the following steps:
step (1): obtaining position information and wind speed data of a plurality of wind fields in a certain area, carrying out graph modeling by using an undirected weighted graph based on the position information and the wind speed data, and carrying out spectrogram convolution calculation on the established undirected weighted graph to construct a wind speed graph signal sequence;
step (2): the multiplication operation in the long and short term memory neural network is replaced by graph convolution calculation, a graph convolution long and short term memory neural network is constructed, and a multi-wind-field wind speed space-time point prediction model based on the graph convolution long and short term memory neural network is established on the basis;
and (3): training parameters of a multi-wind-field wind speed space-time point prediction model based on historical wind speed data of the multi-wind-field to obtain the multi-wind-field wind speed space-time point prediction model;
and (4): based on a transfer learning principle, transferring the point prediction model parameters trained in the step (3), constructing a multi-wind-field wind speed space-time probability prediction model, and further training to obtain the multi-wind-field wind speed space-time probability prediction model;
and (5): and (3) constructing wind field information needing wind speed prediction into a wind speed diagram signal sequence through the step (1), sequentially inputting the wind speed diagram signal sequence to be predicted into the models obtained by training in the step (3) and the step (4), and obtaining the point prediction and probability prediction results of the wind speed of the multi-wind field.
2. The method for multi-wind field wind speed spatiotemporal prediction based on graph convolution and recurrent neural network as claimed in claim 1, wherein said using undirected weighted graph in step (1) is graph modeling based on said location information and wind speed data by the following method:
for M wind farms in a region, its data is modeled using an undirected weighted graph G ═ (V, E, a), where V ═ V1，v2，…，vMIs a set of vertices, each vertex representing a wind field of the area, E is a set of edges, A is an adjacency matrix, for each Aije.A, the definition of which is shown below:
wherein d (v)i，vj) Representing a vertex viAnd vjThe distance between the ith wind field and the jth wind field, i.e. the geographical distance between the ith wind field and the jth wind field,
representing the standard deviation of all pairs of vertex distances for removing the effect of dimension, PCC (v)i，vj) And the system represents a Pearson correlation coefficient calculated based on the ith wind field and the jth wind field historical wind speed data, and gamma is a threshold value of the Pearson coefficient.
3. The multi-wind-field wind speed space-time prediction method based on the graph volume and the recurrent neural network as claimed in claim 2, wherein: constructing the anemogram signal sequence described in step (1) to be located at the vertex viThe wind speed of the wind field at time t is recorded as
The anemogram signal at time t is recorded as
The anemogram signal sequence at several historical moments is xt-K+1，xt-K+2，…，xtK is a natural number, and the anemogram signal x at the next moment is predicted by using the anemogram signal sequencet+1。
4. The multi-wind-field wind speed space-time prediction method based on graph convolution and recurrent neural network as claimed in claim 1, wherein the step (1) uses a spectrogram convolution calculation on the established undirected weighted graph, wherein the calculation formula of the spectrogram convolution layer is as follows:
wherein Q represents the number of convolution kernels, Y：，qRepresenting the output of the qth convolution kernel, X：，pRepresenting the p-th channel map signal input, f is a convolution kernelgRepresenting a graph convolution operation, Wq，p，：The parameter representing the q-th convolution kernel corresponding to the P-th channel map signal input, a (-) represents the activation function, and P represents the number of channels of the map signal input.
5. The method for multi-wind-field wind speed space-time prediction based on the convolution sum cyclic neural network as claimed in claim 1, wherein the convolution sum short term memory neural network in step (2) has the following calculation formula:
It＝σ(Wxi*gXt+Whi*gHt-1+Bi)
Ft＝σ(Wxf*gXt+Whf*gHt-1+Bf)
Ot＝σ(Wxo*gXt+Who*gHt-1+Bo)
Ct＝Ft⊙Ct-1+It⊙tanh(Wxc*gXt+Whc*gHt-1+Bc)
Ht＝Ot⊙tanh(Ct)
wherein, It、Ft、otRespectively representing the input gate, the forgetting gate and the output gate matrix at the time t, CtAnd HtMatrix representing the state of the cell and the state of the hidden layer at time t, XtInput matrix, W, representing long and short term memory cells at time txi、Wxf、Wxo、WxcWeight tensors W of input gate, forgetting gate, output gate and cell state in input layer neuron of long and short term memory networkhi、Whf、Who、WhcWeight tensors of input gate, forget gate, output gate and cell state in neuron of hidden layer of long-short term memory network, respectively, Bi、Bf、BoAnd BcBias matrices representing input gate, forgetting gate, output gate and cell state, respectivelygThe graph convolution operation is shown.
6. The method for multi-wind-field wind speed space-time prediction based on graph convolution and recurrent neural network as claimed in claim 1, wherein the error loss formula of the model training of the multi-wind-field wind speed space-time point prediction model in step (3) is:
wherein M represents the number of samples in each batch of training set, M is the number of wind fields,
representing the actual value of the output of the s-th wind farm in the ith sample of each batch of training sets,
and (4) representing the predicted value of the output of the s-th wind field in the ith sample in each batch of training sets.
7. The multi-wind-field wind speed space-time prediction method based on the graph convolution and the recurrent neural network as claimed in claim 1, wherein the multi-wind-field wind speed space-time probability prediction model in step (4) has a quantile loss formula trained by the model as follows:
wherein q represents the number of quantiles, M represents the number of samples in each batch of training set, M is the number of wind fields,
represents the quantile loss of the jth quantile,
representing the actual value of the s-th wind field output in the ith sample of the training set, fs(W(τj)，b(τj)，Xi) Representing the quantile tau of the s wind field output by the ith sample model in each batch of training setsjLower wind speed prediction, W (τ)j) And b (τ)j) Is the fractional number tau in a multi-wind field wind speed space-time probability prediction modeljW and b parameters, X, of the underlying fully-connected layeriRepresenting the input of the ith sample in each batch of training sets.
8. The multi-wind-field wind speed space-time prediction method based on the graph convolution and the recurrent neural network as claimed in claim 1, wherein the step (4) of migrating the point prediction model parameters trained in the step (3) based on the migration learning principle is to migrate the parameters of the graph convolution long and short term memory neural network layer in the point prediction model to the graph convolution long and short term memory neural network layer in the probabilistic prediction model based on the migration learning algorithm.
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