Wind power plant cluster short-term power prediction method based on space-time diagram convolutional neural network

Abstract

The invention provides a wind power plant cluster short-term power prediction method based on a space-time diagram convolutional neural network, and the method comprises the steps: collecting historical power in a first target time period to obtain a historical power vector time sequence, and collecting weather forecast parameter vectors in a second target time period to obtain a weather forecastparameter matrix time sequence; inputting the historical power vector and the time sequence of the weather forecast parameter matrix into a prediction model, and outputting a predicted power vector time sequence in a third target time period, wherein the prediction model is obtained by training based on a sample historical power vector, a sample weather forecast parameter matrix timing sequence and a prediction power vector timing sequence label, and a neural network structure of the prediction model is formed based on a Bi-GRU network and a graph convolution network. According to the method provided by the invention, two factors of historical power and weather forecast parameters can be jointly considered in power prediction, and the prediction accuracy is also improved.

Description

technical field [0001] The invention relates to the technical field of wind farm cluster power forecasting, in particular to a short-term power forecasting method for wind farm clusters based on spatio-temporal graph convolutional neural networks. Background technique [0002] Renewable energy, especially wind energy, has become the key to alleviating the energy crisis. In recent years, the installed capacity of wind farms has been increasing day by day, and the connection of wind farms is mostly in the form of clusters. However, due to the volatility and randomness of wind farms, wind power not only brings clean energy, but also poses a certain degree of threat to the safe and stable operation of the power grid. Therefore, the accurate prediction of wind speed and power is an important guarantee to ensure the grid-connected operation of wind farms. The ultra-short-term power forecasting of wind farm clusters requires forecasting the wind power in the next 4 hours at an in...

AI Technical Summary

Problems solved by technology

[0012] The present invention provides a short-term power prediction method for wind farm clusters based on spatio-temporal graph convolutional neural network, which is used to solve the problem that the existing wind farm cluster power prediction cannot be combined with historical power and weather forecast information for future wind farm output power prediction , and the defect that a single graph convolutional network extraction feature cannot extract the hidden information in the time series sequence, by setting the neural network structure used in the model training process, including the bi-directional gated recurrent unit Bi-GRU (Bi-GatedRecurrent Unit) network and As a feature extraction module, the graph convolutional network uses the Bi-GRU network to mine the hidden information of the time series sequence to increase the dimension of the features in the time series sequence, so that the power prediction can consider both historical power and weather forecast parameters, and can also improve prediction accuracy

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