A Short-Term Wind Power Prediction Method Based on Two-way Attention and Quadratic Optimization

Abstract

The present invention proposes a short-term wind power prediction method based on two-way attention and secondary optimization. First, the wind power subsequence, wind speed subsequence, wind direction sine time series and wind direction cosine time series are spliced ​​to form an input tensor, and the input tensor Characterize multiple features, establish a two-way attention-residual network-gated recurrent unit deep learning prediction model, and dynamically select a single input tensor as the training sample of the model for training, on the one hand to ensure that the training samples are more representative, on the other hand On the one hand, the deep learning prediction model based on two-way attention-residual network-gated recurrent unit can realize adaptive learning during feature selection, and the two-way attention mechanism is used to consider the time dimension and feature dimension of the input tensor, which improves the model Sensitivity to important information. Finally, the secondary optimization further improves the generalization performance of the model and can improve the accuracy of short-term wind power forecasting.

Description

technical field [0001] The invention relates to the technical field of short-term wind power prediction, and more particularly, to a short-term wind power prediction method based on bidirectional attention and secondary optimization. Background technique [0002] Wind power prediction refers to the short-term forecast of wind speed of wind farms based on the relevant data of wind farm meteorological information, using physical simulation calculation and scientific statistical methods, and predicting the power of wind farms, so as to realize the power dispatching department's dispatching of wind power. Require. Therefore, improving the prediction accuracy of wind power is very important for power system stability and power quality. The wind power prediction accuracy has a great relationship with the stability of the input signal of the wind power series. The more stable the signal, the higher the prediction accuracy. In fact, the input signal of the wind power series is a n...

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Problems solved by technology

[0004] In order to solve the problem that the traditional feature selection method is difficult to accurately evaluate the impact of different features on the wind power prediction target during short-term wind power prediction, the present invention proposes a short-term wind power prediction method based on two-way attention and secondary optimization. Self-adaptive learning improves the accuracy of short-term wind power forecasting

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