Wind power prediction based on periodic characteristic decomposition and multi-layer attention network
Xuechao Liao, Zhenxing Liu, Xiujuan Zheng, Zuowei Ping, Xin He
Abstract
Aiming at the wind power characteristics of temporality, periodicity and complexity, the periodic law of short-term and long-term repetitive patterns is studied, and an integrated dual-channel prediction model is proposed. A practical periodic characteristic extracting strategy is designed to show the hidden periodic law of the original signal. Combining the grid search algorithm with the variation trend of amplitude/period, the optimal periodic step is determined. Based on the above analysis, the original signal is decomposed into temporal and periodic components. Then the temporal attention network and the encoder-decoder attention network are schemed out to dispose the two components respectively. Finally, the linear regression attention network is adopted to realize data fitting. The integrated forecasting framework can deal with the long-term and short-term dependencies of the original data at the same time, and ensure the rapid convergence of training process, thereby improve the prediction accuracy and stability. The multi-dimensional experimental verification is carried out through the comparison of evaluation indicators , prediction trends, scatter plots and box plots.