An optimized complementary prediction method based on data feature extraction for wind speed forecasting

Abstract

Wind speed prediction has a great significance to energy development. However, accurate wind speed prediction is a challenge owing to characteristics of wind series: randomness, fluctuation and nonlinearity. In this study, an optimized decomposed and ensemble multi-feature complementary forecasting method for wind speed is proposed, that mainly includes feature extraction and reconstruction, complementary prediction and nonlinear integration optimization. Firstly the Stacked Autoencoder (SAE) algorithm reconstructs characteristic series of wind speed sequence decomposed by the Variational Mode Decomposition (VMD), and then the Cuckoo Search (CS) algorithm finds weights of Linear Weighted Sum Method (LWSM) which integrates fitting results of the Support Vector Regression (SVR) and Bidirectional Long Short-term Memory (BiLSTM), finally the Bidirectional Gated Recurrent Unit (BiGRU) integrates the results of the Linear Weighted Sum Method, which corrects the errors of the complementary prediction results to optimize the prediction results. Two wind speed sequences are used to verify the proposed method obtained from Hexi Corridor area, China. Compared with other models, the model proposed in this paper has higher accuracy and stronger generalization ability than others.