Short‐term wind speed multistep combined forecasting model based on two‐stage decomposition and LSTM
Xuechao Liao, Zhenxing Liu, Wanxiong Deng
Abstract
Abstract In order to better extract and study the characteristics of the wind speed in time‐domain and frequency‐domain, so as to solve the time‐domain randomness and frequency‐domain complexity problems of the wind speed signal, a combined short‐term prediction model (WD‐VMD‐DLSTM‐AT), which is based on two‐stage decomposition (WD + VMD), double long‐short‐term memory network (DLSTM) and attention mechanism (AT), is proposed; on this basis, a multi‐input multiple output (MIMO) codec model based on attention mechanism (MMED‐AT) is proposed for multiple short‐term wind speed step forecast. Through experimental comparison and analysis, the proposed combined forecasting model has the smallest statistical error and the best prediction accuracy; the MMED‐AT models based on the combined model can obviously eliminate the cumulative error of recursive multistep prediction and further improve the stability of multistep prediction.