Wind power forecast based on broad learning system and simplified long short term memory network

Abstract

Due to its strong randomness and volatility, the modes of wind power are complex. After decomposing wind power time series into three subseries, the complex modes are deconstructed and each subseries maintain unique characteristics. Aiming at the characteristics of each subseries, a wind power forecast method based on Broad Learning System (BLS) and Simplified Long Short Term Memory (SLSTM) is proposed. Firstly, the decomposed subseries is analysed. The first layer subseries reflects the main amplitude changes caused by ramp events and the second represents the local fluctuations accompanying ramp events. Due to the randomness of ramp events, new modes appear frequently in the two subseries. The third layer subseries consists of continuous irregular jitter. Then, the BLS is introduced to forecast the first and second layer subseries. By adjusting its structure dynamically, various modes of ramp events can be learned from amplitude change and accompanying fluctuation. Additionally, according to the characteristics of the third layer subseries, continuous jitter needn't be transmitted in long-term memory. Setting the forget gate to 0, SLSTM is proposed to forecast the third layer subseries. Finally, the wind power data from the Belgian ELIA website is used to verify the superiority of the proposed method.