Multi-step ahead wind speed prediction based on a two-step decomposition technique and prediction model parameter optimization

Abstract

Accurate and reliable wind speed prediction is essential for the exploitation and utilization of the wind energy. In this paper, a novel hybrid multi-step wind speed prediction model is proposed based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), robust local mean decomposition (RLMD), and improved whale optimization algorithm (IWOA), and long-term and short-term memory network (LSTM). CEEMDAN is utilized to decompose the wind speed series into a number of intrinsic mode functions, and the RLMD is used to the second step decompose the most non-stationary intrinsic mode function into a series of product functions. After the two-step decomposition, a group of new subsequences is formed. The long-term and short-term memory network (LSTM) prediction model is constructed for every subsequence and an improved whale optimization algorithm (IWOA) is used to optimize key parameters affecting the prediction performance of the LSTM model. And at last, the subsequence prediction results are superimposed to provide the final prediction results. The effectiveness and advancement of the proposed hybrid model is verified by employing wind speed data from two different wind farms. And according to the experimental results and comparison, it can be found that the proposed model can provide better performance than the seven compared models.