A Sample Entropy Parsimonious Model Using Decomposition-ensemble with SSA and CEEMDAN for Short-term Wind Speed Prediction.

Data processing and integrated forecasting strategy has always been a major obstacle to the development of wind power forecasting system. In view of this, a novel decomposition method with SSA and CEEMDAN is constructed to decompose the original data, and also a sample entropy parsimonious integration model is applied to achieve ultra-short term wind speed prediction. Considering the respective data characteristics of each subsequence, we divide the decomposed multiple subsequences into three parts: high complexity group, low complexity group and residual group. PSO-ELM, IHOA-LSSVR, and IHOA-LSTM are applied to predict them respectively. Compared with other models with high accuracy in this paper, our model has higher prediction accuracy. [ABSTRACT FROM AUTHOR]