Hybrid Ensemble Framework for Short-Term Wind Speed Forecasting - Zhenhao Tang; Gengnan Zhao; Gong Wang; Tinghui Ouyang (IEEE Access, 2020)
A novel hybrid ensemble framework is developed to forecast the short-term wind speed, which consists of a data preprocessing technique, data-driven based forecasting algorithms, and an improved Jaya algorithm. In the data preprocessing process, the pauta criterion is employed to find out the outliers, and the variational mode decomposition algorithm decompose the original series to extract the trend and time-frequency information of the historical inputs. The data-driven forecasting algorithms, such as BP, LSSVM, ANFIS, and Elman, are exploited as the original predictor of the framework, while the weights of the predictors are computed by an improved optimization algorithm-CLSJaya. Based on the experimental results of two time-scale datasets from three sites, the proposed framework successfully overcomes the limitations of the individual forecasting models and achieves promising forecasting accuracy.