A novel compound wind speed forecasting model based on the back propagation neural network optimized by bat algorithm

Abstract

Wind power, a clean and renewable resource, is regarded as one of the most promising and economical resources during the transformation from fossil fuels to new energy resources. Thus, the accuracy of wind speed forecasting work is very important to integrate the wind resource into electrical power system on a large scale. To improve the short-term wind speed forecasting accuracy, a novel compound model is introduced in this paper. For the proposed model, the fast ensemble empirical mode decomposition method was employed to do the data preprocessing. After the data preprocessing, phase space reconstruction was used for choosing each sub-series’ input and output vectors for the forecasting model dynamically. Then, the bat algorithm was applied to optimize the connection weights and thresholds of the traditional back propagation neural network. The forecasting results can be obtained through the aggregation of sequential prediction. The performance evaluation of this proposed model indicates that it can capture the nonlinear characteristics of the wind speed signal efficiently. The proposed model shows better performance when being compared with the parallel models.