LAUSR

Wind speed forecasting plays a vital role in power system management, planning and integration. In previous studies, most forecasting models have focused on improving the accuracy or stability of wind speed forecasting. However, for an effective forecasting model, considering only one criterion (accuracy or stability) is insufficient. In this paper, a novel combined forecasting model was proposed and successfully employed to solve the problem of simultaneously obtaining both high accuracy and strong stability in wind speed forecasting. The proposed model consists of four ANNs (artificial neural networks) with optimum weight coefficients based on MOBA (multi-objective bat algorithm). MOBA overcomes the defect that only one criterion can be achieved by single objective optimization algorithms. In addition, data decomposition and de-noising are also incorporated in the data pre-processing stage. Ten-minute wind speed data from three datasets in Penglai, China, were selected for multi-step ahead forecasting to evaluate the effectiveness of the developed combined model. The experimental results indicate that the combined model outperforms other comparison models for generating forecasts in terms of forecasting accuracy and stability.