Wind speed forecasting models based on data decomposition, feature selection and group method of data handling network

Abstract

High precision wind speed forecasting plays an important role in the process of wind power conversion. In the study, a hybrid wind speed forecasting structure is proposed based on signal decomposition, feature selection and GMDH network. Several decomposition methods including the ensemble empirical mode decomposition, the wavelet packet decomposition, and two novel secondary decomposition method are utilized to decompose the original wind speed into several subseries, respectively. The binary-coded genetic algorithm is adopted to optimize the input features and arrange the input-output structure for the built predictors. The selected variables are put into the GMDH network to build the forecasting model for each subseries. According to the comparison results over 1-step to 6-step predictions, it can be concluded that the proposed models can get satisfactory results in wind speed forecasting.