A novel wind speed forecasting method based on ensemble empirical mode decomposition and GA-BP neural network

Wind energy is one of the most important renewable energy resources. Wind speed forecasting is a critical tool for wind energy conversion system implementation. However, the uncertainty and intermittency of wind speed always affect the prediction accuracy. This paper proposes a novel wind speed forecasting method based on ensemble empirical mode decomposition (EEMD) and GA-BP neural network. The wind speed data are decomposed into certain signals with different frequencies by EEMD. Each signal is taken as input data to establish GA-BP neural network forecasting model. Final forecasted wind speed data are then obtained by adding up the predicted data of each signal. A case study of a wind farm in Inner Mongolia, China shows that this method is more accurate than traditional GA-BP forecasting approach. The study also shows that method with EEMD is more accurate than that with empirical mode decomposition (EMD).