Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural network

With the sharp consumption of fossil energy and deterioration of ecological environment, wind power, as a clean and renewable energy resource, gains more and more attention from all over the world. However, due to the intermittency and stochastic nature of wind speed, an accurate wind power/speed forecasting is not only crucial for availably dispatching the wind power resource but also has a direct relationship with the State Grid operation safely and steadily. In this paper, an innovative hybrid wind speed forecasting model, including fast ensemble empirical mode decomposition, sample entropy, phase space reconstruction and back-propagation neural network with two hidden layers, is proposed to enhance the accuracy of wind speed prediction. The data is firstly preprocessed by fast ensemble empirical mode decomposition and sample entropy. Subsequently, the prediction model called improved back-propagation neural network is built to forecast the sub-series, whose inputs and outputs are obtained in accordance to phase space reconstruction. To verify the effectiveness and advancement of the proposed model, the paper chooses the real data from two wind farms located in different site in China for experiments. Comparing with the benchmark models, the proposed model shows a better performance in short-term wind speed forecasting.