Wind Power Forecast by Using Improved Radial Basis Function Neural Network

摘要

Forecasting wind speed or wind power generation is indispensable for the effective operation of a wind farm, and the optimal management of its revenue and risks. This paper proposes an improved radial basis function neural network structure for forecasting the wind power generation. Results are then compared with back propagation neural network (BPNN), BPNN with Levenberg-Marquardt (BPNN-LM), radial basis function neural network (RBFNN), and the actual measured wind power outputs. Test results show that the presented model can provide more accurate and stable time-horizons forecasting.