Wind Power Prediction Based on Artificial Neural Network
FAN Gao-feng, WANG Wei-sheng, LIU Chun, DAI Hui-zhu (China Electric Power Research Institute, Haidian District, Beijing 100192, China)
Wind power prediction is important to the operation of power system with comparatively large mount of wind power. The wind power prediction methods were classified into several kinds. An artificial neural network (ANN) model for wind power prediction was constructed according to the wind power influence factors. Then the impacts of real time measured power and the atmospheric data at different heights on prediction results were analyzed. Besides, another ANN model for error band prediction was also built. The results indicate that the ANN structure and the training sample have some impact on the prediction precision. The real time measured power as input will improve the precision of 30 min ahead prediction, however will decrease the precision of 1h ahead prediction. The results which using the atmospheric data at all different heights as input have a higher accuracy when compared with the results using hub height data only. The designed ANN can forecast the error band.