Knowledge Mining Based on Environmental Simulation Applied to Wind Farm Power Forecasting

Considering the inherent variability and uncertainty of wind power generation, in this study, a self-organizing map (SOM) combined with rough set theory clustering technique (RST) is proposed to extract the relative knowledge and to choose the most similar history situation and efficient data for wind power forecasting with numerical weather prediction (NWP). Through integrating the SOM and RST methods to cluster the historical data into several classes, the approach could find the similar days and excavate the hidden rules. According to the data reprocessing, the selected samples will improve the forecast accuracy echo state network (ESN) trained by the class of the forecasting day that is adopted to forecast the wind power output accordingly. The developed methods are applied to a case of power forecasting in a wind farm located in northwest of China with wind power data from April 1, 2008, to May 6, 2009. In order to verify its effectiveness, the performance of the proposed method is compared with the traditional backpropagation neural network (BP). The results demonstrated that knowledge mining led to a promising improvement in the performance for wind farm power forecasting.