RBF Neural Network Combined with Knowledge Mining Based on Environment Simulation Applied for Photovoltaic Generation Forecasting

Photovoltaic generation forecasting is one of the main tasks of the planning and operation in power system. Especially with the development of mico-grid, relative study on renewable energy generation gain more and more concerns. In this paper, a short-term forecasting model combining knowledge and intelligent algorithm is developed for photovoltaic array generation. Self-organizing map (SOM) 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). The historical data is classified into several groups, though which we could find the similar days and excavate the hidden rules. According to the data reprocessing, the selected samples will improve the forecast accuracy radial basis function network (RBF) trained by the class of the forecasting day is adopted to forecast the photovoltaic output accordingly. A case study is conducted to verify the effectiveness and the accuracy. Compared with the conventional BP neural network, the forecasting results demonstrate the method proposed in this paper can gain better forecasting performance with higher accuracy. Copyright © 2013 IFSA.