A hybrid method for one-day ahead hourly forecasting of PV power output

This paper proposes a hybrid method combining support vector regression (SVR) and fuzzy inference method for one-day ahead hourly forecasting of photovoltaic (PV) power output. The proposed method comprises training stage and forecasting stage. In the training stage, a number of SVR models are used to learn the collected input/output data sets. To achieve accurate forecast, the fuzzy inference method is used to select an adequate trained model in the forecasting stage, according to the weather information collected from Taiwan Central Weather Bureau (TCWB). The proposed approach is verified on a practical PV power generation system. Numerical results show that the proposed approach achieves better forecasting accuracy than the simple SVR and traditional artificial neural network (ANN) methods.