A Hybrid Predicting Model for the Daily Photovoltaic Output Based on Fuzzy Clustering of Meteorological Data and Joint Algorithm of GAPS and RBF Neural Network

Photovoltaic (PV) output is greatly affected by meteorological factors. If it has no efficient meteorological factors, the prediction accuracy for PV is a little low. Although the Radial Basis Function (RBF) network is already widely utilized in photovoltaic prediction, its prediction error is too large. An algorithm for forecasting the evaluation of the short-term PV output based on fuzzy clustering of meteorological data and a joint algorithm of the Genetic Algorithm Programming System (GAPS) and Radial Basis Function (RBF) is proposed in this paper to increase the prediction accuracy. Selecting the three main types of meteorological data, including atmospheric turbidity, relative humidity, and solar irradiance, as clustering feature vectors of the cluster class and clustering that historical PV outputting data into three groups by an improved fuzzy c-means clustering (IFCM) method are significant in this study. Finally, this research implemented the computational simulation for a real case. Its results show that the proposed model and algorithm work well and can reduce the dimension of the model and improve the prediction accuracy.