Evolving Radial Basis Function Neural Networks for One-Day-Ahead Hourly Forecasting of PV Power Generation

This paper proposes a novel method to predict one-day-ahead hourly photovoltaic (PV) power generation. The proposed method comprises three stages: data classification, training and forecasting. In the first stage, a fuzzy k-means algorithm is used to classify the historical data for daily PV power generation into various weather types. In the second stage, five training models are established, according to the verbal weather forecast of the Taiwan Central Weather Bureau (TCWB). Each training model is constructed using a radial basis function neural network (RBFNN), for which the parameters of each RBFNN, including the position of RBF centers, the width of the RBFs and the weights between the hidden and the output layers, are optimized using a harmony search algorithm (HSA). To select an adequate forecasting model from the trained models, fuzzy inference is used in the forecasting stage. The proposed approach is tested on a practical PV power generation system. The results show that the proposed method provides better forecasting results than the existing methods over one-year testing data.