To deal with the problem of fluctuating photovoltaic power

a photovoltaic power forecasting model based on genetic algorithm (GA) and fuzzy radial basis function (RBF) neural network is proposed and the output power is applied to the battery energy storage system to mitigate the electric shock on the power system. A historical day of identical weather type

a close date and minimum temperature Euclidean distance is chosen as the similar day. The solar radiation intensity and temperature closely correlated with photovoltaic (PV) power are chosen as input variables of the model

a GA and fuzzy RBF neural network is built as the final prediction model based on the parameter optimization method of K-means clustering and genetic algorithm. Furthermore

a smooth control strategy considering PV power forecasting is used to control the grid-connected PV power

so as to smooth the PV power fluctuation. The experimental results show that the proposed forecasting model has high accuracy and the smooth control strategy for power fluctuation based on photovoltaic power forecasting is effective. This work is supported by Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) of China (No. 20110008110042) and National Natural Science Foundation of China (No. 51477174).