PV power forecasting using an integrated GA-PSO-ANFIS approach and Gaussian process regression based feature selection strategy

This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic (PV) installations. An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation. A day-ahead, hourly mean PV power generation forecasting method based on a combination of genetic algorithm (GA), particle swarm optimization (PSO) and adaptive neuro-fuzzy inference systems (ANFIS) is presented in this study. Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant; and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant. The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing. Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches. The proposed approach outperformed existing artificial neural network (ANN), linear regression (LR), and persistence based forecasting models, validating its effectiveness.