Integrating Gray Data Preprocessor and Deep Belief Network for Day-Ahead PV Power Output Forecast

Abstract

Generation output forecasting is a crucial task for planning and sizing of a photovoltaic (PV) power plant. The purpose of this paper is to present an effective model for day-ahead forecasting PV power output ofa plant based on deepbelief network (DBN) combined with gray theory-based data preprocessor (GTDBN), where the DBN attempts to learn high-level abstractions in historical PV output data by utilizing hierarchical architectures. Test results obtained by the proposed model are compared with those obtained by other five forecasting methods including autoregressive integrated moving average model, back propagation neural network, radial basis function neural network, support vector regression, and DBN alone. It shows that the proposed model is superior to other models in forecasting accuracy and is suitable for day-ahead PV power output prediction.