Short-term output power forecasting of photovoltaic systems based on the deep belief net

Abstract

Photovoltaic power is now a major green energy resource, and its generated power can be directly connected to the power grid. However, the stability of power grid may be affected by the random and intermittent characteristics of photovoltaic power. In order to solve this problem, a forecasting model based on the deep belief nets is proposed. First, affecting factors of photovoltaic power generation are studied, including solar radiation intensity, air temperature, relative humidity, and wind speed. Based on the correlation coefficient between output power and each factor, the most influential factors can be determined and used as inputs of the proposed forecasting model for training process. Second, the forecasting model is then established and applied to predict the photovoltaic output powers for 2 weeks in summer and winter, respectively. The mean absolute percentage error, mean squared error, and Theil's inequality coefficient are used to evaluate the performance efficiency between the proposed deep belief net model and back propagation neural network model. The performance outcomes reveal that the proposed deep belief net model can improve the prediction errors with rapid convergence significantly, better than the back propagation model.