Short-Term Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory

Abstract

The forecast for photovoltaic (PV) power generation is of great significance for the operation and control of power system. In this paper, a short-term combination forecasting model for PV power based on similar day and cross entropy theory is proposed. The main influencing factors of PV power are analyzed. From the perspective of entropy theory, considering distance entropy and grey relation entropy, a comprehensive index is proposed to select similar days. Then, the least square support vector machine (LSSVM), autoregressive and moving average (ARMA), and back propagation (BP) neural network are used to forecast PV power, respectively. The weights of three single forecasting methods are dynamically set by the cross entropy algorithm and the short-term combination forecasting model for PV power is established. The results show that this method can effectively improve the prediction accuracy of PV power and is of great significance to real-time economical dispatch.