Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model

Abstract

The output prediction of photovoltaic (PV) power station is necessary because the weather, environment and seasonal factors lead to the unstable PV power generation, which will affect the planning and scheduling of power system. Compared with the single model method, the linear combination method could improve the prediction accuracy of the output of PV power station. However, the linear combination forecast method is a simple convex combination of different prediction methods and is lack of general applicability. This paper presents a nonlinear combination method based on BP neural network and ARMA model to predict the output of PV power plant. This method based on the nonlinear relationship between the results of two single prediction models and the actual value, and utilize the nonlinear fitting ability of BP neural network, predicted the power generation capacity of PV power station. The nonlinear prediction theory and algorithm are given at the end of the article, and also compare nonlinear combined model with linear combined model of the power plant output prediction, the results show that the proposed method has a high accuration and an extensive applicability.