Study on Ultra-short-time Power Forecast of Photovoltaic System based on Ground-based Cloud Image Recognition and Key Impact Factors

Abstract

In recent years, under the dual pressure of resource shortage and environmental pollution, the photovoltaic (PV) power generation industry has flourished. The irradiance forecasting technology of PV power plants is of great significance for output prediction, grid dispatching and safe operation. Cloud cover is always the key factor making the irradiance fluctuate. In this article, colorful ground-based cloud images are collected by the all-sky imager every minute as the research object. Based on the traditional threshold method, a hybrid entropy threshold method is proposed to identify cloud clusters. Using the correlation analysis, among many impact factors with high correlation, five are extracted as input parameters of a BP network optimized by genetic algorithm (GA-BP). Through verification and comparison analysis, it is concluded that the recognition accuracy of the hybrid entropy threshold method is higher, and the average relative error can be controlled at about 5%. Based on this, the irradiance prediction of GA-BP also achieved better results than other models. It can meet the application requirements of PV power plants.