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.
Highlights
As the industrialization trend continues to spread throughout the world, active industrial production consumes a lot of resources
If the initialization is not suitable, it will lead to the slow convergence of the BP network and make the model fall into local extremes
In order to prove the effectiveness of genetic algorithm optimization and to prove that the cloud amount extracted by the hybrid entropy threshold method can improve the accuracy of irradiance prediction, four prediction models are established for comparative analysis
Summary
As the industrialization trend continues to spread throughout the world, active industrial production consumes a lot of resources. Studies have proved that when the installed PV generating capacity accounts for more than 15% of the grid, large fluctuations in the PV system will lead to the paralysis of the entire grid [2]. Based on these uncertainty and insecurity, accurate forecasts of solar irradiance and PV output are important and necessary. Cloud cover is a nonnegligible factor that causes fluctuations in PV output It is of great significance for improving forecasting accuracy to figure out how to fully extract the features of ground-based cloud images and combine them with PV system operating parameters and numerical weather forecast information to establish a multi-information fusion model
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