Abstract
Surface solar radiation is affected by many random mutation factors, which makes the ultra-short-term prediction face great challenges. In this paper, the surface radiation observation station in the northwest (Dunhuang) desert area with broad PV prospects is selected as the research object. The input parameters of the test sample are: cloud forecast value, reflectivity and brightness temperature value of a satellite cloud image closest to the forecast time. The MATLAB software is used to model the prediction program and to predict the surface solar radiation in the next 10 minutes. A combined algorithm of satellite cloud images and neural network is applied to predict surface solar radiation for the next 10 minutes and is compared with the measured surface solar radiation. The model is a lightweight calculation model, it satisfies the calculation precision of engineering requirements. The results show that the diurnal variation trend of measured and predicted radiation values is basically the same. Among them, the prediction accuracy of the model for cloudy days is higher, while for snowy days with more abrupt changes, the prediction error of abrupt points is larger. The model can provide reference for ultra-short-term prediction of surface radiation.
Highlights
Due to the limited reserves of petroleum, coal and other resources, the development of new energy is an inevitable trend
The difficulty in the study of ultra-short term prediction of surface solar radiation lies in the movement and dissipation of clouds
The observation of clouds is mainly based on ground cloud map and satellite cloud map
Summary
Due to the limited reserves of petroleum, coal and other resources, the development of new energy is an inevitable trend. Chen et al [1] used ground-based cloud map and neural network to model surface radiation prediction. Steven et al [4] established a short-term (0-3h) surface solar radiation prediction method by using geostationary satellite observation and numerical weather forecast. Rosiek et al [5] used satellite observation and artificial neural network to establish online 3-hour power output forecast of photovoltaic building integration system. The difficulty in the study of ultra-short term prediction of surface solar radiation lies in the movement and dissipation of clouds. Northwest China has abundant solar energy resources and broad prospects for developing photovoltaic power stations. This study can provide reference for ultra-short term radiation prediction of photovoltaic power generation
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