Intra-day solar irradiation forecast using machine learning with satellite data

Abstract

Northwest China is rich in solar resource and the photovoltaic manufacturing industry is developing rapidly. Accurate solar radiation forecast suitable for the northwest desert area has become an urgent need. In this work, we use the classical machine learning models (SVM and RF) that are more complex than traditional statistical models to forecast intra-day global solar irradiance (GHI). Since there are some uncertainties in solar radiation attenuation models. We explore an approach that requires little preprocessing to enter satellite data as input: the mean of the satellite image window. Such a process provides a direct GHI forecast without using the clear sky index as a proxy. The model includes several satellite channels, not only visible channels. Since China's Fengyun-4 series satellites (FY-4) are the new generation of stationary meteorological satellite and have not yet been fully tested and applied to solar irradiance prediction, the regional average of each channels (Channel01∼Channel07) of FY-4A satellite cloud image are taken as important parameters input of the ML model, with lead time from 10 min to 3 h. The combination of climatology and persistence (Clim-Pers) model is chosen as the benchmark model. Our cases studies in Yuzhong, Minqin and Dunhuang show that the FS of RF model is higher than the SVM model in all forecast cases, and the performance advantage becomes more obvious when the lead time beyond 90 min. The FS values of RF model in Yuzhong, Minqin and Dunhuang site at time horizons 10 min-3 h are 13.5–37.6%, 18.2–35.8% and 17.3–34.2%, respectively, the forecast performance is very stable in different climate types. Therefore, it is a good and simple way to improve the accuracy of ultra-short-term solar forecasting by introducing satellite observations into the ML model.