Blending of a novel all sky imager model with persistence and a satellite based model for high-resolution irradiance nowcasting

Abstract

High shares of variable energy sources such as photovoltaics (PV) make balancing network load and generation increasingly challenging. Electricity grids with high PV-penetration benefit from the consideration of intra-minute and intra-hour variabilities via nowcasts (shortest-term forecasts).In this study we present a novel all-sky imager (ASI) nowcasting system which is benchmarked against an established ASI method, a satellite nowcasting system and persistence. Nowcasts of our novel ASI method, satellite method and persistence are subsequently combined to a hybrid model with improved accuracy.ASI systems exploit sky images from fisheye cameras to analyze sky conditions, predict upcoming cloud situations and derive irradiance nowcasts from these. Our ASI method uses a novel machine-learning (ML) based approach that makes direct use of pixel values and other image features. Measurements from a spatially distributed irradiance measuring network of eight stations within a radius of 10km around the camera position are used to train our ASI and our hybrid model and validate our nowcasting results. In the evaluation we put a special focus on irradiance variability conditions. The high share of stable cloud situations within our dataset (∼68 %) results in a relatively good performance of persistence nowcasts. In higher variability situations persistence is quickly outperformed by all other nowcasting methods.Our ASI method clearly outperforms the reference ASI method at all lead times (LTs). It moreover exhibits a significantly lower root mean square error (RMSE) than the satellite-based method up to LT≤11min ahead and a lower mean absolute error (MAE) throughout the entire interval. The hybrid model brings about an RMSE improvement score of 5-13% over the respective optimal individual method.