Performance study of affine transformation and the advanced clear-sky model to improve intra-day solar forecasts

Solar irradiance forecasting is one of the most efficient methods to handle the potential problems caused by the large and frequent photovoltaic fluctuations. For the satellite-based forecasting method, the atmospheric attenuation is paid lesser attention than other parts (notably the cloud effects). This study aims to explore the possibility of improving irradiance forecasting by using an advanced clear-sky model (i.e., the McClear model) and the running-window based affine transformation with local measurements. The McClear model notably aims at accounting for aerosol and water vapor intraday variabilities, in contrast with the European solar radiation atlas (ESRA) model based on climatological monthly means of Linke turbidity. The affine transformation with a running window of few days in the sliding past can serve as a correction procedure and has the potential to lower the impacts by inaccurate atmospheric estimation. Irradiance forecasting is carried out at lead times from 15 min to 3 h at an interval of 15 min, based on China's second-generation geostationary satellite Fengyun-4A. The measure-oriented and distribution-oriented approaches are used for a comprehensive verification. The results show that without affine transformation, the forecasting model with the McClear model outperforms that with the ESRA model, due to better estimations of atmospheric attenuation. On the other hand, affine transformation significantly improves the forecasting models. Overestimations still exist but are significantly reduced to the range of 2%-5.5%. After affine transformation, the forecasting models achieve very close performances no matter which clear-sky model is implemented, except that forecasts with the McClear model are much better calibrated at a high irradiance level (i.e., 900 W/m(2)).