LAUSR

The introduction of solar power in energy grids is crucial for sustainable progress, but gets restricted due to frequently fluctuating electricity outputs generated by intermittent solar irradiance. The problem requires proper power planning and operations management that requires accurate and reliable solar irradiance forecasts. In this context, this research presents a holistic and lightweight framework for solar irradiance forecasting, the critical component for generating photovoltaic (PV) output. The paper’s holistic approach leverages meteorological variables, historical global horizontal irradiance (GHI) data, ground-based sky imager (GSI) images, satellite-derived cloud masks, and satellite-based clear sky data to improve forecasting accuracy. The proposed framework extends the forecasting horizon to 60 min while covering a continuous 6-h historical context. Innovative feature extraction techniques were implemented to reduce the cloud image dimensions, enabling the development of a lightweight forecasting model. The results demonstrate the effectiveness of this approach, contributing to a more reliable GHI forecasting for efficient energy grid management.