Real-Time Solar Irradiance Nowcasting for Renewable Energy Forecasting over Western India

The global transition toward low-carbon energy systems has increased the reliance on renewable energy sources and driven solar power to become a key component of sustainable electricity generation, thereby increasing the importance of accurate irradiance forecasting. As solar penetration grows, power system operations increasingly depend on reliable short-term forecasts to support grid balancing, reserve allocation, and real-time decision-making. Global Horizontal Irradiance (GHI) represents the integrated influence of atmospheric conditions and cloud processes on surface solar radiation and governs short-term variability in photovoltaic power output. However, rapid cloud evolution introduces strong spatiotemporal variability in GHI, making accurate prediction at sub-hourly lead times a persistent challenge for short-term solar forecasting. In this study, we develop a real-time nowcasting system to predict GHI over the western region of India at 15-minute resolution with effective lead times of up to 2 hours. The system is based on a convolutional long short-term memory (ConvLSTM) model that learns spatiotemporal cloud–radiation relationships from high-frequency geostationary satellite observations. We utilize INSAT-3DR and INSAT-3DS products obtained from the MOSDAC archive, which provide continuous monitoring of cloud evolution over the region. The nowcasting framework is implemented using routinely available satellite observations and is evaluated over a large spatial domain covering western India, a region characterized by strong seasonal variability and diverse cloud regimes associated with pre-monsoon, monsoon, and post-monsoon periods. The results demonstrate consistent performance across seasons and show that the system captures the mean diurnal evolution of GHI with stable skill during daytime solar-active periods. Evaluation results indicate mean absolute errors of approximately 60 W m-2 for 1–2 hour lead times and 72 W m-2 for 2–3 hour lead times, corresponding to about 7–12 % of typical daytime GHI under moderate to high irradiance conditions. Overall, this work demonstrates the feasibility of satellite-driven deep learning systems for real-time GHI nowcasting and highlights the potential of integrating geostationary satellite observations and spatiotemporal learning models to support renewable energy forecasting and real-time grid decision-making in regions with high and growing solar power penetration.