Solar Nowcasting

Solar nowcasting focuses on predicting solar energy production over short timescales (minutes to hours), crucial for integrating intermittent solar power into electricity grids. Current research emphasizes developing accurate and efficient nowcasting models using diverse data sources, including satellite imagery (geostationary and multispectral), ground-based sky images, and weather data, often employing machine learning techniques such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and graph neural networks (GNNs). Improved nowcasting capabilities are vital for optimizing grid management, reducing reliance on fossil fuel-based reserve power, and enhancing the overall efficiency and sustainability of renewable energy systems.