Solar Generation

Accurate solar power generation forecasting is crucial for efficient grid integration and renewable energy management. Current research heavily utilizes machine learning, particularly deep learning architectures like convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and ensemble methods, often incorporating weather data, air quality indices, and even satellite imagery to improve prediction accuracy and address challenges like spatial variability and data scarcity. These advancements aim to enhance the reliability and predictability of solar energy, enabling better grid planning, resource allocation, and ultimately, a smoother transition to sustainable energy systems. Probabilistic forecasting methods are also gaining traction to better quantify uncertainty in predictions.