Photovoltaic Power Forecasting

Photovoltaic power forecasting (PVPF) aims to accurately predict solar energy generation, crucial for efficient grid management and renewable energy integration. Current research heavily emphasizes the development and optimization of machine learning models, including transformers, recurrent neural networks (like GRUs and LSTMs), and ensemble methods, often incorporating advanced techniques like neural architecture search to automatically design optimal architectures. These advancements focus on improving forecast accuracy across various time horizons and resolutions, particularly for distributed PV systems, and are driven by the need for reliable predictions to enhance grid stability and market operations. The ultimate goal is to provide more precise and robust forecasts, leading to better resource allocation and reduced reliance on fossil fuels.