PV Advancements & challenges. Forecasting techniques, real applications, and grid integration for a sustainable energy future

The rapid increase in solar power generation requires accurate forecasting of photovoltaic (PV) energy production for effective grid integration and energy market participation. This paper presents state-of-the-art forecasting techniques and models, including statistical and time series models, machine learning models, deep learning models, probabilistic forecasting models, and data-driven feature engineering and selection. In addition, applications of PV energy production forecasting in grid integration and energy market operations such as load balancing, generation planning, transmission and distribution planning, energy storage optimisation, bidding strategies, and renewable energy certificate trading are presented. Furthermore, challenges persist in PV power generation forecasting are presented, including data quality and availability, model uncertainty, forecast horizon and time resolution, and integration of multiple data sources and models. Finally, this article focusses on providing insight into the current PV energy production forecasting landscape and guiding future research and development efforts to address these challenges, enhance forecasting capabilities, and facilitate the global transition to a more sustainable and low-carbon energy future.