Harnessing Multivariate AI to Enhance Hydropower Generation Forecasting

Renewable energy resources are essential in combating climate change, pollution, and the depletion of fossil fuel reserves. The effective management of these resources, including planning future energy availability, optimizing energy sales, and scheduling plant maintenance, necessitates the use of forecasting models. However, forecasting renewable energy production is challenging due to the complex relationship between energy generation and external factors, such as weather conditions. This study focuses on the development of multivariate machine learning models, using historical weather data, tailored for hydropower forecasting, with a specific emphasis on hydropower generated in plants integrated into Water Distribution Systems-a domain that has remained largely unexplored in scientific literature until now. To validate the approach, real data from a hydroelectric plant in Sicily, Italy, were utilized. The study developed and compared different machine learning forecasting models, demonstrating the importance of using weather-related factors for accurate forecasting.