This master’s thesis aims to explore predictive methods for solar power production through a statistical modeling approach, employing novel data-driven algorithms based on historical data from the main photovoltaic plant at the Politecnico di Torino. Chapter 1 provides an overview of renewable energy sources, emphasizing their integration into the energy sector and broader economic and political contexts. The focus then shifts to solar photovoltaic technologies, with a brief review of system components and the basic operating principles of a panel. This leads to a survey of forecasting approaches, from physical and statistical models to those based on artificial intelligence. Chapter 2 focuses on artificial neural networks, beginning with the perceptron model, neuron equation, and activation functions.