In recent years, the liberalization of the electricity market and the increasing penetration of distributed energy generation based on renewable sources have contributed to expand the uncertainty in the energy commodities trend. Effective methods able to make accurate predictions about electricity price and demand, as well as forecasting of the amount of power produced by photovoltaic systems and wind generators, would have a beneficial impact on the system management and would maximize profits for the actors involved in the energy exchange market. This work aims to test and compare different Machine Learning methods (Linear Regression, Support Vector Regression and Neural Networks) to obtain predictions of two energy commodities: zonal electricity price and PV power production.