The scope of the project consists of the process of building, training and validating a model, developed in R language, capable of predicting the energy generation of a photovoltaic plant installation from the data available in the installation that is going to be used as inputs for the model. The model will be developed using statistical learning methods and can predict the forecasts for a specific number of hours introduced by the user, with a maximum of twenty-four hours previsions in advance. The model’s predictions will be used in a model predictive control system to couple the forecasted energy generation with the consumption of a nearby building. All the infrastructure for obtaining and processing data and for the execution of the code is installed and available and it doesn’t need any change to be applied to the development of the project, so it isn’t the scope of the project it’s modification. The process of building, training and validating the model will be performed with the available data collected during the period between 15/10/2019 and 31/01/2022. The process of validating the model will be done with statistical indicators capable to evaluate the performance of the model objectively. The process of using the model to produce real forecasts of energy generation will depend in the availability of the meteorological forecasts of the data of the installation that will be used as inputs of the model and it isn’t the scope of this project to obtain this forecast
