Effective Net Load Forecasting in Solar-Integrated Homes with Battery Systems

Household energy saving has become increasingly important, and finding Machine Learning models that allow residents to predict their consumption, particularly when equipped with a renewable energy source, such as photovoltaic (PV) panels, is a central challenge in achieving the goal of greater energy efficiency. Several models and approaches have been currently used to address this challenge. In this paper we present a series of foundational experiments that provide valuable insights into the task of net load forecasting using some well-known models available for regression tasks, i.e. the Multi-Layer Perceptron, CatBoost and XGBoost. We tested these models with the specific aim of assessing their robustness in a scenario where a decreasing time frame is used for their training, and resorting to a household equipped with PV plant and a battery storage system as use case. The results, measured by R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score, show notable performance for both MLP and XGBoost models, even in data-scarce settings, while CatBoost lags behind, proving to be less effective.