Physics-Informed Data-Driven Models for Electricity Load Forecasting in Complex Energy Systems
Abstract
The ongoing energy transition, driven by the increasing electrification of end-use sectors, the widespread deployment of renewable energy sources, and the decentralization of power generation, is modifying the operation of modern energy systems. In this context, accurate day-ahead electricity load forecasting has become essential for ensuring system reliability, operational efficiency, and effective energy management. While data-driven machine learning techniques have demonstrated remarkable predictive capabilities, their performance often deteriorates under rare operating conditions, and they generally lack physical interpretability. This work reviews the evolution of electricity load forecasting methods and proposes a physics-informed data-driven framework to investigate how persistent forecasting errors can be reduced by embedding physically meaningful information into machine learning models. The approach is validated on the Italian power system using three years of historical data and evaluated using two complementary learning paradigms, namely gradient boosting and deep learning. Results show that enriching data-driven models with physical variables improves forecasting accuracy, with XGBoost achieving the best performance and reducing MAE from 968 to 792 MW through meteorological and spatial information. XGBoost also provides a competitive performance level with respect to the official forecasts available for the Italian system, while simultaneously outperforming LSTM…
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