Interpretable Short‐Term Electric Load Forecasting

The reliable and efficient operation of power systems hinges on accurate electrical load forecasts over various horizons and objectives, ranging from long-term grid adequacy estimation to day-ahead system scheduling and real-time dispatching. In this context, deep learning (DL) models outperform conventional statistical approaches at the expense of interpretability, making the research on interpretable algorithms essential. Among these methods, the temporal fusion transformer (TFT) has proven its validity across multiple sectors, while its applicability to short-term load forecasting (STLF) lacks sufficient corroboration. This study evaluates the TFT within a multivariate STLF task concerning a department building of an Italian university, focusing on interpretability. The performance of the model is evaluated against established benchmarks: The TFT outscores all competitors, yielding a mean absolute percentage error of 7.40% on the test subset, 25% lower than the competitors. Jointly, the interpretability of the model is studied: Variable selection weights identify the most important features, a comprehensive investigation of attention patterns highlights the most critical timesteps for generating forecasts, and the Comaniciu distance helps detect regime shifts and significant events. Overall, this study presents an effective TFT-based model for day-ahead STLF in terms of interpretability and forecasting performance.