# Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models Source: [Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models]() Collection index: [llms.txt]() Data updated: 2026-05-02T05:00:10.851Z Authors: Matthias Hertel, Alexandra Nikoltchovska, Sebastian P\\"utz, Ralf Mikut, Benjamin Sch\\"afer, Veit Hagenmeyer Published: 2026-04-30T17:36:24.000Z ## Abstract Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in energy systems. However, applications in critical infrastructure like power grids require transparency to ensure trust and reliability and cannot rely on pure black-box models. To enhance the transparency of TSFMs, we propose an efficient algorithm for computing Shapley Additive Explanations (SHAP) tailored to these models. The proposed approach leverages the flexibility of TSFMs with respect to input context length and provided covariates. This property enables efficient temporal and covariate masking (selectively withholding inputs), allowing for a scalable explanation of model predictions using SHAP. We evaluate two TSFMs - Chronos-2 and TabPFN-TS - on a day-ahead load forecasting task for a transmission system operator (TSO). In a zero-shot setting, both models achieve predictive performance competitive with a Transformer model trained specifically on multiple years of TSO data. The explanations obtained through our proposed approach align with established domain knowledge, particularly as the TSFMs appropriately use weather and calendar information for load prediction. Overall, we demonstrate that TSFMs can serve as transparent and reliable tools for operational energy forecasting. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Day-ahead electrical load forecasting on TransnetBW (test)](): Day-ahead electrical load forecasting; TransnetBW (test); MAE: not recorded. ## Original sources and implementation [Original paper]() [PDF]()