Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations. Yet, it remains a dataset-specific task, requiring comprehensive training data, limiting scalability, and resulting in high model development and maintenance effort. Recently, foundation models aiming to learn generalizable patterns via extensive pretraining have shown strong performance in multiple prediction tasks. Despite their success and strong potential in energy forecasting, a systematic, use-case-differentiated evaluation is still missing. We address this gap by presenting the Foundation Models in Energy Time Series Forecasting (FETS) benchmark. We (1) provide a structured overview of energy forecasting use cases along three main dimensions, i.e., stakeholders, attributes, and data categories, (2) curate 54 datasets across 9 data categories, guided by typical stakeholder interests, and (3) benchmark foundation models against task-specific machine learning across different forecasting settings. In our benchmark study, covariate-informed zero-shot foundation models perform best in aggregate, with Chronos-2 attaining the lowest overall median NRMSE (0.472), closely followed by TiRex-2 (0.474). Both perform better than XGBoost (0.611) and random forest (0.696), although they were trained task-specifically on the full historic target data. Further analysis reveals a strong correlation between predictive performance and spectral entropy. Performance saturates beyond a certain context length and improves with aggregation level, e.g., for national load, district heating, and power grid data. Overall, with the lowest median error, limited data requirements, and low inference and hardware demands, foundation models reduce development and maintenance effort, emerging as scalable and generalizable energy forecasting solutions.

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