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.

翻译：近年来，时间序列基础模型（TSFMs）作为通用预测模型兴起，并展现出在能源系统中的巨大应用潜力。然而，在电网等关键基础设施应用中，为确保可信度与可靠性，模型必须具备透明度，不能完全依赖黑箱模型。为提升TSFMs的透明度，我们提出一种针对该类模型高效计算沙普利加法解释（SHAP）的算法。该方法利用TSFMs在输入上下文长度和协变量方面的灵活性，从而实现高效的时序与协变量掩码（选择性屏蔽输入），进而借助SHAP对模型预测进行可扩展解释。我们以某输电系统运营商（TSO）日前负荷预测任务为场景，评估了Chronos-2与TabPFN-TS两种TSFMs。在零样本设置下，两个模型的预测性能均能与专门基于多年TSO数据训练的Transformer模型相媲美。通过所提方法获得的解释与领域知识高度一致，特别是当TSFMs能恰当利用天气和日历信息进行负荷预测时。总体而言，我们证明了TSFMs可作为运营能源预测中透明且可靠的工具。