Electricity price forecasting in Europe presents unique challenges due to the continent's increasingly integrated and physically interconnected power market. While recent advances in foundation models have led to substantial improvements in general time series forecasting, most existing approaches do not incorporate prior graph knowledge from the transmission topology, which can limit their ability to exploit meaningful cross-region dependencies in interconnected power systems, motivating a domain-specific foundation model. In this paper, we address this gap by first introducing a comprehensive and up-to-date dataset across 24 European countries (38 regions), spanning from 2022-01-01 to 2026-01-01. Building on this groundwork, we propose PriceFM, a probabilistic foundation model pretrained on this large dataset. Specifically, PriceFM maps each region's price and exogenous features into a comparable latent embedding via a shared Mixture-of-Experts (MoE) projection layer, then injects prior graph knowledge by constructing a sparse graph mask derived from transmission topology. Across a large-scale European benchmark, PriceFM achieves strong performance and demonstrates superior generalization under both zero-shot and full-shot evaluation compared with multiple competitive baselines.

翻译：欧洲的电价预测面临独特挑战，这源于欧洲大陆日益一体化和物理互联的电力市场。尽管基础模型的最新进展已显著提升了一般时间序列预测的性能，但现有方法大多未纳入来自输电拓扑的先验图知识，这限制了它们在互联电力系统中利用有意义的跨区域依赖关系的能力，从而催生了针对特定领域的基础模型。本文通过首先引入一个涵盖24个欧洲国家（38个区域）、时间跨度为2022年1月1日至2026年1月1日的全面且最新的数据集来填补这一空白。在此基础上，我们提出了PriceFM——一个在该大型数据集上预训练的概率性基础模型。具体而言，PriceFM通过共享的专家混合投影层将每个区域的价格和外生特征映射到可比较的潜在嵌入中，然后通过构建从输电拓扑导出的稀疏图掩码来注入先验图知识。在大规模欧洲基准测试中，PriceFM展现出强劲的性能，并在零样本和全样本评估下均表现出优于多个竞争基线的泛化能力。