Electricity price forecasting in Europe presents unique challenges due to increasing renewable generation variability, market integration, and the continent's physically interconnected power system. 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, including load, solar, and wind generation forecasts, 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 compared with multiple competitive baselines. The results highlight the value of topology-guided forecasting with increasing renewable generation and strong cross-border interconnections. The methodology is available at: https://runyao-yu.github.io/PriceFM/.

翻译：欧洲电价预测面临着可再生能源发电变异性增加、市场一体化以及欧亚大陆物理互联电力系统带来的独特挑战。尽管基础模型的最新进展已显著提升通用时间序列预测性能，但现有方法大多未纳入输电拓扑的先验图知识，这限制了其挖掘互联电力系统中跨区域有效依赖关系的能力，从而催生了领域专用基础模型的需求。为填补这一空白，我们首先引入一个覆盖24个欧洲国家（38个区域）的全面最新数据集，时间跨度从2022年1月1日至2026年1月1日。在此基础上，我们提出PriceFM——一个在该大规模数据集上预训练的概率性基础模型。具体而言，PriceFM通过共享的混合专家（MoE）投影层将各区域电价及外生特征（包括负荷、太阳能与风能发电预测）映射至可比的潜在嵌入空间，随后利用源自输电拓扑的稀疏图掩码注入先验图知识。在欧洲大规模基准测试中，PriceFM展现出优异性能，并在泛化能力上超越多个竞争基线模型。研究结果凸显了在可再生能源发电增长与强跨境互联背景下，拓扑引导预测的重要价值。相关方法可访问：https://runyao-yu.github.io/PriceFM/。