PriceFM: Foundation Model for Probabilistic Electricity Price Forecasting

TL;DR

PriceFM is a probabilistic foundation model for European electricity price forecasting that leverages transmission topology and exogenous features, demonstrating superior performance and generalization across a large dataset.

Contribution

The paper introduces PriceFM, a novel domain-specific foundation model that incorporates graph knowledge for probabilistic electricity price forecasting in Europe.

Findings

PriceFM outperforms multiple baselines in European price forecasting.

Incorporating transmission topology improves model accuracy.

The model demonstrates strong generalization across diverse regions.

Abstract

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/.