PriceFM: Foundation Model for Probabilistic Electricity Price Forecasting

2025/08/06 by Runyao Yu, Chenghong Gu, Yu, Runyao +11 · 3 citations
Energy · Engineering · #Computational Engineering #Electric Power System Optimization #Energy Efficiency and Management #Energy Load and Power Forecasting #FOS: Computer and information sciences #Finance #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2508.04875

openalex publication_date 2025/08/06 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28

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/.

Citations

• Orderbook Feature Learning and Asymmetric Generalization in Intraday Electricity Markets
• OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting
• Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
• Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
• Chronos: Learning the Language of Time Series
• TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
• A decoder-only foundation model for time-series forecasting
• iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
• A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
• TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
• FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
• Probabilistic Forecasting in Day-Ahead Electricity Markets: Simulating Peak and Off-Peak Prices
• Graph Attention Networks
• Inductive Representation Learning on Large Graphs
• Semi-Supervised Classification with Graph Convolutional Networks
• Comparing Predictive Accuracy

Cited by

• WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting
• Graph Neural Networks for Electricity Load Forecasting
• A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting
• Deep Learning for Electricity Price Forecasting: A Review of Day-Ahead, Intraday, and Balancing Electricity Markets

Related

• Statistical electricity price forecasting: A structural approach
• Forecasting Electricity Prices
• Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting
• Leveraging Asynchronous Cross-border Market Data for Improved Day-Ahead Electricity Price Forecasting in European Markets
• Electricity Price Forecasting in the Irish Balancing Market