Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?

2026-08-31

AI Summary

研究对比了基础模型与特定市场模型在电价预测和电池套利中的表现，发现TabPFN模型在统计上更优，但经济价值取决于风险偏好。

Abstract

Foundation models promise accurate forecasts with little or no task-specific training, but whether they can replace models designed specifically for electricity price forecasting remains unclear. We compare nine variants from five foundation model families, evaluated in zero-shot mode, with two state-of-the-art electricity price forecasting benchmarks in Germany, Poland, and Spain over 2021-2025. Their performance is assessed in terms of point and probabilistic forecasting accuracy, as well as economic value in battery energy storage arbitrage. Only the TabPFN models consistently and significantly outperform the benchmarks across all three markets and all statistical measures. However, this statistical dominance does not translate directly into economic dominance: TabPFN performs best under unlimited bids and riskier quantile-based strategies, whereas the Distributional Deep Neural Network benchmark is more profitable when risk tolerance is lower. Thus, foundation models cannot universally replace market-specific models, and their value depends on both model architecture and the decision problem.

Problem

• foundation models
• electricity price forecasting
• battery arbitrage
• market-specific models

Innovation

• Foundation Models
• Electricity Price Forecasting
• Battery Arbitrage
• TabPFN