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

arXiv: https://arxiv.org/abs/2609.00089 · HTML: https://mlsanity.com/paper/2609.00089/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 61 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Chronos-2: From Univariate to Universal Forecasting](https://mlsanity.com/paper/2510.15821/) (2025)
- [Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark](https://mlsanity.com/paper/2008.08004/) (2020)
- [Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts](https://mlsanity.com/paper/2410.10469/) (2024)
- [Moirai 2.0: When Less Is More for Time Series Forecasting](https://mlsanity.com/paper/2511.11698/) (2025)
- [From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting](https://mlsanity.com/paper/2501.02945/) (2025)
- [Distributional neural networks for electricity price forecasting](https://mlsanity.com/paper/2207.02832/) (2022)
- [CRPS Learning](https://mlsanity.com/paper/2102.00968/) (2021)
- [Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression](https://mlsanity.com/paper/2404.02270/) (2024)
- [PriceFM: Foundation Model for Probabilistic Electricity Price Forecasting](https://mlsanity.com/paper/2508.04875/) (2025)
- [Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting](https://mlsanity.com/paper/2506.08113/) (2025)
- [Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?](https://mlsanity.com/paper/2308.15443/) (2023)
- [Leveraging Asynchronous Cross-border Market Data for Improved Day-Ahead Electricity Price Forecasting in European Markets](https://mlsanity.com/paper/2507.13250/) (2025)
- [Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables](https://mlsanity.com/paper/2603.15802/) (2026)
- [Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting](https://mlsanity.com/paper/2509.19417/) (2025)
- [Day-Ahead Electricity Price Forecasting for Volatile Markets Using Foundation Models with Regularization Strategy](https://mlsanity.com/paper/2602.05430/) (2026)
- [Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting](https://mlsanity.com/paper/2603.06726/) (2026)

## Built on by (most cited first, 1 of 1)

- [Stealing profits: Spread-based temporal hierarchy forecasting for day-ahead electricity markets](https://mlsanity.com/paper/2609.23223/) (2026)

## Abstract (arXiv)

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.