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Cross-Market Meta-Learning for Electricity Price Forecasting

arXiv 2610.17387 ↗First posted 12 Oct 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

Day-ahead (DA) electricity price forecasting is usually posed per market, with a separate model fitted to each grid's own history. This caps accuracy: statistical and tree-based baselines saturate, while higher-capacity sequence models need more training data than a single market's few years of hourly prices can supply. Such methods also treat each node as an independent series, discarding the spatial dependencies induced by network topology and congestion. Because price formation across independent system operators (ISOs) is driven by shared physics and largely common market design, we instead treat cross-market history as a pretraining corpus. We present MetaGrid, a topology-aware transformer, meta-trained across markets so that a single zone-count-independent parameter set adapts to an unseen grid. On a leak-free benchmark of five U.S. ISOs, where every model receives the same prices, DA load forecasts and calendar features, MetaGrid attains the lowest average MAE, improving on the best baseline by 3.1% and on the same architecture trained from scratch by 7.7%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 23 references that are in the map. First posted on arXiv 12 Oct 2026.

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References · 17

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  12. 2020Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmarkcited by 108
5 more
  1. 2019Optimal Power Flow Using Graph Neural Networkscited by 63
  2. 2020Transfer Learning for Electricity Price Forecastingcited by 5
  3. 2023On First-Order Meta-Reinforcement Learning with Moreau Envelopescited by 2
  4. 2023Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representationscited by 2
  5. 2025Cross-Domain Graph Anomaly Detection via Test-Time Training with Homophily-Guided Self-Supervisioncited by 2

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