arXiv Machine Learning June 25, 2026 By Yujiang He, Frederic Uhrweiller, Bernhard Sick

Towards Continuous Power Forecasting: Practical Continual Learning for Real-World Energy Systems in Nonstationary Time Series

Read the original on arXiv Machine Learning →

arXiv:2606. 24955v1 Announce Type: new Abstract: Power forecasting models deployed in real-world energy markets must operate under nonstationary conditions, where data distributions continually evolve due to weather variability, infrastructure upgrades, and changing consumption behaviors.

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