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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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