arXiv Machine Learning July 31, 2026 By Zhixian Wang, Leandro Von Krannichfeldt, Qingsong Wen, Chaoli Zhang, Liang Sun, Shirui Pan, Yi Wang

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Read the original on arXiv Machine Learning →

arXiv:2307. 07191v3 Announce Type: replace Abstract: Energy forecasting is crucial for the power grid, but fundamentally different from general time series analysis: it highly relies on covariates like meteorological factors, and its goals must align with actual power grid operations, such as risk assessment and system reliability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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