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

Bridging AI and Energy Forecasting: An Autonomous Workflow with Customized Toolkit

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.

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

arXiv Machine Learning

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arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.

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arXiv Machine Learning

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arXiv:2511. 09789v3 Announce Type: replace Abstract: Short-term energy forecasting plays an important role in real-time operational decision-making, such as electricity market bidding and power system dispatch, where both numerical accuracy and correct directional signals are essential.

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arXiv Machine Learning

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arXiv:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.

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Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction

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FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting

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LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications