arXiv AI By Dihia Falouz, Aida Douaibia, Amine Bechar, Youssef Elmir, Abbes Amira, Adel Oulefki

An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations

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arXiv:2606. 28467v1 Announce Type: cross Abstract: Appliance-level energy monitoring in office buildings produces noisy alerts that non-expert facility managers struggle to use.

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

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.

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LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications

arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external too...

By Xiaogang Xu, Jiaqi Tang, Jianmin Chen, Yingying Yan, Zhenchao Tang, Xiangxin Zhou, Xiaobin Hu, Wei Wei, Jinfeng Wu, Qifeng Chen, Lu Zhou, Jiafei Wu, Zhe Liu, Jianwei Yin, Weimin Zheng
arXiv AI
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LLM Agents for Time-Series: A Survey

The survey reviews LLM-based agents tailored to time-series tasks, organizing them by problem type rather than technical components. It categorizes existing systems into forecasting and reasoning, augmentation and synthesis, anomaly detection and diagnosis, and decision support, and analyzes how task demands influence agent architecture, tool use, and memory design. The paper also summarizes datasets, environments, and compares model performance, providing a task-oriented guide and highlighting gaps for future research.

By Yilong Chen, Xiao Qin, Chenghao Liu, Liang Wu, Noelle I. Samia, Kaize Ding