瓦特委员会：基于受治理大语言模型的情境感知家庭能源情景生成
WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs
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- Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
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中文总结 AI 辅助
针对智能电网因数据受限发展受阻的问题，提出瓦特委员会框架，利用基于大语言模型的代理，在文化、时间和物理等约束下生成家庭能源情景，经实验评估及消融研究验证了其有效性和一致性。
中文摘要 AI 辅助
向低碳电力系统的加速转变以及屋顶太阳能和电动汽车等电表后技术的广泛采用，对电网提出了新的运营和分析要求。同时，智能电网研究越来越依赖机器学习，但由于隐私问题、监管障碍和收集成本，高分辨率家庭能源数据获取有限限制了进展。本文提出瓦特委员会，一个数据生成框架，其中家庭用电需求由基于大语言模型的代理委员会生成，这些代理在特定角色下运行，在明确的文化、时间和物理约束下生成、审核和验证结构化能源情景。这些代理不是静态预测器，而是受治理管道中的自适应决策者。受强调情境因素在能源使用中重要性的研究启发，该框架通过纳入家庭构成、时间因素和环境条件的引导推理过程产生情境敏感的日常活动。我们根据详细的CER数据集评估生成的概况，该数据集包含4232个家庭一年多的负荷测量以及基于调查 的社会经济信息。我们还通过消融研究评估框架的一致性。源代码可在这个https URL获取。
英文摘要
The accelerating shift toward low-carbon power systems, together with the widespread adoption of behind-the-meter technologies such as rooftop solar and electric vehicles, is placing new operational and analytical demands on electricity grids. At the same time, smart-grid research increasingly relies on machine learning (ML), yet progress is constrained by limited access to high-resolution household energy data due to privacy concerns, regulatory barriers, and collection costs. This work presents WattCouncil, a data-generation framework in which household electricity demand is generated by a council of Large Language Model (LLM)-based agents operating in specialized roles to generate, audit, and validate structured energy scenarios under explicit cultural, temporal, and physical constraints. Rather than acting as static predictors, these agents serve as adaptive decision-makers within a governed pipeline. Motivated by studies highlighting the importance of contextual factors in energy use, our framework produces context-sensitive daily routines through a guided reasoning process that incorporates household composition, temporal factors, and environmental conditions. We evaluate the generated profiles against the detailed CER dataset, which contains over a year of load measurements for 4232 households together with survey-based socio-economic information. We further assess the consistency of the framework through ablation studies. Source code is available at https://github.com/Singularity-AI-Lab/wattcouncil
