用大模型团队生成符合真实情境的家用电力需求数据，解决隐私与数据短缺难题。
WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs
- 多角色大模型协作生成电力使用场景，模拟家庭构成、时间与环境因素。
- 在4232户的真实数据集上验证，生成结果与实际用电模式高度一致。
- 适合智能电网研究者，尤其关注隐私保护下的数据生成方法。
随着低碳电力系统加速发展及屋顶光伏、电动汽车等分布式技术普及，电网面临新的运行与分析挑战。然而，智能电网研究受限于高分辨率家庭用电数据的获取——受隐私顾虑、监管障碍和采集成本制约。本文提出WattCouncil，一种基于大语言模型（LLM）代理的协同数据生成框架，通过多个具备特定职能的代理在文化、时间与物理约束下生成结构化能源情景。这些代理不作为静态预测器，而是作为受控流程中的动态决策者，结合家庭构成、时间特征与环境条件，通过引导式推理生成具有上下文敏感性的日度用电行为。我们在包含4232户超过一年负荷数据的CER数据集上评估生成结果，并通过消融实验验证框架一致性。代码已开源。
原文摘要 · Abstract (English)
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
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