mlsanityData to 14 Oct 2026

WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs

arXiv 2607.10720 ↗First posted 12 Jul 2026cs.AI cs.LG cs.MAcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 43 references that are in the map. First posted on arXiv 12 Jul 2026.

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References · 8

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  2. 2024Large Language Models for Data Annotation and Synthesis: A Surveycited by 205
  3. 2024Efficient Dynamic Ensembling for Multiple LLM Expertscited by 9
  4. 2025A Strategic Coordination Framework of Small LLMs Matches Large LLMs in Data Synthesiscited by 9
  5. 2025Knowledge Distillation from Large Language Models for Household Energy Modelingcited by 4
  6. 2021Reshaping Smart Energy Transition: An analysis of human-building interactions in Qatar Using Machine Learning Techniquescited by 2
  7. 2021Characterizing Residential Load Patterns by Household Demographic and Socioeconomic Factorscited by 1
  8. 2024Supporting Energy Policy Research with Large Language Modelscited by 1

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  3. 2025From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins
  4. 2025RE-LLM: Integrating Large Language Models into Renewable Energy Systems
  5. 2025From User Preferences to Optimization Constraints Using Large Language Models
  6. 2025Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings
  7. 2023A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability
  8. 2025Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles