WattCouncil Context-Aware Household Energy Scenario Generation With Governed LLMs

Jul 16, 2026

The accelerating shift toward low-carbon power systems and the adoption of behind-the-meter technologies such as rooftop solar and electric vehicles create new operational and analytical needs for electricity grids. WattCouncil addresses this by generating household electricity demand through a council of role-specialized LLM-based agents that create, audit, and validate scenarios under explicit cultural, temporal, and physical constraints. The framework produces context-sensitive daily routines using guided reasoning. Evaluations compare generated profiles against the CER dataset, with ablation studies to test framework consistency.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

Abstract

Introduction

Motivation: low-carbon grids and behind-the-meter technologies

Data limitations in high-resolution household energy measurements

Need for controlled, context-aware synthetic data

Role of LLMs and governed agent-based generation

WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs Mohannad Takrouri∗ Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, United Arab Emirates [mohannad.takrouri@mbzuai.ac.ae](mohannad.takrouri@mbzuai.ac.ae) Nicolas Cuadrado∗ Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, United Arab Emirates [nicolas.avila@mbzuai.ac.ae](nicolas.avila@mbzuai.ac.ae) Martin Takac Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, United Arab Emirates [martin.takac@mbzuai.ac.ae](martin.takac@mbzuai.ac.ae) arXiv :2607 . 10720v 1 [ cs .AI] 12 Jul 2026 Abstract 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 datageneration 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 contextsensitive 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 socioeconomic information. We further assess the consistency of the framework through ablation studies. 1 Keywords Synthetic energy data generation, Large Language Models, Multiagent systems, Household electricity consumption, Governed data generation, Smart grid analytics 1 Introduction Global energy consumption continues to grow alongside societal and economic development, intensifying efforts to improve energy efficiency and reduce carbon emissions to address climate change. At the same time, electricity systems are undergoing structural change driven by the widespread deployment of renewable energy sources (RES), whose inherent variability and intermittency introduce new forms of uncertainty for grid operation and analysis [34, 41] . Traditional power grids were designed around centralized, dispatchable generation and relatively predictable consumption patterns. Modern energy systems, however, increasingly depend on a detailed understanding of how demand varies across time, context, and usage conditions, particularly as electrification and distributed ∗ Both authors contributed equally to this research. 1 Source code is available at [https://github.com/Singularity-AI-Lab/wattcouncil](https://github.com/Singularity-AI-Lab/wattcouncil). resources reshape load dynamics. This evolving landscape has motivated energy research that prioritizes not only real-time operations but also the characterization and exploratory analysis of demand behavior as a foundation for planning, policy design, and comparative scenario assessment. Although residential electricity consumption exhibits patterns that can be exploited by data-driven methods, many existing modeling approaches remain tied to limited historical data or rigid simulators that do not fully capture household-level variability. Empirical analyses have shown that electricity use in the residential sector is influenced by multiple de

FAQs

What is WattCouncil designed to do?

WattCouncil is a data-generation framework that produces household electricity demand scenarios using a council of specialized, LLM-based agents. The agents generate, audit, and validate structured energy scenarios under explicit constraints.

How does the framework incorporate context when generating daily routines?

It uses guided reasoning to generate context-sensitive daily routines by incorporating household composition, temporal factors, and environmental conditions. This ensures scenarios reflect specified cultural, temporal, and physical constraints.

How is the generated data evaluated?

The framework evaluates generated profiles against the CER dataset, which includes over a year of load measurements for 4232 households plus survey-based socioeconomic information. Ablation studies are also used to assess consistency.

Jul 16, 2026

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