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Evidence record 32 · automatically gathered

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

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 present

Record details

Published: 12 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Privacy · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (12 July 2026), “WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs,” evidence record 32, https://ethics.ai/record/32 (originally published by arXiv).

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