{
  "id": 32,
  "url": "https://arxiv.org/abs/2607.10720v1",
  "title": "WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs",
  "summary": "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",
  "authors": "Mohannad Takrouri, Nicolas M. Cuadrado A., Martin Takáč",
  "category": "research",
  "topics": "regulation,privacy-surveillance,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-12T11:28:02.000Z",
  "fetched_at": "2026-07-14T14:14:15.664Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/32",
  "original_url": "https://arxiv.org/abs/2607.10720v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}