{
  "id": 15885,
  "url": "https://arxiv.org/abs/2608.02088v1",
  "title": "An AI-Based Decision-Support Pipeline for Day-Ahead Photovoltaic Forecasting",
  "summary": "Reliable photovoltaic (PV) forecasts are needed for low-carbon energy systems, but newly deployed sites often have short, imperfect records. This makes standard day-ahead forecasting difficult: persistence and physical baselines can be sensitive to calibration and timestamp alignment, while single machine-learning models may capture only one structure in the data and overstate skill under non-temporal validation. We study this problem at a United Kingdom charging-station site, where PV forecast",
  "authors": "Fariba Dehghan, Sebastian Stein, Vahid Yazdanpanah, Stephanie Gauthier, Masood Nazari",
  "category": "research",
  "topics": "safety-alignment,environment",
  "orgs": null,
  "regions": "uk",
  "published_at": "2026-08-03T11:48:32.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/15885",
  "original_url": "https://arxiv.org/abs/2608.02088v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}