{
  "id": 7691968,
  "title": "Distributed JEPA: A Self-Supervised Framework for Energy Forecasting",
  "url": "https://urgent.news/2026/09/15/distributed-jepa-a-self-supervised-framework-for-energy-forecasting",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-15T11:36:20.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.17029v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Traditional energy forecasting solutions rely on task-specific supervision and energy asset representations, limiting transferability and the ability to capture general temporal dynamics across heterogeneous assets. We address this by proposing a distributed Joint Embedding Predictive Architecture (JEPA) for self-supervised learning from heterogeneous energy time-series. The framework predicts…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}