{
  "abstract": "Ski resorts face mounting pressure to reduce energy consumption while maintaining skier satisfaction, with energy costs repre- senting up to 69% of operational expenses. This paper presents a novel agent-based digital twin framework that integrates human behavior modeling with energy consumption analysis to address this critical challenge. The framework uniquely combines: (1) a validated physics-based energy model for ski-lifts achieving less than 12% mean error on real operational data, (2) an agent- based simulation of individual skier decision-making that captures heterogeneous preferences, skill levels, and dynamic responses to queue conditions, and (3) a graph-based representation of resort infrastructure enabling scalable scenario analysis. The system is validated using real ski-pass data from a major European resort, demonstrating accurate reproduction of skier flows and lift occupancy patterns. Through multi-objective analysis, we demonstrate the trade-offs between energy consumption and skier satis- faction under different operational strategies, revealing that adaptive speed control can reduce energy by up to 40% on low-traffic days with minimal impact on satisfaction. This work contributes to the growing field of digital twins for sustainable infrastructure management, providing a validated methodology that bridges human behavior modeling, energy systems analysis, and operational optimization. The framework enables resort managers to evaluate energy-saving strategies and infrastructure modifications with- out costly physical experimentation, with broader applicability to other energy-intensive service systems with human-in-the-loop dynamics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Mircea Simoiu"
    },
    {
      "name": "Stéphane Ploix"
    },
    {
      "name": "Fateh Boulmaiz"
    },
    {
      "name": "Siao-Leu Phouratsamay"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/epj6578dhjmrq.bib",
  "cited_by": 0,
  "doi": null,
  "fulltext_url": null,
  "id": "epj6578dhjmrq",
  "issns": [
    "2590-1745"
  ],
  "landing_url": "https://hal.science/hal-05793209v1",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.info"
    ],
    [
      "hal",
      "0.phys"
    ],
    [
      "hal",
      "1.info.info-bi"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://hal.science/hal-05793209/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-11",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/epj6578dhjmrq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:54Z",
      "native_id": "hal-05793209",
      "source": "hal",
      "url": "https://hal.science/hal-05793209v1"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "hal:0.phys",
      "subfield": "general"
    }
  ],
  "title": "An Agent-Based Digital Twin for Energy Management in Ski Resorts: Integrating Skier Behavior Modeling with Lift Energy Consumption Optimization",
  "title_text": "An Agent-Based Digital Twin for Energy Management in Ski Resorts: Integrating Skier Behavior Modeling with Lift Energy Consumption Optimization",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:54Z",
  "url": "https://research.draconitic.com/paper/epj6578dhjmrq",
  "venue": "Energy Conversion and Management: X",
  "versions": []
}