{
  "title": "HelioForge Energy Lab",
  "url": "https://mkgrid.co.technology/",
  "applicationUrl": "https://mkgrid.co.technology/run/",
  "description": "Explore hybrid-energy layouts, learning activities, and bundled example results in an interactive research and education preview.",
  "summary": "HelioForge is an energy-learning workbench for exploring hybrid-system designs and scenario assumptions. Its standalone preview shows bundled examples; new calculations require the local Python application.",
  "features": [
    "Browse hybrid-energy architectures and inspect their labeled system layouts.",
    "Read architecture-linked lessons with experiment steps and self-check questions.",
    "Review bundled scenario examples and compare visible energy assumptions and outcomes."
  ],
  "steps": [
    "Choose an architecture and inspect its components and stated assumptions.",
    "Open a linked lesson and follow its experiment prompts and self-check.",
    "Review a bundled scenario result and compare its energy and cost measures."
  ],
  "limitations": [
    "The standalone browser preview uses bundled examples and does not run new Python calculations or optimization.",
    "New numerical runs require the separate local Python API and its setup.",
    "Models are educational research screens, not calibrated plant controls, forecasts, or investment advice."
  ],
  "faqs": [
    {
      "question": "Can the standalone preview calculate a new scenario?",
      "answer": "No. It displays the interface and bundled examples; run the separate local Python app for new calculations."
    },
    {
      "question": "Are the model results operational guidance?",
      "answer": "No. They are bounded research and learning examples with explicit assumptions, not a plant controller or calibrated digital twin."
    }
  ],
  "updated": "2026-09-30",
  "author": "Mohammad Rezwan Khan",
  "sourceRevision": "5f408d436ef271c67550f952587d8f0ba77353ed",
  "publicSource": "https://github.com/mohammadrezwankhan/helioforge-energy-lab",
  "runtimeSha256": "9c3bb931cb0124c736d2666b2b0226a5c35bb5d29d72651d1642bb950cce3c93",
  "dataStatus": "synthetic demonstration",
  "access": "public; no sign-in",
  "methodologyUrl": "https://mkgrid.co.technology/methodology/",
  "authorUrl": "https://mkgrid.co.technology/about/",
  "capabilities": {
    "architectures": 36,
    "lessons": 36,
    "localApiRunnableArchitectures": 19,
    "studyOnlyArchitectures": 17,
    "browserMode": "bundled snapshot; new numerical runs disabled"
  }
}