H↗ HelioForge

HYBRID ENERGY / RESEARCH / LEARNING

Explore the system.
Understand its limits.

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.

Open HelioForge preview ↗Read the model boundaries

Free browser preview · No sign-in · Bundled example results

HelioForge’s actual hybrid-energy learning interface
Actual preview interface. New numerical runs require the local Python app.

EXPLORE

Follow the energy and the assumptions

  • 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.

START HERE

Try it in three steps

  1. Choose an architecture and inspect its components and stated assumptions.
  2. Open a linked lesson and follow its experiment prompts and self-check.
  3. Review a bundled scenario result and compare its energy and cost measures.

CAPABILITY & LIMITS

A research preview with a visible boundary

The catalogue contains 36 architectures and 36 linked lessons. In the separate local Python application, 19 architectures have electricity-balance screens and 17 remain study-only. This hosted preview shows bundled examples; it does not perform new Python calculations.

  • 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.

Read what is implemented and what remains a study →

COMMON QUESTIONS

Before you explore

Can the standalone preview calculate a new scenario?

No. It displays the interface and bundled examples; run the separate local Python app for new calculations.

Are the model results operational guidance?

No. They are bounded research and learning examples with explicit assumptions, not a plant controller or calibrated digital twin.

Does this website call external AI?

No. The public browser preview has no external AI integration. The separate local application’s optional provider adapter requires explicit configuration and consent.

Does local progress transfer from MKLab?

No. Browser storage belongs to the current origin and device. Local progress saved on another domain is not automatically transferred.

Source, methods and evidence

Maintained by Mohammad Rezwan Khan. Updated .

Public source and verification · App facts · Plain-text guide · Notices

Source revision 5f408d436ef2 · Browser runtime SHA-256 9c3bb931cb0124c7