H↗ HelioForge

HYBRID ENERGY / RESEARCH / LEARNING

Explore the system.
Understand its limits.

Explore hybrid-system designs and inspect assumptions. The public browser edition shows bundled synthetic examples and the Terminal calculates new bounded 24-hour energy balances; advanced study models require the separate local Python application.

Open HelioForge preview ↗Read the model boundaries

Free browser preview · No sign-in · Bundled synthetic examples

HelioForge’s actual hybrid-energy learning interface
Actual interface. The Terminal calculates bounded cases in the browser; advanced study models use the separate local Python application.

ENGINEERING / FIELD NOTES

See the system behind the study

A short visual introduction to the engineering context sits alongside this overview. The hosted preview remains synthetic and the film does not validate a design or model.

Poster for the engineering film introducing system context

START WITH A QUESTION

Choose what you need to do

Each path opens a different delivery mode.

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.
  • The public browser edition shows bundled synthetic examples; all 59 advanced study models run new calculations in the separate local Python application.
  • Inspect timelines, sensitivity maps, tariff inputs, signed local evidence and complete result exports.
  • The Terminal runs new deterministic, bounded 24-hour energy-balance cases in the browser; it uses a greedy policy and is not an optimizer.
  • Optional local external providers are separate, opt-in integrations; credentials and explicit consent are required where applicable. The public static preview makes no provider calls.

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. The 59 advanced study models are listed in the versioned model registry.

  • Bundled synthetic examples are precomputed outputs; the public browser edition does not calculate new study-model results from edited inputs.
  • The Terminal runs new deterministic, bounded 24-hour energy-balance cases in the browser; it uses a greedy policy and is not an optimizer.
  • The separate local Python application runs new calculations for the advanced study models and optimization.
  • Optional local external providers are separate, opt-in integrations; credentials and explicit consent are required where applicable. The public static preview makes no provider calls.
  • 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

What is a useful first step?

Explore a bundled example. Open the public preview and inspect a labeled synthetic result.

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.

How can I calculate a Terminal case?

Calculate a bounded Terminal case. Open Scenario compare and use the Parametric energy workbench.

How can I run an advanced study?

Set up advanced local calculations. Use the source setup guide to run the Python application on your machine.

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 · Model registry · Learning pages

Base revision 47e5efc6eb2d · Source tree dirty; local changes included · Browser runtime SHA-256 a1db77380fbc8a16