MKLAB / OPEN BENCH
HelioForge Energy Lab
Explore hybrid-energy layouts, learning activities, and bundled example results in an interactive research and education preview.
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Public browser demonstration
Purpose
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 app ↗Open comparison (0/3)Your first workflow
- 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.
Published capabilities
- 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.
Before you use the result
- 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.
Data: synthetic demonstration
Inspect the evidence behind this app →Source and configuration
Verified public source ↗License: MIT. Storage: Not documented.
Start with the published browser edition above. Local backend setup, when available, belongs to the source repository’s own instructions; it is not executed by this overview.
Questions and answers
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.