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Home & UtilitiesGenerated accuracy model review: 2026-05-06

Generator Sizing Calculator accuracy and testing

This page documents how Calwarden builds trust for this calculator: source notes, regression-style default tests, limitations, assumptions, unit handling, rounding, and visible change history.

Formula source

  • Recommended watts = (running watts + largest starting surge watts) x (1 + safety margin percent / 100).

Test cases

Default input regression check

Generated from the checked-in default inputs and calculate function.

Inputs

  • Total Running Load: 4,500 W
  • Largest Starting Surge: 2,000 W
  • Safety Margin: 20%

Expected outputs

  • Recommended Generator Size: 7,800 W
  • Recommended Size: 7.8 kW
  • Load Before Margin: 6,500 W

Known limitations

  • This page documents the checked-in calculator logic and does not certify results for every real-world edge case.
  • Use the result as a planning estimate unless the calculator page states a stricter purpose.

Assumptions

  • All prices, rates, taxes, and usage values are entered by the user.
  • Calculations run locally in your browser and are not stored by Calwarden.
  • This is planning math, not financial, tax, engineering, or legal advice.

Unit handling

  • Total Running Load accepts values in W.
  • Largest Starting Surge accepts values in W.
  • Safety Margin accepts values in %.
  • Recommended Generator Size is displayed in W.
  • Recommended Size is displayed in kW.
  • Load Before Margin is displayed in W.

Rounding rules

  • Number displays use up to 2 decimal places in the calculator UI.
  • Input precision follows configured step values where provided.

Version history

1.0.0 - 2026-05-06

Generated accuracy and testing page from the current generator-sizing-calculator calculator definition.

Change log

2026-05-06

Published default regression test documentation and metadata fallbacks for Generator Sizing Calculator.

Calculator transparency

Accuracy pages are generated from checked-in calculator definitions and can be upgraded with curated review metadata over time.

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