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Rooftop Solar Underperformance Detector

Compare real solar output with what the panels should produce and flag faults.

Advanced26h1–3 people15/20 fit
TL;DR

Build Rooftop Solar Underperformance Detector for solar owners and installers in ~26h with 1–3 people. Stack: pvlib + Python + Charts + Weather API.

Why it wins: Physics model plus anomaly detection in one clear chart.

Build flow

01 · Problem
Dirty or faulty panels lose output for months because owners never compare against expected yield.
02 · Solution
Model expected output with pvlib using location, tilt and weather, compare with inverter data and flag sustained gaps with likely causes.
03 · Build
  1. 1Model expected yield with pvlib
  2. 2Import inverter CSV
  3. 3Compute ratio and thresholds
  4. 4Generate a report
04 · Demo
Upload a month of inverter data and show the week the panels were dirty.

MVP checklist

  • Expected vs actual chart
  • Fault flags
  • Likely cause hints
  • Monthly report

Tech stack

TechRole
pvlibSolar output modelling
PythonBackend / scripting
ChartsData visualisation
Weather APIWeather data

Reference repos

Open-source projects you can build on. Links checked 2026-10-31.

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