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Igor Ferreira

Evidence panel

Solar generation loss, from inverter telemetry alone

Undergraduate thesis, in a pair, at UCL. Using only the telemetry of a residential hybrid inverter, we compared five expected-power models and a per-string loss detector. Sunny turns the result into a plain-language report, with an LLM assistant grounded in the report.

Track
Machine learning and data
Role
Co-author, in a pair
Period
2026
Status
in progress
Tools
Python, pandas, scikit-learn, XGBoost, Flask, Cloudflare Pages

Evidence

  • Error about 41% lower than the baseline

    Mean daily MAE on the hold-out from August 1 to September 27, 2026 (55 days), opened a single time, against the median by time of day. Exploratory gain, computed after the opening.

    Measured.

  • 1 alert in 221 evaluated days

    Per-string detector; 58 of those days are from the hold-out. The alert fell on September 27, on a real loss of one string that the inverter did not record.

    Measured.

  • Model and protocol frozen by SHA-256

    Hash checked before and after each stage; the program refuses to run on the hold-out again.

    Measured.

Video

Sunny in 90 seconds, no narration.

Live

  • SunnyCloudflare Pages, checked on October 15, 2026

The question

Is the native telemetry of a residential hybrid inverter, without measured irradiance, enough to estimate the expected power and to detect and locate generation losses?

Data

63,897 records at 5-minute intervals, with 55 variables, collected through the manufacturer’s API between January 15 and September 27, 2026. The modeling uses only daytime records from valid days.

Validation

  • Five models: the baseline (median by time of day), Random Forest V1 and three XGBoost models.
  • V1 was frozen on July 7 and the protocol on September 28, both by SHA-256, before the August and September hold-out was opened a single time.
  • Of the three hypotheses registered before the test, two were refuted, and the thesis reports this.

Limitations

A single installation, with about eight and a half months of data. The 41% gain holds for MAE and against a deliberately simple baseline. The cause of the September 27 loss was not confirmed in the field.

Credits and notes

  • Undergraduate thesis in a pair, supervised at UCL.