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arXiv Machine Learning By Gabriel Kasmi, Yves-Marie Saint-Drenan, Laurent Dubus, Philippe Blanc

Nationally Consistent, Locally Incomplete: A Bayesian Remote-Sensing Audit of Rooftop Photovoltaic Registries

Read the original on arXiv Machine Learning →

The paper presents a Bayesian framework that uses remote sensing to estimate the true rooftop photovoltaic (PV) capacity, correcting for detector imperfections and providing uncertainty estimates. Applied to France, the method yields a 4.03 GWp estimate for rooftop PV below 36 kWp, aligning with transmission operator data within 3.3% nationally but revealing local under‑reports up to 61%. It also identifies a significant truncation bias in French open PV data, highlighting the need for more reliable capacity estimates worldwide.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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