DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping

• URL: http://arxiv.org/abs/2207.07466v1
• Date: Fri, 15 Jul 2022 13:23:24 GMT
• Title: DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping
• Authors: Gabriel Kasmi, Laurent Dubus, Philippe Blanc, Yves-Marie Saint-Drenan
• Abstract summary: We build on existing work to propose an automated PV registry pipeline. This pipeline automatically generates a dataset recording all distributed PV installations' location, area, installed capacity, and tilt angle. We propose an unsupervised method based on the it Registre national d'installation (RNI), that centralizes all individual PV systems aggregated at communal level. We deploy our model on 9 French it d'epartements covering more than 50 000 square kilometers, providing the largest mapping of distributed PV panels with this level of detail to date.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Photovoltaic (PV) energy is key to mitigating the current energy crisis. However, distributed PV generation, which amounts to half of the PV energy generation, makes it increasingly difficult for transmission system operators (TSOs) to balance the load and supply and avoid grid congestions. Indeed, in the absence of measurements, estimating the distributed PV generation is tough. In recent years, many remote sensing-based approaches have been proposed to map distributed PV installations. However, to be applicable in industrial settings, one needs to assess the accuracy of the mapping over the whole deployment area. We build on existing work to propose an automated PV registry pipeline. This pipeline automatically generates a dataset recording all distributed PV installations' location, area, installed capacity, and tilt angle. It only requires aerial orthoimagery and topological data, both of which are freely accessible online. In order to assess the accuracy of the registry, we propose an unsupervised method based on the {\it Registre national d'installation} (RNI), that centralizes all individual PV systems aggregated at communal level, enabling practitioners to assess the accuracy of the registry and eventually remove outliers. We deploy our model on 9 French {\it d\'epartements} covering more than 50 000 square kilometers, providing the largest mapping of distributed PV panels with this level of detail to date. We then demonstrate how practitioners can use our unsupervised accuracy assessment method to assess the accuracy of the outputs. In particular, we show how it can easily identify outliers in the detections. Overall, our approach paves the way for a safer integration of deep learning-based pipelines for remote PV mapping. Code is available at {\tt https://github.com/gabrielkasmi/dsfrance}.

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