While photovoltaic (PV) systems are installed at an unprecedented rate, reliable information on an installation level remains scarce. As a result, automatically created PV registries are a timely contribution to optimize grid planning and operations. This paper demonstrates how aerial imagery and three-dimensional building data can be combined to create an address-level PV registry, specifying area, tilt, and orientation angles. We demonstrate the benefits of this approach for PV capacity estimation. In addition, this work presents, for the first time, a comparison between automated and officially-created PV registries. Our results indicate that our enriched automated registry proves to be useful to validate, update, and complement official registries.
@article{arxiv.2012.03690,
title = {An Enriched Automated PV Registry: Combining Image Recognition and 3D Building Data},
author = {Benjamin Rausch and Kevin Mayer and Marie-Louise Arlt and Gunther Gust and Philipp Staudt and Christof Weinhardt and Dirk Neumann and Ram Rajagopal},
journal= {arXiv preprint arXiv:2012.03690},
year = {2020}
}
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Tackling Climate Change with Machine Learning at NeurIPS 2020 (Spotlight talk)