An Enriched Automated PV Registry: Combining Image Recognition and 3D Building Data

Benjamin Rausch, Kevin Mayer, Marie-Louise Arlt, Gunther Gust, Philipp Staudt, Christof Weinhardt, Dirk Neumann, Ram Rajagopal

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.

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