Konferenzbeitrag in NeurIPS 2020 Workshop Tackling Climate Change with Machine Learning

2 November 2020

Unserer Beitrag "An Enriched Automated PV Registry: Combining Image Recognition and 3D Building Data" wurde in dem "NeurIPS 2020 Workshop Tackling Climate Change with Machine Learning" als Konferenzbeitrag angenommen.

Autoren: Rausch B, Mayer K, Arlt M, Gust G, Staudt P, Rajagopal R, Neumann D, Weinhardt C

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.