An Enriched Automated PV Registry: Combining Image Recognition and 3D Building Data
arXiv:2012.03690
Abstract
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.
Tackling Climate Change with Machine Learning at NeurIPS 2020 (Spotlight talk)
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- SolarNet: A Deep Learning Framework to Map Solar Power Plants In China From Satellite Imagery
- Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery