Abstract
Abstract. In guiding the energy transition efforts towards renewable energy sources, 3D city models were shown to be useful tools when assessing the annual solar energy generation potential of urban landscapes. However, the simplified roof geometry included in these 3D city models and the lack of additional semantic information about the buildings’ roof often yield less accurate solar potential evaluations than desirable. In this paper we propose three different methods to infer and store additional information into 3D city models, namely on physical obstacles present on the roof and existing solar panels. Both can be used to increase the accuracy of roof solar panel retrofit potential. These methods are developed and tested on the open datasets available in the Netherlands, specifically AHN3 lidar point-cloud and PDOK aerial photography. However, we believe they can be adapted to different environments as well, based on the available datasets and their precision locally available.
Highlights
The share of renewable energy consumption in the Netherlands is currently at 7.4%
Our experiments show that obstacles detected by the first method are usually correct but incomplete: higher obstacles such as chimneys are detected whereas lower ones, like solar panels, are missing due to not crossing the obstacle distance parameter
The objective of this paper is to present a method to infer semantic properties of roofs by focusing on roof obstacles such as dormers, chimneys, and solar panels
Summary
The share of renewable energy consumption in the Netherlands is currently at 7.4% Small, both in respect to the other energy sources and compared to the International Energy Agency members median of 15.5%. With the goal to raise the share of solar in energy production to 27% by 2030, solar energy plays an important role in the transition towards renewable energy sources and future development of Dutch energy network (Dutch New Energy Research, 2021). To maintain this development, tools to evaluate solar potential are valuable resources for determining the suitability of mounting solar panels on buildings. Small-scale obstacles on the roof, which influence the available area and shading, are often not or only insufficiently regarded in the solar potential models and analysis process (Rodríguez et al, 2017)
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