A Spatially Detailed Approach to the Assessment of Rooftop Solar Energy Potential based on LiDAR Data

Mohammad Aslani, Stefan Seipel, Stefan Seipel

2022

Abstract

Rooftop solar energy has long been regarded as a promising solution to cities’ growing energy demand and environmental problems. A reliable estimate of rooftop solar energy facilitates the deployment of photovoltaics and helps formulate renewable-related policies. This reliable estimate underpins the necessity of accurately pinpointing the areas utilizable for mounting photovoltaics. The size, shape, and superstructures of rooftops as well as shadow effects are the important factors that have a considerable impact on utilizable areas. In this study, the utilizable areas and solar energy potential of rooftops are estimated by considering the mentioned factors using a three-step methodology. The first step involves training PointNet++, a deep network for object detection in point clouds, to recognize rooftops in LiDAR data. Second, planar segments of rooftops are extracted using clustering. Finally, areas that receive sufficient solar irradiation, have an appropriate size, and fulfill photovoltaic installation requirements are identified using morphological operations and predefined thresholds. The obtained results show high accuracy for rooftop extraction (93%) and plane segmentation (99%). Moreover, the spatially detailed analysis indicates that 17% of rooftop areas are usable for photovoltaics.

Paper Citation

in Harvard Style

Aslani M. and Seipel S. (2022). A Spatially Detailed Approach to the Assessment of Rooftop Solar Energy Potential based on LiDAR Data. In Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-571-5, pages 56-63. DOI: 10.5220/0011108300003185

in Bibtex Style

@conference{gistam22,
author={Mohammad Aslani and Stefan Seipel},
title={A Spatially Detailed Approach to the Assessment of Rooftop Solar Energy Potential based on LiDAR Data},
booktitle={Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2022},
pages={56-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011108300003185},
isbn={978-989-758-571-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - A Spatially Detailed Approach to the Assessment of Rooftop Solar Energy Potential based on LiDAR Data
SN - 978-989-758-571-5
AU - Aslani M.
AU - Seipel S.
PY - 2022
SP - 56
EP - 63
DO - 10.5220/0011108300003185