Mao, Hongzhi, Chen, Xie, Luo, Yongqiang, Deng, Jie ORCID: https://orcid.org/0000-0001-6896-8622, Tian, Zhiyong, Yu, Jinghua, Xiao, Yimin and Fan, Jianhua (2023) Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images. Renewable and Sustainable Energy Reviews, 179. p. 113276. ISSN 1364-0321

Preview

PDF RSER-D-22-03705 Accepted manuscript.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview

Abstract

Solar photovoltaic (PV) system, as one kind of the most promising renewable energy technologies, plays a key role in reducing carbon emissions to achieve the targets of global net zero carbon. In the past few decades, PV installations have seen a rapid growth. Predicting the installed amount and the capacity of solar PV systems is therefore useful for formulating effective carbon reduction policies in the related area. In the present study, the methods of identifying PV installation based on satellite and aerial images have been reviewed. Suggestions have been put forward to optimize the identification process and to predict the potential of rooftop PV installation. The results show that the specific purposes of PV identification can be categorized as image classification, object detection and semantic segmentation. The available identification methods encompass pixel-based analysis method (PBIA), object-based analysis method (OBIA) and deep learning. Deep learning has a high accuracy in segmentation for all sizes of PV systems, with precision and recall of rooftop PV segmentation in the range of 41–98.9% and 54.5–95.8%, respectively. OBIA has the best accuracy in detecting centralized PV systems with relatively low-resolution multispectral images. Furthermore, a grading segmentation strategy for PV segmentation in the large region is presented, combining the three identification methods and the images with different resolutions. In addition, the potential of rooftop PV installation can be predicted by segmenting the available roof area in the images. After considering the shading effects, upper structure and other uses, the roof availability coefficient tends to be in the range of 0.25–0.46. It is also suggested to combine PV and roof segmentation to estimate the installation potential more accurately, in the context of rapid growth of the rooftop PV.

Item Type:	Article
Identifier:	10.1016/j.rser.2023.113276
Keywords:	Solar photo, voltaic, Identification, Satellite and aerial images, Deep learning
Subjects:	Construction and engineering > Civil and environmental engineering
Related URLs:	Publisher
Depositing User:	Jie Deng
Date Deposited:	05 May 2023 12:40
Last Modified:	04 Nov 2024 11:18
URI:	https://repository.uwl.ac.uk/id/eprint/9918

Downloads

Actions (login required)

View Item