Advancing building facade solar potential assessment through AIoT, GIS, and meteorology synergy

IF 13 Q1 ENERGY & FUELS
Kechuan Dong , Qing Yu , Zhiling Guo , Jian Xu , Hongjun Tan , Haoran Zhang , Jinyue Yan
{"title":"Advancing building facade solar potential assessment through AIoT, GIS, and meteorology synergy","authors":"Kechuan Dong ,&nbsp;Qing Yu ,&nbsp;Zhiling Guo ,&nbsp;Jian Xu ,&nbsp;Hongjun Tan ,&nbsp;Haoran Zhang ,&nbsp;Jinyue Yan","doi":"10.1016/j.adapen.2025.100212","DOIUrl":null,"url":null,"abstract":"<div><div>The assessment of building solar potential plays a pivotal role in Building Integrated Photovoltaics (BIPV) and urban energy systems. While current evaluations predominantly focus on rooftop solar resources, a comprehensive analysis of building facade BIPV potential is often lacking. This study presents an innovative methodology that harnesses state-of-the-art Artificial Intelligence of Things (AIoT) techniques, Geographic Information Systems (GIS), and Meteorology to develop a model for accurately estimating spatial–temporal building facade BIPV potential considering 3 Dimension (3D) shading effect. Here, we introduce a zero-shot Deep Learning framework for detailed parsing of facade elements, utilizing cutting-edge techniques in Large-scale Segment Anything Model (SAM), Grounding DINO (Detection Transformer with improved denoising anchor boxes), and Stable Diffusion. Considering urban morphology, 3D shading impacts, and multi-source weather data enables a meticulous estimation of solar potential for each facade element. The experimental findings, gathered from a range of buildings across four countries and an entire street in Japan, highlight the effectiveness and applicability of our approach in conducting comprehensive analyses of facade solar potential. These results underscore the critical importance of integrating shadow effects and detailed facade elements to ensure accurate estimations of PV potential.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100212"},"PeriodicalIF":13.0000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Applied Energy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266679242500006X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

The assessment of building solar potential plays a pivotal role in Building Integrated Photovoltaics (BIPV) and urban energy systems. While current evaluations predominantly focus on rooftop solar resources, a comprehensive analysis of building facade BIPV potential is often lacking. This study presents an innovative methodology that harnesses state-of-the-art Artificial Intelligence of Things (AIoT) techniques, Geographic Information Systems (GIS), and Meteorology to develop a model for accurately estimating spatial–temporal building facade BIPV potential considering 3 Dimension (3D) shading effect. Here, we introduce a zero-shot Deep Learning framework for detailed parsing of facade elements, utilizing cutting-edge techniques in Large-scale Segment Anything Model (SAM), Grounding DINO (Detection Transformer with improved denoising anchor boxes), and Stable Diffusion. Considering urban morphology, 3D shading impacts, and multi-source weather data enables a meticulous estimation of solar potential for each facade element. The experimental findings, gathered from a range of buildings across four countries and an entire street in Japan, highlight the effectiveness and applicability of our approach in conducting comprehensive analyses of facade solar potential. These results underscore the critical importance of integrating shadow effects and detailed facade elements to ensure accurate estimations of PV potential.
通过AIoT、GIS和气象学协同推进建筑立面太阳能潜力评估
建筑太阳能潜力评估在建筑综合光伏系统和城市能源系统中起着至关重要的作用。虽然目前的评估主要集中在屋顶太阳能资源,但对建筑立面BIPV潜力的综合分析往往缺乏。本研究提出了一种创新的方法,利用最先进的物联网人工智能(AIoT)技术、地理信息系统(GIS)和气象学来开发一个模型,用于准确估计考虑三维(3D)阴影效应的建筑立面BIPV时空潜力。在这里,我们介绍了一个零采样深度学习框架,用于详细解析立面元素,利用大规模分段任意模型(SAM)、接地DINO(改进去噪锚盒检测变压器)和稳定扩散中的尖端技术。考虑到城市形态、3D阴影影响和多源天气数据,可以对每个立面元素的太阳能潜力进行细致的估计。实验结果来自四个国家的一系列建筑和日本的一整条街道,强调了我们在立面太阳能潜力进行综合分析时的有效性和适用性。这些结果强调了整合阴影效果和详细立面元素的重要性,以确保准确估计PV潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Advances in Applied Energy
Advances in Applied Energy Energy-General Energy
CiteScore
23.90
自引率
0.00%
发文量
36
审稿时长
21 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信