Loading…
Learning from success: A machine learning approach to guiding solar building envelope applications in non-domestic market
Solar building envelopes, also known as Building Integrated PV (BIPV) show significant growth in Asia and Europe, although other regions such as Australia are still lagging. The decision to uptake BIPV is complex due to the heterogeneous interest of adopters and multi-dimensional features. Instead o...
Saved in:
Published in: | Journal of cleaner production 2022-11, Vol.374, p.133997, Article 133997 |
---|---|
Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c309t-65d1a98fde5dcd05ce3cb45e220e00160453223e913c7d985d84c7eee5c1c9183 |
---|---|
cites | cdi_FETCH-LOGICAL-c309t-65d1a98fde5dcd05ce3cb45e220e00160453223e913c7d985d84c7eee5c1c9183 |
container_end_page | |
container_issue | |
container_start_page | 133997 |
container_title | Journal of cleaner production |
container_volume | 374 |
creator | Weerasinghe, Nilmini Pradeepika Yang, Rebecca Jing Wang, Chen |
description | Solar building envelopes, also known as Building Integrated PV (BIPV) show significant growth in Asia and Europe, although other regions such as Australia are still lagging. The decision to uptake BIPV is complex due to the heterogeneous interest of adopters and multi-dimensional features. Instead of redesigning BIPV in hypothetical buildings, we built a machine learning model using a database of real BIPV and building-attached PV (BAPV) applications, for the purpose of learning and predicting a BIPV adoption decision-making in non-domestic buildings in western countries. We used Australia as a case study to execute the support vector machine (SVM) prediction model. It was revealed that the combination of project determinants such as geographical conditions, equivalent building materials, interest rates and capital cost influenced the decision of BIPV. The prediction model provides pieces of information for stakeholders across the BIPV ecosystem to take their decision on investment, policymaking, and research directions. The current global industry transformation and innovations in technology are favourable to politically promoting and investing in BIPV. Such promotion and investment would help both expand the current market and reach the greenhouse targets.
•Solar building envelope contributes to energy generation and architecture exist.•Decisions are based on economic, environmental and architectural performances.•Project determinants have influenced the choice of heterogeneous adopters.•Industry transformation and innovations are favourable for solar building envelope. |
doi_str_mv | 10.1016/j.jclepro.2022.133997 |
format | article |
fullrecord | <record><control><sourceid>elsevier_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1016_j_jclepro_2022_133997</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0959652622035697</els_id><sourcerecordid>S0959652622035697</sourcerecordid><originalsourceid>FETCH-LOGICAL-c309t-65d1a98fde5dcd05ce3cb45e220e00160453223e913c7d985d84c7eee5c1c9183</originalsourceid><addsrcrecordid>eNqFUMtOwzAQtBBIlMcnIPkHEvyIk5gLqipeUiUucLbS9aY4pHZlp5X69yS0nDmtdjQzOzuE3HGWc8bL-y7voMdtDLlgQuRcSq2rMzLjdaUzXtXlOZkxrXRWKlFekquUOsZ4xapiRg5LbKJ3fk3bGDY07QAwpQc6p5sGvpxH2v8Rmu14YgTpEOh65-yEpdA3ka52rv9d0e-xD1ucuL2DZnDBJ-o89cFnNmwwDQ5G5_iNww25aJs-4e1pXpPP56ePxWu2fH95W8yXGUimhzGz5Y2uW4vKgmUKUMKqUCgEw_GLkhVKCiFRcwmV1bWydQEVIirgoHktr4k6-kIMKUVszTa6McLBcGam_kxnTv2ZqT9z7G_UPR51OIbbO4wmgUMPaF1EGIwN7h-HHwxrftw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Learning from success: A machine learning approach to guiding solar building envelope applications in non-domestic market</title><source>ScienceDirect Freedom Collection</source><creator>Weerasinghe, Nilmini Pradeepika ; Yang, Rebecca Jing ; Wang, Chen</creator><creatorcontrib>Weerasinghe, Nilmini Pradeepika ; Yang, Rebecca Jing ; Wang, Chen</creatorcontrib><description>Solar building envelopes, also known as Building Integrated PV (BIPV) show significant growth in Asia and Europe, although other regions such as Australia are still lagging. The decision to uptake BIPV is complex due to the heterogeneous interest of adopters and multi-dimensional features. Instead of redesigning BIPV in hypothetical buildings, we built a machine learning model using a database of real BIPV and building-attached PV (BAPV) applications, for the purpose of learning and predicting a BIPV adoption decision-making in non-domestic buildings in western countries. We used Australia as a case study to execute the support vector machine (SVM) prediction model. It was revealed that the combination of project determinants such as geographical conditions, equivalent building materials, interest rates and capital cost influenced the decision of BIPV. The prediction model provides pieces of information for stakeholders across the BIPV ecosystem to take their decision on investment, policymaking, and research directions. The current global industry transformation and innovations in technology are favourable to politically promoting and investing in BIPV. Such promotion and investment would help both expand the current market and reach the greenhouse targets.
•Solar building envelope contributes to energy generation and architecture exist.•Decisions are based on economic, environmental and architectural performances.•Project determinants have influenced the choice of heterogeneous adopters.•Industry transformation and innovations are favourable for solar building envelope.</description><identifier>ISSN: 0959-6526</identifier><identifier>EISSN: 1879-1786</identifier><identifier>DOI: 10.1016/j.jclepro.2022.133997</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Building integrated photovoltaics (BIPV) ; Decision-making ; Machine learning ; Solar building applications ; Support vector machine</subject><ispartof>Journal of cleaner production, 2022-11, Vol.374, p.133997, Article 133997</ispartof><rights>2022 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c309t-65d1a98fde5dcd05ce3cb45e220e00160453223e913c7d985d84c7eee5c1c9183</citedby><cites>FETCH-LOGICAL-c309t-65d1a98fde5dcd05ce3cb45e220e00160453223e913c7d985d84c7eee5c1c9183</cites><orcidid>0000-0002-0418-1967</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Weerasinghe, Nilmini Pradeepika</creatorcontrib><creatorcontrib>Yang, Rebecca Jing</creatorcontrib><creatorcontrib>Wang, Chen</creatorcontrib><title>Learning from success: A machine learning approach to guiding solar building envelope applications in non-domestic market</title><title>Journal of cleaner production</title><description>Solar building envelopes, also known as Building Integrated PV (BIPV) show significant growth in Asia and Europe, although other regions such as Australia are still lagging. The decision to uptake BIPV is complex due to the heterogeneous interest of adopters and multi-dimensional features. Instead of redesigning BIPV in hypothetical buildings, we built a machine learning model using a database of real BIPV and building-attached PV (BAPV) applications, for the purpose of learning and predicting a BIPV adoption decision-making in non-domestic buildings in western countries. We used Australia as a case study to execute the support vector machine (SVM) prediction model. It was revealed that the combination of project determinants such as geographical conditions, equivalent building materials, interest rates and capital cost influenced the decision of BIPV. The prediction model provides pieces of information for stakeholders across the BIPV ecosystem to take their decision on investment, policymaking, and research directions. The current global industry transformation and innovations in technology are favourable to politically promoting and investing in BIPV. Such promotion and investment would help both expand the current market and reach the greenhouse targets.
•Solar building envelope contributes to energy generation and architecture exist.•Decisions are based on economic, environmental and architectural performances.•Project determinants have influenced the choice of heterogeneous adopters.•Industry transformation and innovations are favourable for solar building envelope.</description><subject>Building integrated photovoltaics (BIPV)</subject><subject>Decision-making</subject><subject>Machine learning</subject><subject>Solar building applications</subject><subject>Support vector machine</subject><issn>0959-6526</issn><issn>1879-1786</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqFUMtOwzAQtBBIlMcnIPkHEvyIk5gLqipeUiUucLbS9aY4pHZlp5X69yS0nDmtdjQzOzuE3HGWc8bL-y7voMdtDLlgQuRcSq2rMzLjdaUzXtXlOZkxrXRWKlFekquUOsZ4xapiRg5LbKJ3fk3bGDY07QAwpQc6p5sGvpxH2v8Rmu14YgTpEOh65-yEpdA3ka52rv9d0e-xD1ucuL2DZnDBJ-o89cFnNmwwDQ5G5_iNww25aJs-4e1pXpPP56ePxWu2fH95W8yXGUimhzGz5Y2uW4vKgmUKUMKqUCgEw_GLkhVKCiFRcwmV1bWydQEVIirgoHktr4k6-kIMKUVszTa6McLBcGam_kxnTv2ZqT9z7G_UPR51OIbbO4wmgUMPaF1EGIwN7h-HHwxrftw</recordid><startdate>20221110</startdate><enddate>20221110</enddate><creator>Weerasinghe, Nilmini Pradeepika</creator><creator>Yang, Rebecca Jing</creator><creator>Wang, Chen</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-0418-1967</orcidid></search><sort><creationdate>20221110</creationdate><title>Learning from success: A machine learning approach to guiding solar building envelope applications in non-domestic market</title><author>Weerasinghe, Nilmini Pradeepika ; Yang, Rebecca Jing ; Wang, Chen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c309t-65d1a98fde5dcd05ce3cb45e220e00160453223e913c7d985d84c7eee5c1c9183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Building integrated photovoltaics (BIPV)</topic><topic>Decision-making</topic><topic>Machine learning</topic><topic>Solar building applications</topic><topic>Support vector machine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Weerasinghe, Nilmini Pradeepika</creatorcontrib><creatorcontrib>Yang, Rebecca Jing</creatorcontrib><creatorcontrib>Wang, Chen</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of cleaner production</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Weerasinghe, Nilmini Pradeepika</au><au>Yang, Rebecca Jing</au><au>Wang, Chen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Learning from success: A machine learning approach to guiding solar building envelope applications in non-domestic market</atitle><jtitle>Journal of cleaner production</jtitle><date>2022-11-10</date><risdate>2022</risdate><volume>374</volume><spage>133997</spage><pages>133997-</pages><artnum>133997</artnum><issn>0959-6526</issn><eissn>1879-1786</eissn><abstract>Solar building envelopes, also known as Building Integrated PV (BIPV) show significant growth in Asia and Europe, although other regions such as Australia are still lagging. The decision to uptake BIPV is complex due to the heterogeneous interest of adopters and multi-dimensional features. Instead of redesigning BIPV in hypothetical buildings, we built a machine learning model using a database of real BIPV and building-attached PV (BAPV) applications, for the purpose of learning and predicting a BIPV adoption decision-making in non-domestic buildings in western countries. We used Australia as a case study to execute the support vector machine (SVM) prediction model. It was revealed that the combination of project determinants such as geographical conditions, equivalent building materials, interest rates and capital cost influenced the decision of BIPV. The prediction model provides pieces of information for stakeholders across the BIPV ecosystem to take their decision on investment, policymaking, and research directions. The current global industry transformation and innovations in technology are favourable to politically promoting and investing in BIPV. Such promotion and investment would help both expand the current market and reach the greenhouse targets.
•Solar building envelope contributes to energy generation and architecture exist.•Decisions are based on economic, environmental and architectural performances.•Project determinants have influenced the choice of heterogeneous adopters.•Industry transformation and innovations are favourable for solar building envelope.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.jclepro.2022.133997</doi><orcidid>https://orcid.org/0000-0002-0418-1967</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0959-6526 |
ispartof | Journal of cleaner production, 2022-11, Vol.374, p.133997, Article 133997 |
issn | 0959-6526 1879-1786 |
language | eng |
recordid | cdi_crossref_primary_10_1016_j_jclepro_2022_133997 |
source | ScienceDirect Freedom Collection |
subjects | Building integrated photovoltaics (BIPV) Decision-making Machine learning Solar building applications Support vector machine |
title | Learning from success: A machine learning approach to guiding solar building envelope applications in non-domestic market |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T16%3A59%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-elsevier_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Learning%20from%20success:%20A%20machine%20learning%20approach%20to%20guiding%20solar%20building%20envelope%20applications%20in%20non-domestic%20market&rft.jtitle=Journal%20of%20cleaner%20production&rft.au=Weerasinghe,%20Nilmini%20Pradeepika&rft.date=2022-11-10&rft.volume=374&rft.spage=133997&rft.pages=133997-&rft.artnum=133997&rft.issn=0959-6526&rft.eissn=1879-1786&rft_id=info:doi/10.1016/j.jclepro.2022.133997&rft_dat=%3Celsevier_cross%3ES0959652622035697%3C/elsevier_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c309t-65d1a98fde5dcd05ce3cb45e220e00160453223e913c7d985d84c7eee5c1c9183%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |