Sökning: onr:"swepub:oai:DiVA.org:umu-204512" > Energy-efficient re...
Fältnamn | Indikatorer | Metadata |
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000 | 03645naa a2200373 4500 | |
001 | oai:DiVA.org:umu-204512 | |
003 | SwePub | |
008 | 230207s2022 | |||||||||||000 ||eng| | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-2045122 URI |
024 | 7 | a https://doi.org/10.1051/e3sconf/2022356010032 DOI |
040 | a (SwePub)umu | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a kon2 swepub-publicationtype |
100 | 1 | a Feng, Kailunu Umeå universitet,Institutionen för tillämpad fysik och elektronik4 aut0 (Swepub:umu)kafe0024 |
245 | 1 0 | a Energy-efficient retrofitting with incomplete building information :b a data-driven approach |
264 | c 2022-08-31 | |
264 | 1 | b EDP Sciences,c 2022 |
338 | a electronic2 rdacarrier | |
520 | a The high-performance insulations and energy-efficient HVAC have been widely employed as energy-efficient retrofitting for building renovation. Building performance simulation (BPS) based on physical models is a popular method to estimate expected energy savings for building retrofitting. However, many buildings, especially the older building constructed several decades ago, do not have full access to complete information for a BPS method. To address this challenge, this paper proposes a data-driven approach to support the decision-making of building retrofitting under incomplete information. The data-driven approach is constructed by integrating backpropagation neural networks (BRBNN), fuzzy C-means clustering (FCM), principal component analysis (PCA), and trimmed scores regression (TSR). It is motivated by the available big data sources from real-life building performance datasets to directly model the retrofitting performances without generally missing information, and simultaneously impute the case-specific incomplete information. This empirical study is conducted on real-life buildings in Sweden. The result indicates that the approach can model the performance ranges of energy-efficient retrofitting for family houses with more than 90% confidence. The developed approach provides a tool to predict the performance of individual buildings from different retrofitting measures, enabling supportive decision-making for building owners with inaccessible complete building information, to compare alternative retrofitting measures. | |
650 | 7 | a TEKNIK OCH TEKNOLOGIERx Samhällsbyggnadsteknikx Husbyggnad0 (SwePub)201032 hsv//swe |
650 | 7 | a ENGINEERING AND TECHNOLOGYx Civil Engineeringx Building Technologies0 (SwePub)201032 hsv//eng |
700 | 1 | a Lu, Weizhuo,c Professor,d 1974-u Umeå universitet,Institutionen för tillämpad fysik och elektronik4 aut0 (Swepub:umu)welu0004 |
700 | 1 | a Penaka, Santhan Reddyu Umeå universitet,Institutionen för tillämpad fysik och elektronik4 aut0 (Swepub:umu)sare0075 |
700 | 1 | a Eklund, Eriku Umeå Municipality, Sweden4 aut |
700 | 1 | a Andersson, Staffan,d 1952-u Umeå universitet,Institutionen för tillämpad fysik och elektronik4 aut0 (Swepub:umu)stan0001 |
700 | 1 | a Olofsson, Thomas,d 1968-u Umeå universitet,Institutionen för tillämpad fysik och elektronik4 aut0 (Swepub:umu)thol0001 |
710 | 2 | a Umeå universitetb Institutionen för tillämpad fysik och elektronik4 org |
773 | 0 | t E3S web of conferencesd : EDP Sciences |
856 | 4 | u https://doi.org/10.1051/e3sconf/202235601003y Fulltext |
856 | 4 | u https://umu.diva-portal.org/smash/get/diva2:1734856/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-204512 |
856 | 4 8 | u https://doi.org/10.1051/e3sconf/202235601003 |
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