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2022 (English)In: E3S web of conferences / [ed] A. Li, T. Olofsson; R. Kosonen, EDP Sciences, 2022, Vol. 356, article id 01003Conference paper, Published paper (Refereed)
Abstract [en]
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.
Place, publisher, year, edition, pages
EDP Sciences, 2022
Series
ROOMVENT Conference, ISSN 25550403, E-ISSN 22671242
National Category
Building Technologies
Identifiers
urn:nbn:se:umu:diva-204512 (URN)10.1051/e3sconf/202235601003 (DOI)2-s2.0-85146829162 (Scopus ID)
Conference
16th ROOMVENT Conference (ROOMVENT 2022), Xi'an, China, 16-19 september, 2022.
Funder
Swedish Research Council FormasEU, Horizon 2020
2023-02-072023-02-072023-02-07Bibliographically approved