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Transfer of building retrofitting evaluations for data-scarce conditions: an empirical study for Sweden to China
Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China.
Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China.
Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China.
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. International Joint Laboratory on Low Carbon Built Environment, Ministry of Education of China, Xi'an University of Architecture and Technology, Xi'an, China.ORCID iD: 0000-0002-8704-8538
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2024 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 310, article id 114041Article in journal (Refereed) Published
Abstract [en]

Evaluating and comparing the performances of different strategies is critical for energy-efficient building retrofitting. Data-driven modelling based on large building performance datasets is an effective method for such evaluations. However, it could be challenging to apply this approach to buildings from data-scarce areas where local building performance datasets have not been well-established, which means the data falls short of the high demand for building retrofitting on a global level. To address this, a transfer learning approach is proposed in this study that can evaluate the performance of buildings without local well-established building performance datasets. The proposed approach is applied in the Swedish-Chinese empirical study that relies on the Swedish dataset to transfer and predict the building performance in China without well-established datasets. It was achieved by applying fuzzy C-means clustering and a neural network (FCM-BRBNN) to pre-train the evaluation model based on the Swedish dataset. Then, the proposed approach collects a small sample of Chinese buildings in the data-scarce area and transfers the model to local building performance prediction. The results show that the transfer learning approach can reliably predict the performance of building retrofitting in data-scarce areas with only hundreds of local building samples. As such, this study provides a novel methodology that can support the evaluation and comparison of retrofitting strategies in data-scarce regions and countries with only limited local data. It could efficiently assist designers in optimizing energy-efficient designs in the pre-retrofit stage. Crucially, the methodology enables the transfer of knowledge regarding building performance across different countries and regions, being pivotal for the international collaboration required to stimulate the global energy-efficiency transformation.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 310, article id 114041
Keywords [en]
Building retrofitting, Data-driven model, Energy-efficient, Transfer learning
National Category
Building Technologies
Identifiers
URN: urn:nbn:se:umu:diva-223261DOI: 10.1016/j.enbuild.2024.114041Scopus ID: 2-s2.0-85187962486OAI: oai:DiVA.org:umu-223261DiVA, id: diva2:1852387
Funder
Swedish Research Council Formas, 2020-02085Swedish Research Council Formas, 2022-01475Swedish Energy Agency, P2022-00141Available from: 2024-04-18 Created: 2024-04-18 Last updated: 2024-04-18Bibliographically approved

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Olofsson, ThomasLu, Weizhuo

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