Review of data-driven energy modelling techniques for building retrofit

C. Deb and A. Schlueter

Renewable and Sustainable Energy Reviews, 2021, vol. 144, issue C

Abstract: In order to meet the ambitious emission-reduction targets of the Paris Agreement, energy efficient transition of the building sector requires building retrofit methodologies as a critical part of a greenhouse-gas (GHG) emissions mitigation plan, since in 2050 a high proportion of the current global building stock will still be in use. This paper reviews current retrofit methodologies with a focus on the contrast between data-driven approaches that utilize measured building data, acquired through either 1) on-site sensor deployment or 2) from pre-aggregated national repositories of building data. Differentiating between 1) bottom-up approaches that can be divided into white-, grey- and black-box modelling, and 2) top-down approaches that utilize analytical methods of clustering and regression, this paper presents the state-of-the-art in current building retrofit methodologies; outlines their strengths and weaknesses; briefly highlights the challenges in their implementation and concludes by identifying a hybrid approach - of lean in-situ measurements supplemented by modelling for verification - as a potential strategy to develop and implement more robust retrofit methodologies for the building stock.

Keywords: Building retrofit; Data-driven modelling; Energy models; Greenhouse-gas (GHG) emissions mitigation; Building simulation; In-situ measurements; Machine learning (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (24)

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DOI: 10.1016/j.rser.2021.110990

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