Review of data-driven energy modelling techniques for building retrofit
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Author / Producer
Date
2021-07
Publication Type
Review Article
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yes
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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.
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Publication status
published
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Book title
Journal / series
Volume
144
Pages / Article No.
110990
Publisher
Elsevier
Event
Edition / version
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Software
Geographic location
Date collected
Date created
Subject
Building retrofit; Data-driven modelling; Energy models; Greenhouse-gas (GHG) emissions mitigation; Building simulation; In-situ measurements; Machine learning
Organisational unit
03902 - Schlüter, Arno / Schlüter, Arno