1. Review of data-driven energy modelling techniques for building retrofit
- Author
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Chirag Deb and Arno Schlueter
- Subjects
Greenhouse-gas (GHG) emissions mitigation ,Renewable Energy, Sustainability and the Environment ,Computer science ,Data-driven modelling ,020209 energy ,Energy models ,02 engineering and technology ,Plan (drawing) ,Construction engineering ,Data-driven ,Building simulation ,Building retrofit ,In-situ measurements ,Software deployment ,Order (exchange) ,Greenhouse gas ,Machine learning ,0202 electrical engineering, electronic engineering, information engineering ,Cluster analysis ,Strengths and weaknesses ,Efficient energy use - 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., Renewable and Sustainable Energy Reviews, 144, ISSN:1364-0321
- Published
- 2021
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