Review of data-driven energy modelling techniques for building retrofit

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

卷:144

Review

Deb, C.¹  Schlueter, A.¹ 

[1] Swiss Fed Inst Technol, Inst Technol Architecture, Chair Architecture & Bldg Syst, Zurich, Switzerland

关键词: Building retrofit;    Data-driven modelling;    Energy models;    Greenhouse-gas (GHG) emissions mitigation;    Building simulation;    In-situ measurements;    Machine learning;   

DOI  :  10.1016/j.rser.2021.110990

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free