Energy-saving potential prediction models for large-scale building: A state-of-the-art review

Xiu'e Yang, Shuli Liu, Yuliang Zou, Wenjie Ji, Qunli Zhang, Abdullahi Ahmed, Xiaojing Han, Yongliang Shen and Shaoliang Zhang

Renewable and Sustainable Energy Reviews, 2022, vol. 156, issue C

Abstract: Energy-saving potential prediction models play a major role in developing retrofit scheme. Reliable estimation and quantification of energy saving of retrofit measures for these models is essential, since it is often used for guiding political decision-makers. The aim of this paper is to provide up-to-date approaches of predicting energy-saving effect for building retrofit in large-scale, including data-driven, physics-based, and hybrid approaches, while throwing light on workflow and key factors in developing models. The review focuses on pointing out pivotal aspects that are not considered in current models of predicting energy-saving effect for building retrofit in large-scale. It is concluded that the validation of proposed models mainly focuses on an aggregated level, which makes it ignore performance gap differences between buildings. The models exist the problem of prebound- and rebound effects due to uncertainty factor. Occupant's willingness to retrofit is ignored in all three categories of models, which can lead to the prediction result deviate from the actual situation in a certain extent. This paper promotes the development of models for predicting energy-saving potential for large-scale buildings, and help to formulate appropriate strategies for the retrofit of existing buildings.

Keywords: Prediction models; Energy-saving; Physical-based; Data-driven; Building retrofit (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.rser.2021.111992

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