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

Journal article

Yang, X., Liu, S., Zou, Yuliang, Ji, W., Ahmed, A., Zhange, Q., Han, X., Shen, Y. and Zhang, S. 2022. Energy-saving potential prediction models for large-scale building: A state-of-the-art review. Renewable and Sustainable Energy Reviews. 156.
AuthorsYang, X., Liu, S., Zou, Yuliang, Ji, W., Ahmed, A., Zhange, Q., Han, X., Shen, Y. and Zhang, S.

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.

KeywordsPrediction models, Energy-saving, Physical-based, Data-driven, Building retrofit
JournalRenewable and Sustainable Energy Reviews
Journal citation156
Digital Object Identifier (DOI)
Official URL
Publication dates
Online17 Dec 2021
Publication process dates
Accepted07 Dec 2021
Deposited30 Jun 2022
Accepted author manuscript
File Access Level
Output statusPublished

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