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Does historical data still count? Exploring the applicability of smart building applications in the post-pandemic period

Lookup NU author(s): Dr Xiang XieORCiD


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The emergence of COVID-19 pandemic is causing tremendous impact on our daily lives, including the way people interact with buildings. Leveraging the advances in machine learning and other supporting digital technologies, recent attempts have been sought to establish exciting smart building applications that facilitates better facility management and higher energy efficiency. However, relying on the historical data collected prior to the pandemic, the resulting smart building applications are not necessarily effective under the current ever-changing situation due to the drifts of data distribution. This paper investigates the bidirectional interaction between human and buildings that leads to dramatic change of building performance data distributions post-pandemic, and evaluates the applicability of typical facility management and energy management applications against these changes. According to the evaluation, this paper recommends three mitigation measures to rescue the applications and embedded machine learning algorithms from the data inconsistency issue in the post-pandemic era. Among these measures, incorporating occupancy and behavioural parameters as independent variables in machine learning algorithms is highlighted. Taking a Bayesian perspective, the value of data is exploited, historical or recent, pre- and post-pandemic, under a people-focused view.

Publication metadata

Author(s): Xie X, Lu QC, Herrera M, Yu QJ, Parlikad A, Schooling J

Publication type: Article

Publication status: Published

Journal: Sustainable Cities and Society

Year: 2021

Volume: 69

Print publication date: 01/06/2021

Online publication date: 01/03/2021

Acceptance date: 22/02/2021

ISSN (print): 2210-6707

ISSN (electronic): 2210-6715

Publisher: Elsevier BV


DOI: 10.1016/j.scs.2021.102804


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