Does historical data still count? Exploring the applicability of smart building applications in the post-pandemic period
| Autor: | Qiuchen Lu, Jennifer Schooling, Ajith Kumar Parlikad, Qiaojun Yu, Xiang Xie, Manuel Herrera |
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| Přispěvatelé: | Xie, X [0000-0003-4601-9519], Herrera, M [0000-0001-9662-0017], Schooling, JM [0000-0002-4777-0438], Apollo - University of Cambridge Repository |
| Rok vydání: | 2021 |
| Předmět: |
Historical data
Occupancy Computer science Energy management Post-pandemic media_common.quotation_subject Geography Planning and Development Bayesian probability 0211 other engineering and technologies Transportation 02 engineering and technology 010501 environmental sciences 01 natural sciences Facility management Machine learning 021108 energy 0105 earth and related environmental sciences Civil and Structural Engineering Building automation media_common Variables Renewable Energy Sustainability and the Environment business.industry Perspective (graphical) Data science Smart building business Efficient energy use |
| Zdroj: | Sustainable Cities and Society. 69:102804 |
| ISSN: | 2210-6707 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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