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.

COVID-19 大流行的出现正在对我们的日常生活造成巨大影响，包括人们与建筑物互动的方式。利用机器学习和其他支持数字技术的进步，人们最近尝试建立令人兴奋的智能建筑应用程序，以促进更好的设施管理和更高的能源效率。然而，依靠疫情前收集的历史数据，由于数据分布的漂移，由此产生的智能建筑应用在当前瞬息万变的情况下不一定有效。本文研究了人与建筑物之间的双向相互作用，这种相互作用导致大流行后建筑性能数据分布发生巨大变化，并评估典型设施管理和能源管理应用程序对这些变化的适用性。根据评估，本文推荐了三种缓解措施，以挽救后大流行时代数据不一致问题中的应用程序和嵌入式机器学习算法。在这些措施中，强调将入住率和行为参数作为独立变量纳入机器学习算法。从贝叶斯的角度来看，在以人为本的观点下，数据的价值被利用，无论是历史的还是最近的，大流行前后的。在这些措施中，强调将入住率和行为参数作为独立变量纳入机器学习算法。从贝叶斯的角度来看，在以人为本的观点下，数据的价值被利用，无论是历史的还是最近的，大流行前后的。在这些措施中，强调将入住率和行为参数作为独立变量纳入机器学习算法。从贝叶斯的角度来看，在以人为本的观点下，数据的价值被利用，无论是历史的还是最近的，大流行前后的。