Smart buildings are complex systems, yet architecture, engineering, and construction (AEC) professionals often perform their work without considering the human factors of building occupants. Traditionally, the AEC industry has employed a linear design and delivery approach. As buildings become smarter, the AEC industry must adapt. To maximize human well-being and the operational performance of smart buildings, an iterative, human-centred approach must be employed. The omission of human factors in the design and delivery of smart building systems risks misalignment between occupant-user needs and the AEC industry’s perception of occupant-user needs. This research proposes a human-centred approach to smart housing. The study employed a multi-phase, mixed-methods research design. Data were collected from 309 high performance housing units in the United States. Longitudinal energy use data, occupant surveys, and semi-structured interviews are the primary data inputs. Affinity diagramming was leveraged to categorize the qualitative data. The output of the affinity diagramming analysis and energy analysis led to the development of data-driven Personas that communicate smart housing user needs. While these data were gathered in the United States, researchers, practitioners, and policy-makers can leverage the human-centred approach presented in this paper toward the design of other human-centred buildings and infrastructure.

智能建筑是复杂的系统，但是建筑，工程和建筑（AEC）专业人员通常在执行工作时没有考虑建筑物占用者的人为因素。传统上，AEC行业采用线性设计和交付方法。随着建筑物变得越来越智能，AEC行业必须适应。为了最大化人类的福祉和智能建筑的运行性能，必须采用以人为本的迭代方法。在智能建筑系统的设计和交付中人为因素的遗漏可能会导致住户用户需求与AEC行业对住户用户需求的感知不一致。这项研究提出了一种以人为本的智能住房方法。该研究采用了多阶段，混合方法的研究设计。数据收集自美国的309个高性能住房单位。纵向能源使用数据，乘员调查和半结构化访谈是主要数据输入。亲和图被用来对定性数据进行分类。亲和力图表分析和能量分析的结果导致了数据驱动型角色的发展，这些角色可传达智能房屋用户的需求。在美国收集这些数据时，研究人员，从业人员和政策制定者可以利用本文介绍的以人为本的方法来设计其他以人为本的建筑物和基础设施。亲和力图表分析和能量分析的结果促成了数据驱动型角色的发展，这些角色可传达智能住房用户的需求。在美国收集这些数据时，研究人员，从业人员和政策制定者可以利用本文中介绍的以人为本的方法来设计其他以人为本的建筑物和基础设施。亲和力图表分析和能量分析的结果导致了数据驱动型角色的发展，这些角色可传达智能房屋用户的需求。在美国收集这些数据时，研究人员，从业人员和政策制定者可以利用本文中介绍的以人为本的方法来设计其他以人为本的建筑物和基础设施。