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.

In the past decades, a tremendous effort has been put into research and development of improved building systems and technologies to reduce the building energy consumption and advance energy efficiency. However, there is little to no published quantifiable evidence that assesses the energy consumption and efficiency for residential buildings with a context of green building standards. To fill this gap of information, this paper reports an empirical study that investigates the green home energy efficiency and its interaction with resident behaviors. This work uses an integrated approach of energy simulation and multivariate regression modeling. The data are from a sample of more than 300 residential units which meet the green building standards. Findings identify 43% of the annual reduction in energy usage and energy expenditures for a typical American home. Findings also identify four energy-consumption-related resident behaviors depending on which the actual energy efficiency performance of green building technology may differ