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A human-centred approach to smart housing

机译：以人为本的智能住房方法

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摘要

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.
机译：智能建筑是复杂的系统，但架构，工程和建筑（AEC）专业人士经常在不考虑建筑物的人为因素的情况下进行工作。传统上，AEC产业采用了线性设计和交付方法。随着建筑物变得更聪明，AEC行业必须适应。为了最大化人类福祉和智能建筑的操作表现，必须采用迭代，以人为本的方法。遗漏在智能建筑系统的设计和交付中的人类因素风险乘客需求与AEC产业对乘员用户需求之间的错位。本研究提出了一种以人为本的智能外壳的方法。该研究采用了多相，混合方法研究设计。从美国的309个高性能住房单元收集数据。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。利用相关性示意图以对定性数据进行分类。亲和图分析和能量分析的输出导致了传播智能住房用户需求的数据驱动的Personals的开发。虽然这些数据收集在美国，研究人员，从业者和政策制定者可以利用本文中呈现的以人为本的方法，以设计其他以人为本的建筑物和基础设施的设计。

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