Abstract

Human-centered control systems in buildings have gained increasing attention in recent years as they foster the integration of user preferences in the decision-making control algorithm. However, such advanced systems are built upon the assumption that occupants are willing to interact as data providers. Introducing technologies reliant on feedback from the users, without initially analysing how users accept them, can lead to impeding the anticipated operation of such systems. Hence, this rare interdisciplinary research presents a nexus of technical and social sciences, that addresses the occupant-related misconceptions regarding the interaction with advanced control systems. This study identifies the occupant preferences and provides a method to predict the willingness of occupants to interact with advanced control for indoor environment control and demand-response actions. Additionally, the proposed approach enables to identify the main drivers and motivators of that interaction. This study is built upon the behavioural science theory called the Theory of Planned Behaviour and 435 school building occupants participated in the study. The findings from this pertinent research show that many common concepts in the building control domain do not comply with the occupants’ viewpoint and need to be reconsidered. For instance, in the post-pandemic era, the most important comfort aspect according to the occupants is the indoor air quality compared to thermal and visual comfort. Furthermore, although higher automation levels are desired, control access through advanced technologies, such as human-centered control, is not easily accepted by occupants. Hence, their willingness for interaction should be predicted instead of assuming that occupants will share data with advanced control systems. The proposed model in this research has predictive potential and yielded an explained variance of 58%, showing that the target occupants of the building are willing to interact with the controller. These findings are important to researchers and practitioners who design and develop human-centered control systems to foresee the engagement level of occupants in indoor environment control or demand-response actions.

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