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The philosophy of building energy management is going through a paradigm change from traditional, often inefficient, user-controlled systems to one that is centrally automated with the aid of IoT-enabled technologies. In this context, occupants’ perceived control and building automation may seem to be in conflict. The inquiry of this study is rooted in a proposition that while building automation and centralized control systems are assumed to provide indoor comfort and conserve energy use, limiting occupants’ control over their work environment may result in dissatisfaction, and in turn decrease productivity. For assessing this hypothesis, data from the post-occupancy evaluation survey of a smart building in a university in Australia was used to analyze the relationships between perceived control, satisfaction, and perceived productivity. Using structural equation modeling, we have found a positive direct effect of occupants’ perceived control on overall satisfaction with their working area. Meanwhile, perceived control exerts an influence on perceived productivity through satisfaction. Furthermore, a field experiment conducted in the same building revealed the potential impact that occupant controllability can have on energy saving. We changed the default light settings from automatic on-and-off to manual-on and automatic-off, letting occupants choose themselves whether to switch the light on or not. Interestingly, about half of the participants usually kept the lights off, preferring daylight in their rooms. This also resulted in a reduction in lighting electricity use by 17.8% without any upfront investment and major technical modification. These findings emphasize the important role of perceived control on occupant satisfaction and productivity, as well as on the energy-saving potential of the user-in-the-loop automation of buildings.

Occupant behaviors are one of the dominant factors that influence building energy use. Traditional building energy modeling programs use typical occupant schedules that often do not reflect actual situations. Robust occupant behavior modeling that seamlessly integrates with building energy models will not only improve simulation performance, but also provide a deeper understanding of occupant behaviors in buildings. This paper presents a development and validation approach to a novel occupant behavior model in commercial buildings. A robust agent-based modeling (ABM) tool, namely Performance Moderator Functions server (PMFserv), is used as the basis of the occupant behavior model. The ABM considers various occupant perceptions and interactions with window, door, and window-blinds based on the environmental conditions. An elaborate agent-based model that represents an office space in an existing building is developed. This is followed by a validation study of the ABM through the use of embedded sensors that capture the indoor ambient conditions and a survey to record actual occupant behaviors. By comparing the recorded behavior data with ABM output, this paper discusses the proposed ABM's prediction ability, limitations, and extensibility. Finally, the paper concludes with the potential of integrating the occupant behavior model with building energy simulation programs.

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Occupant behaviors are one of the most dominant factors that influence building energy use. Understanding the influences from building occupants can promote the development of energy- efficient buildings. This paper quantifies the impact of different occupant behavior information on building energy model (BEM) from multiple perspectives. For this purpose, an occupant behavior model that uses agent-based modeling (ABM) approach is implemented via co-simulation with a BEM of an existing commercial building. Then, actual occupant behavior data in correspondence to ABM output, including operations on window, door, and blinds in selected thermal zones of the building are recorded using survey logs. A simulation experiment is conducted by creating three BEMs with constant, actual, and modeled occupant behavioral inputs. The analysis of the simulation results among these scenarios helps us gain an in-depth understanding of how occupant behaviors influence building performance. This study aims to facilitate robust building design and operation with human-in-the-loop system optimization.

In solving variational, differential and optimisation equations subject to constraints, the penalty method is a well known procedure. Recent publications show that with a combination of positive and negative penalty parameters, constraint violations can be determined and controlled. This relies on the monotonic nature of convergence of the penalised model towards the constrained system, which has been proven and demonstrated for systems with discrete constraints. This paper investigates the use of this approach for systems with continuous constraints. It addresses the questions of whether the number of critical penalty parameters for continuous constraints is finite, and whether the positive and negative penalty method result in monotonic and bounded convergence towards the constrained solution, and compares the use of penalty functions in integral form and discrete forms to represent continuous constraints.