Time-dependent occupant behaviour models of window control in summer

Yun, Geun Young; Steemers, Koen

doi:10.1016/j.buildenv.2007.08.001

Cited by 230 publications

(132 citation statements)

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“…The temperature is extremely important to the occupant's perception of indoor air quality and thermal processes occurring inside room [35]. It reflects transient state of thermal environment because of an inherent link with building usage and surroundings [36]. The temperature in a building is dependent on outside temperature, sun loading, plus heating and cooling added by the HVAC system and other sources, e.g.…”

Section: Discussionmentioning

confidence: 99%

Stochastic Modeling of Indoor Air Temperature

et al. 2013

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Temperature is one of the main parameters describing thermal comfort and indoor air quality. In this paper we propose an approach, based on a modification of the continuous time random walk, to model the indoor air temperature. We perform a statistical analysis of the recorded time series, that allows us to point out the main statistical properties of the recorded variable. The obtained conclusions about the nature of the process lead to a continuous time random walk, that in contrast to the classical approach, models time dependence of the jumps distribution. Moreover, we show that the waiting times can be modeled by a tempered stable distribution, which yields a subdiffusive behavior in short times and diffusive behavior in longer times. Finally, by conducting a simulation study we illustrate possible applications of the presented approach in the thermal comfort monitoring and forecasting.

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Section: Discussionmentioning

confidence: 99%

Stochastic Modeling of Indoor Air Temperature

et al. 2013

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“…Some studies on occupants are related to the use of passive and active environmental control systems in buildings. For example, the use of windows by building occupants has been shown to be closely related to prevailing indoor and outdoor conditions [16,17]. Field studies on the use of air-conditioning systems by building occupants are relatively rare, but the existing research reveals that occupant control of air-conditioning systems is a key determinant in changes in building energy consumption [18,19].…”

Section: Introductionmentioning

confidence: 99%

Cooling Energy Implications of Occupant Factor in Buildings under Climate Change

Lim

Yun

2017

Sustainability

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Occupant factor is a critical element in the overall energy consumption of buildings. This study aims to reveal how occupant factors influence the energy consumption of example buildings in Seoul, Tokyo, and Hong Kong under climate change projections, and to prioritize factors with energy saving potential for buildings in consideration of future climate change. The study finds that the cooling degree-hours base of 23.3 • C in the three cities sharply increases with future climate change by analyzing future hourly weather data produced herein. Simulations are made with EnergyPlus Runtime Language (Erl) for modeling occupant behavior. The simulation results reveal that a dynamic thermostat control based on an adaptive comfort model is an effective method to reduce cooling energy consumption under future climate change, reducing cooling energy consumption by up to 18% in some instances. In particular, we reveal that a combined application of the adaptive comfort control, nighttime ventilation, and the use of occupancy-based lighting and equipment result in reducing cooling energy consumption by 28%. The outcomes of this study are potentially useful in providing cost-effective solutions to adapt buildings for future climate change with simple modifications to occupant behavior. Also, the roles of renewable energy are briefly discussed.

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“…However, other studies used lighting schedules selected from the recommended occupant schedules, outlined by Chinese design standards [14]. This resulted in the generated lighting schedules which were too simplistic and lacked verification against measured data [18]. Also, this lead to large discrepancies between simulated and measured lighting energy use [19][20].…”

Section: Introductionmentioning

confidence: 99%