Occupant behavior modeling for building performance simulation: Current state and future challenges

Yan, Da; O’Brien, William; Hong, Tianzhen; Feng, Xiaohang; Gunay, H. Burak; Tahmasebi, Farhang; Mahdavi, Ardeshir

doi:10.1016/j.enbuild.2015.08.032

Cited by 723 publications

(388 citation statements)

References 93 publications

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“…Both heat and moisture gains were included as a function of indoor air temperature. The resulting occupant model may be characterized as deterministic, whereas recent advances in research include occupant behaviour via stochastic models, taking into account uncertainties and varying presence over time (Stoppel and Leite 2014;Yan et al 2015). However, a standard deterministic presence profile will suffice for most museums because visitors do not interact with the building and presence may be predicted well (Hoes et al 2009).…”

Section: Discussionmentioning

confidence: 99%

The importance of integrally simulating the building, HVAC and control systems, and occupants’ impact for energy predictions of buildings including temperature and humidity control: validated case study museum Hermitage Amsterdam

Kramer

Schijndel

Schellen

2016

Journal of Building Performance Simulation

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For buildings including temperature and humidity control, this study compares the energy prediction accuracy of a ZABESmodel (Zone Air Building Energy Simulation) to an IBES-model (Integral Building Energy Simulation), which additionally includes models of the air handling unit (AHU) and controllers. Museum Hermitage Amsterdam served as a case study. For one year, measurements were performed in the main exhibition hall and its AHU. The ZABES-model was developed using heat air and moisture model for building and systems evaluation (implemented in MATLAB). The IBES-model was developed in Simulink and consists of the ZABES-model and models of AHU-components and controllers. Both models have been validated in detail. The IBES-model's energy prediction errors are well within 10%. However, the ZABESmodel underestimated the total annual energy consumption by 84%. Moreover, including occupants' heat and moisture gains leads to realistic results using the IBES-model, but leads to unrealistic results using the ZABES-model. In conclusion, IBES-models are essential for reliable energy predictions of buildings including humidity control.

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Section: Discussionmentioning

confidence: 99%

The importance of integrally simulating the building, HVAC and control systems, and occupants’ impact for energy predictions of buildings including temperature and humidity control: validated case study museum Hermitage Amsterdam

Kramer

Schijndel

Schellen

2016

Journal of Building Performance Simulation

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“…In [46], they explore a four-step iterative process: (1) occupant monitoring and data collection; (2) model development; (3) model evaluation; and (4) model implementation into building simulation tools. They claim that a major theme in user modeling is to develop an efficient means to implement occupant behavior models and integrate them into building energy modeling programs.…”

Section: User-behavior Modelingmentioning

confidence: 99%

Novel Simulation Approaches for Smart Grids

Tsampasis

Sarakis²,

Leligou³

et al. 2016

JSAN

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Abstract:The complexity of the power grid, in conjunction with the ever increasing demand for electricity, creates the need for efficient analysis and control of the power system. The evolution of the legacy system towards the new smart grid intensifies this need due to the large number of sensors and actuators that must be monitored and controlled, the new types of distributed energy sources that need to be integrated and the new types of loads that must be supported. At the same time, integration of human-activity awareness into the smart grid is emerging and this will allow the system to monitor, share and manage information and actions on the business, as well as the real world. In this context, modeling and simulation is an invaluable tool for system behavior analysis, energy consumption estimation and future state prediction. In this paper, we review current smart grid simulators and approaches for building and user behavior modeling, and present a federated smart grid simulation framework, in which building, control and user behavior modeling and simulation are decoupled from power or network simulators and implemented as discrete components. This framework enables evaluation of the interactions between the communication infrastructure and the power system taking into account the human activities, which are at the focus of emerging energy-related applications that aim to shape user behavior. Validation of the key functionality of the proposed framework is also presented.

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“…The complicating factor is the uncertainty of actual interaction and diversity of occupant profiles [8], which includes passive and active users. Each occupant behavior pattern requires different technical solution for the building systems, which may affect the interactions between them [4].…”

Section: Introductionmentioning

confidence: 99%

“…The adaptive user behavior control is considered one of the gaps in the behavioral model. "The combination and interaction of multiple influencing factors, behaviors and occupants will fill the gap between academic research and simulation applications of occupant behavior" [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Impact of Occupant Behavior on Daylight Performance of an Office Room in a Tropical Climate

Carvalho¹,

Hazboun²,

Carvalho³

et al. 2018

JCEA

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This work explores three patterns of occupants' control of window blinds and the potential influence on daylight performance of an office room in a tropical climate. In this climate, windows are frequently obstructed by curtains to avoid glare, despite the daylighting and the exterior view. The consequences are obstructed outside view, poor daylight quality and dependency on artificial lighting. This paper assesses the impact on available daylight using parametric analysis based on daylighting dynamic computer simulations using Grasshopper and Daysim software, combining WWR (window-to-wall ratio) (40% and 80%), SVF (sky view factor) (small and large) and occupant behavior (active, intermediate and passive users). The user patterns are based in an office buildings survey that identifies preferences concerning daylight use and control of shading devices. The daylight performance criteria combine UDI (useful daylight illuminance) (500-5,000 lux) and illuminance uniformity distribution. Results confirm the impact of occupant behavior on daylighting performance. The optimum combination of external shading devices, high SVF and high window size results in a useful daylighting for 1/3 of the time for passive users and 2/3 for active users.

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Occupant behavior modeling for building performance simulation: Current state and future challenges

Cited by 723 publications

References 93 publications

The importance of integrally simulating the building, HVAC and control systems, and occupants’ impact for energy predictions of buildings including temperature and humidity control: validated case study museum Hermitage Amsterdam

The importance of integrally simulating the building, HVAC and control systems, and occupants’ impact for energy predictions of buildings including temperature and humidity control: validated case study museum Hermitage Amsterdam

Novel Simulation Approaches for Smart Grids

The Impact of Occupant Behavior on Daylight Performance of an Office Room in a Tropical Climate

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