Modeling occupancy and behavior for better building design and operation—A critical review

Dong, Bing; Yan, Da; Li, Zhaoxuan; Yuan, Jiao; Feng, Xiaohang; Fontenot, Hannah

doi:10.1007/s12273-018-0452-x

Cited by 151 publications

(52 citation statements)

References 161 publications

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“…They also highlight the need to update standards and building energy codes which currently employ a simplistic approach, which provides schedules that do not match those of the real users. In contrast, Dong et al (Dong et al, 2018) propose that occupancy databases be shared by researchers in view of the difficulty of collecting real data on occupancy behaviour. Mora et al (Mora et al, 2017) suggest modelling 3 occupancy profiles (Regulation, Current-use, Statistical) to evaluate variations in energy building performance.…”

Section: Modelling Occupant Behaviour In Buildingsmentioning

confidence: 99%

Comparing the impact of presence patterns on energy demand in residential buildings using measured data and simulation models

et al. 2019

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Prediction of the energy performance of buildings helps designers with decision-making during the design process in new construction, as well as in renovation projects. Simulation software is used as a prediction tool to calculate the energy performance of buildings. However, numerous studies question its reliability due to the existing discrepancy (gap) between calculated and actual energy performance. Although occupant behaviour is identified as a factor of major impact on the energy performance of buildings, the complex stochastic nature of user behaviour makes it difficult to define actual occupancy patterns. As a result, standard and normative data are usually used as input in energy simulation models. The aim of this research is to test the effect of the use of actual presence profiles on energy demand simulations compared to the use of international normative presence profiles. A study on energy demand has therefore been developed, using dynamic simulation and monitoring campaigns. The results show that the heating and cooling energy demand may differ by up to 15% depending on whether actual or standard presence profiles are used. Therefore, presence profiles should be considered as a significant factor in the adjustment of input data in renovation projects. The final aim of this investigation is to determine the effect of using more accurate building and occupancy simulation parameters when assessing the feasibility of building renovation (payback period calculation for example). This paper focuses on the effect of presence profiles.

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Section: Modelling Occupant Behaviour In Buildingsmentioning

confidence: 99%

Comparing the impact of presence patterns on energy demand in residential buildings using measured data and simulation models

et al. 2019

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“…In contrast, for a space occupied by a few people, such as single offices or residential building, studies pay more attention to the fine-grained information to investigate the occupancy behavior. When it comes into time dimension, the time interval of information processing is closely related to the specific application [4].…”

Section: Building Occupancy Resolutionmentioning

confidence: 99%

“…Dong, et al [4] reviewed the statistical and data mining models for occupancy and behavior modelling, such as agentbased model and k-means clustering. However, for two days of the working day, the prediction methods based on the history data can be indistinguishable.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Real-time Occupancy Information Acquisition Method for Demand-driven Building Energy Management

Liu¹,

Luo²,

Zhang³

2019

Proceedings of the Creative Construction Conference 2019

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Efficient building energy management needs to meet both energy savings and user comfort requirements, which relies on the accurate acquisition of occupancy information over time. In order to meet the data requirements of different building application environments, this paper reviews different sensor technologies based on the principle of obtaining occupancy information, and divides them into five categories, namely motion sensors, environmental sensors, radio frequency (RF) sensors, camera, and contextual sensors. A review of the fusion of multiple sensors that have been widely used for building occupancy detection in recent years has also been conducted. The pros and cons of the prior art are discussed in detail, thus future research can better fit the needs of different system control (such as heating, ventilation, and air conditioning (HVAC) and lighting systems) in different types of buildings.

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“…It was built during 1961-1963 and there are about 100 rooms, encompassing classrooms, some offices, a coffeehouse, toilets, and smaller storage areas. According to the principal, there were about 140 pupils (ages [13][14][15][16] and about 20 personnel occupying the building during 2014-2015. A district heating system (DHS) supplies space and domestic hot water (DHW) heating.…”

Section: Introductionmentioning

confidence: 99%

Measured and Simulated Energy Use in a Secondary School Building in Sweden—A Case Study of Validation, Airing, and Occupancy Behaviour

et al. 2020

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In this case study, the energy performance of a secondary school building from the 1960s in Gävle, Sweden, was modelled in the building energy simulation (BES) tool IDA ICE version 4.8 prior to major renovation planning. The objectives of the study were to validate the BES model during both occupied and unoccupied periods, investigate how to model airing and varying occupancy behaviour, and finally investigate energy use to identify potential energy-efficiency measures. The BES model was validated by using field measurements and evidence-based input. Thermal bridges, infiltration, mechanical ventilation, domestic hot water circulation losses, and space heating power were calculated and measured. A backcasting method was developed to model heat losses due to airing, opening windows and doors, and other occupancy behaviour through regression analysis between daily heat power and outdoor temperature. Validation results show good agreement: 3.4% discrepancy between space heating measurements and simulations during an unoccupied week. Corresponding monthly discrepancy varied between 5.5% and 10.6% during three months with occupants. Annual simulation indicates that the best potential renovation measures are changing to efficient windows, improved envelope airtightness, new controls of the HVAC system, and increased external wall thermal insulation.

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Modeling occupancy and behavior for better building design and operation—A critical review

Cited by 151 publications

References 161 publications

Comparing the impact of presence patterns on energy demand in residential buildings using measured data and simulation models

Comparing the impact of presence patterns on energy demand in residential buildings using measured data and simulation models

An Overview of Real-time Occupancy Information Acquisition Method for Demand-driven Building Energy Management

Measured and Simulated Energy Use in a Secondary School Building in Sweden—A Case Study of Validation, Airing, and Occupancy Behaviour

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