Revealing occupancy patterns in an office building through the use of occupancy sensor data

Duarte, Carlos; Wymelenberg, Kevin Van Den; Rieger, Craig

doi:10.1016/j.enbuild.2013.08.062

Cited by 227 publications

(106 citation statements)

References 21 publications

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“…Much research is underway to identify the uncertainty of human behavior in buildings. Most of the research has been based on surveys, but in recent years, there has been an attempt to develop stochastic occupancy profiles for individual building using occupancy sensor (Duarte et al 2013;Wang et al 2016;Diraco et al 2015), Bluetooth positioning (Zhao et al 2014), and random process (Chen et al 2015;O'Neill and Niu 2017). The identification of actual occupancy schedule may contribute to accurate building energy forecasting and occupancy-based control.…”

Section: Uncertainty In Human Behaviormentioning

confidence: 99%

Review on stochastic modeling methods for building stock energy prediction

Lim

Zhai

2017

Build. Simul.

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Increasing risks of energy security and greenhouse gas emission due to the growing urbanization trend have prompted the need for urban energy demand prediction and management, in which the building energy consumption is the main cause. This paper reviews the recent advances and state-of-the-art in modeling building stock energy consumption, including both the top-down and bottom-up approaches. The study compares and summarizes the strengths and weaknesses of each primary method. Specific focus has been paid to the bottom-up stochastic engineering modeling methods, which hold sound quantitative theory bases as well as considering uncertain reality conditions. Stochastic building stock energy models account for the uncertainties that are the main limitation in existing building stock models. Discussions are provided regarding the process in the current stochastic building stock energy model. Challenges and possible future directions are examined for the improvement of stochastic building stock energy model.

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Section: Uncertainty In Human Behaviormentioning

confidence: 99%

Review on stochastic modeling methods for building stock energy prediction

Lim

Zhai

2017

Build. Simul.

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“…In the design phase, the operational energy consumption is typically simulated by using standard occupancy schedules [21]. However, those only provide a poor estimate of the real occupancy measured in the operation (there is a 46% difference between the American Society of Heating, Refrigerating and Air-Conditioning Engineers ASHRAE standardized occupancy used in the simulation and the real occupancy according to Duarte et al [22]). Operational energy consumption is affected by lighting, plug loads, heating, ventilation and air conditioning equipment utilization, fresh air requirements, and internal heat gain/loss, which depend on the number of occupants and their behavior.…”

Section: Measuring Energy Efficiencymentioning

confidence: 99%

Effects of Building Occupancy on Indicators of Energy Efficiency

2017

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Abstract:The potential to reduce energy consumption in buildings is high. The design phase of the building is very important. In addition, it is vital to understand how to measure the energy efficiency in the building operation phase in order to encourage the right efficiency efforts. In understanding the building energy efficiency, it is important to comprehend the interplay of building occupancy, space efficiency, and energy efficiency. Recent studies found in the literature concerning energy efficiency in office buildings have concentrated heavily on the technical characteristics of the buildings or technical systems. The most commonly used engineering indicator for building energy efficiency is the specific energy consumption (SEC), commonly measured in kWh/m 2 per annum. While the SEC is a sound way to measure the technical properties of a building and to guide its design, it obviously omits the issues of building occupancy and space efficiency. This paper studies existing energy efficiency indicators and introduces a new indicator for building energy efficiency which takes into account both space and occupancy efficiency.

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“…When these homogeneous occupant profiles are used, each space will have same or very similar load profiles, thus no diversity is considered. Duarte et al [14] collected long-term data to show variations of occupancy diversity factors in private offices for time of day, day of the week, holidays, and month of the year, which show differences as much as 46% from those currently recommended by ASHRAE Standard 90.1 2004 energy cost budget guideline, a document referenced by energy modelers regarding occupancy diversity factors for simulations. The simplification of homogeneous schedules also does not capture actual system performance.…”

Section: Introductionmentioning

confidence: 99%

Simulation of occupancy in buildings

Feng

Yan

Hong

2015

Energy and Buildings

199

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Occupants are involved in a variety of activities in buildings, which drive them to move among rooms, enter or leave a building. In this study, occupancy is defined at four levels and varies with time: (1) the number of occupants in a building, (2) occupancy status of a space, (3) the number of occupants in a space, and (4) the space location of an occupant. Occupancy has a great influence on internal loads and ventilation requirement, thus building energy consumption. Based on a comprehensive review and comparison of literature on occupancy modeling, three representative occupancy models, corresponding to the levels 2, 3, and 4, are selected and implemented in a software module. Main contributions of our study include: (1) new methods to classify occupancy models, (2) the review and selection of various levels of occupancy models, and (3) new methods to integrate these model into a tool that can be used in different ways for different applications and by different audiences. The software can simulate more detailed occupancy in buildings to improve the simulation of energy use, and better evaluate building technologies in buildings. The occupancy of an office building is simulated as an example to demonstrate the use of the software module.

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Revealing occupancy patterns in an office building through the use of occupancy sensor data

Cited by 227 publications

References 21 publications

Review on stochastic modeling methods for building stock energy prediction

Review on stochastic modeling methods for building stock energy prediction

Effects of Building Occupancy on Indicators of Energy Efficiency

Simulation of occupancy in buildings

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