Occupancy diversity factors for common university building types

Davis, James A.; Nutter, Darin W.

doi:10.1016/j.enbuild.2010.03.025

Cited by 114 publications

(46 citation statements)

References 2 publications

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“…Building energy simulations (BESs), such as the ASHRAE 90.1-2013 occupancy profiles, have used homogenous profiles [24,25]; however, as these profiles do not account for the stochastic characteristic of human behavior [26,27], research has moved away from this method, and has taken up stochastic modeling. As the stochastic modeling technique randomizes events, it makes it possible to include a reasonable estimation of real-life variance.…”

Section: Modeling Techniquesmentioning

confidence: 99%

The Energy Saving Potential of Occupancy-Based Lighting Control Strategies in Open-Plan Offices: The Influence of Occupancy Patterns

2017

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Occupancy-based lighting control strategies have been proven to be effective in diminishing offices' energy consumption. These strategies have typically worked by controlling lighting at the room level but, recently, lighting systems have begun to be equipped with sensors on a more fine-grained level, enabling lighting control at the desk level. For some office cases, however, the savings gained using this strategy may not outweigh the costs and design efforts compared to room control. This is because, in some offices, individual occupancy patterns are similar, hence the difference in savings between desk and room control would be minimal. This study examined the influence of occupancy pattern variance within an office space on the relative energy savings of control strategies with different control zone sizes. We applied stochastic modeling to estimate the occupancy patterns, as this method can account for uncertainty. To validate our model, simulation results were compared to earlier studies and real measurements, which demonstrated that our simulations provided realistic occupancy patterns. Next, office cases varying in both job-function type distribution and office policy were investigated on energy savings potential to determine the influence of occupancy pattern variance. The relative energy savings potential of the different control strategies differed minimally for the test cases, suggesting that variations in individual occupancy patterns negligibly influence energy savings. In all cases, lighting control at the desk level showed a significantly higher energy savings potential than strategies with lower control zone granularity, suggesting that it is useful to implement occupancy-based lighting at the desk level in all office cases. This strategy should, thus, receive more attention from both researchers and lighting designers.

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Section: Modeling Techniquesmentioning

confidence: 99%

The Energy Saving Potential of Occupancy-Based Lighting Control Strategies in Open-Plan Offices: The Influence of Occupancy Patterns

2017

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“…In general, the methods used in current studies to determine occupancy and occupancy behaviour profiles can be classified as statistical analysis such as regression, logistic regression, cluster analysis (Guerra Santin et al, 2009), engineering methods such as load profiles (Capasso, Grattieri, Lamedica, & Prudenzi, 1994;McLoughlin et al, 2012;Wilden & Wackelgard, 2010;Yao and Steemers, 2005), and machine-learning algorithms, for example neural networks, Markov chains, data-mining, genetic algorithms and agent-based models (Davis & Nutter, 2010;Duarte, van den Wymelenberg, & Rieger, 2013;D'Oca & Hong, 2015;Jovanovic, Sretenovic, & Zivkovic, 2015;Mahdavi & Tahmasebi, 2015;Virote & Neves-Silva, 2012). …”

Section: Influence Of Occupant Behaviour In Building Simulationmentioning

confidence: 99%

“…These models can be used to randomly generate multiple building occupancy patterns to evaluate the uncertainties related to occupant behaviour. For these models, diverse machine-learning algorithms are used such as Markov chains or artificial neural networks (Davis & Nutter, 2010;Jovanovic et al, 2015;Virote & Neves-Silva, 2012). Prediction models aim to generate artificial occupancy patterns that are similar to the actual (measured) patterns.…”

Section: Influence Of Occupant Behaviour In Building Simulationmentioning

confidence: 99%

Development of Dutch occupancy and heating profiles for building simulation

Guerra-Santin

Silvester

2016

Building Research & Information

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Building simulations are often used to predict energy demand and to determine the financial feasibility of the low-carbon projects. However, recent research has documented large differences between actual and predicted energy consumption. In retrofit projects, this difference creates uncertainty about the payback periods and, as a consequence, owners are reluctant to invest in energy-efficient technologies. The differences between the actual and the expected energy consumption are caused by inexact input data on the thermal properties of the building envelope and by the use of standard occupancy data. Integrating occupancy patterns of diversity and variability in behaviour into building simulation can potentially foresee and account for the impact of behaviour in building performance. The presented research develops and applies occupancy heating profiles for building simulation tools in order create more accurate predictions of energy demand and energy performance. Statistical analyses were used to define the relationship between seven most common household types and occupancy patterns in the Netherlands. The developed household profiles aim at providing energy modellers with reliable, detailed and ready-to-use occupancy data for building simulation. This household-specific occupancy information can be used in projects that are highly sensitive to the uncertainty related to return of investments.

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“…can have impact on their behaviour [15,16]; the operational management of the work place has the potential to influence where people are located in the building and to determine the nature, type and freedom to follow own work preferences and dictate movement from workspaces, and absences from the office [17]; the design of the building itself has an impact on the environmental factors to which individuals are exposed [18,19]; season will affect many factors in a naturally ventilated or mixedmode building such as the indoor environment [20], occupants' thermal comfort [21], clothing insulation [22] and occupants' window use during the daytime as well [6], and could therefore affect the end-of-day window position in commercial non-air-conditioned buildings.…”

Section: The Influence Of Non-environmental Factorsmentioning

confidence: 99%

Factors affecting ‘end-of-day’ window position in a non-air-conditioned office building

Wei

Buswell

Loveday

2013

Energy and Buildings

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This paper presents a longitudinal study observing people's use of windows in mixed mode cellular offices, and sets out to identify factors other than air temperature, that have a significant influence on window operation. The work focuses on the final position of the window at the end of the working day.The results have been compared to other published work and the building is found to have similar characteristics when it and its occupants are treated as a whole. When sub-sets of the data are examined, the factors of season, floor level, gender, and personal preference emerge as having a statistically significant effect on the end-of-day window position in the building examined and these findings are properly discussed with relevant references.

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Occupancy diversity factors for common university building types

Cited by 114 publications

References 2 publications

The Energy Saving Potential of Occupancy-Based Lighting Control Strategies in Open-Plan Offices: The Influence of Occupancy Patterns

The Energy Saving Potential of Occupancy-Based Lighting Control Strategies in Open-Plan Offices: The Influence of Occupancy Patterns

Development of Dutch occupancy and heating profiles for building simulation

Factors affecting ‘end-of-day’ window position in a non-air-conditioned office building

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