A review on occupant behavior in urban building energy models

Happle, Gabriel; Fonseca, Jimeno A.; Schlueter, Arno

doi:10.1016/j.enbuild.2018.06.030

Cited by 171 publications

(78 citation statements)

References 94 publications

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“…For a discussion on the potential biases introduced by considering only places with available data, we refer to the limitations in section 6. In this initial exploratory analysis, we are interested in the representative data-driven schedule of occupancy for each geographic location and use-type for direct comparison with the status-quo of occupancy modeling, which especially on the district-and urban-scale use ASHRAE standard schedules [23,50].…”

Section: Discussionmentioning

confidence: 99%

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Context-specific urban occupancy modeling using location-based services data

Happle

Fonseca

Schlueter

2020

Building and Environment

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Energy-related occupant behavior is a major source of uncertainty in building and urban energy performance simulations. Standardized assumptions, published by ASHRAE and others in the form of occupancy schedules, are widely used in research and practice, especially on the district-scale. In this work, we gathered location-based services data to create context-specific, data-driven occupancy schedules. Using a web mapping service, we collected data for retail and restaurant uses in the downtown neighborhoods of 13 different U.S. cities to create data-driven schedules for each context. The schedules were compared to ASHRAE standard assumptions using the earth mover's distance approach and the schedules' energy-related features. We found that standard schedules seem to significantly overestimate weekly building occupancy, although the shapes of the schedules are generally similar. The use of standard schedules could therefore, have significant impacts on district-scale energy demand simulations, as the overestimation will be cumulative.As compared to the differences between data-driven and standard schedules, the differences between different locations are significantly smaller. However in extreme cases, the weekly cumulative occupancy and the number of occupied hours differ by more than 30% between locations, which means that context-specific differences together with climatic differences might also impact building performance simulation results. Furthermore, we found differences in daily data between the different days of the week. In particular, the observed behavior on Fridays is significantly different from other weekdays for both considered use-types. This indicates that the conventional categorization of occupant behavior models into three day-types: weekday, Saturday, and Sunday, should be reconsidered.

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Section: Discussionmentioning

confidence: 99%

“…However, in our review of occupant behavior in urban building energy models [23], we found that standard schedules of occupancy are used in research beyond their original purpose. We found tools and studies that used standard schedules, among others, to:…”

Section: Origins and Purposes Of Standard Occupancy Schedulesmentioning

confidence: 99%

Context-specific urban occupancy modeling using location-based services data

Happle

Fonseca

Schlueter

2020

Building and Environment

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“…Unwanted infiltration, which has been studied as an external parameter affecting the energy loads of buildings, has been found to be an important factor in energy consumption reduction (Mattingly and Peters, 1977;Anderlind, 1985;Ren and Chen, 2015;Happle et al, 2018). In a recent study, Goubran et al (2017) calculated the infiltration rate in glass entrance doors through simulations and experiments.…”

Section: Research Backgroundmentioning

confidence: 99%

The Effects of Building Glass Façade Geometry on Wind Infiltration and Heating and Cooling Energy Consumption

Darvish¹,

Eghbali²,

Eghbali³

et al. 2020

IJTech

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The control of energy loss through building envelopes has always been a passive design solution for architecture and improvements in space quality. A significant factor is the control of infiltration through the geometry of the glass façades of buildings. The uncontrolled input air flow from the outside into an interior space is known as infiltration. The main infiltration factor is the pressure difference between a building's interior and exterior. This difference might result from the interaction of the wind with the façade. Other possible causes are the stack effect and mechanical ventilation. There is a fundamental question about the effects of the outer glass shell geometry on wind infiltration and building energy consumption. The purpose of this study was to investigate the geometries of building façades with glass materials in different climates and to measure wind infiltration. Consequently, building energy simulations were performed to calculate the infiltration rates in building shells with different geometries. Four forms were simulated, and the effects of the wind infiltration-induced air exchange on heating and cooling energy consumption were evaluated in four climates in Iran. The results indicate that convex geometry reduces the wind pressure in the outer shell and the air exchange rate resulting from the infiltration; thus, heating and cooling energy consumption is reduced.

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“…Research on scalable residential end-use energy demand models for large scale building stocks is relatively new, if compared to the research developed to predict energy and thermal performance of individual buildings [3]. Most of the modelling techniques which have been developed to simulate the energy demand from small to large scale have been collected by several reviews [3][4][5][6][7][8][9]. In these papers, the modelling techniques are separated into two main categories: top-down and bottom-up.…”

Section: Introductionmentioning

confidence: 99%

A high-temporal resolution residential building occupancy model to generate high-temporal resolution heating load profiles of occupancy-integrated archetypes

Buttitta

Finn

2020

Energy and Buildings

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A strong correlation exists between occupant behaviour and space heating energy use. In particular, the occupancy status (e.g., daytime absence) is known to have a significant influence on residential heating load profiles, as well as on cumulative heating energy consumption. In the literature, many occupancy models have been utilised to predict occupancy profiles of individual dwellings as part of the larger residential building stock. However, none of the existing models consider diversity associated with occupancy-integrated archetypes to generate high-temporal resolution heating load profiles. The current paper uses Time Use Survey (TUS) data to develop a high-temporal resolution residential building occupancy model. The key feature of the proposed model, implemented using MATLAB, is the ability generate stochastic occupancy time-series data for national population subgroups characterised by specific occupancy profiles. It is shown that the results are capable of closely approximating data available from TUS. The developed model can be applied to improve the quality of modelled high-temporal resolution heating load profiles for generic building stock characterised by population subgroups represented by different occupancy-integrated archetypes. A case study is performed on a building stock sample located in London, UK. The developed occupancy model has been implemented in MATLAB and is available for download

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A review on occupant behavior in urban building energy models

Cited by 171 publications

References 94 publications

Context-specific urban occupancy modeling using location-based services data

Context-specific urban occupancy modeling using location-based services data

The Effects of Building Glass Façade Geometry on Wind Infiltration and Heating and Cooling Energy Consumption

A high-temporal resolution residential building occupancy model to generate high-temporal resolution heating load profiles of occupancy-integrated archetypes

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