A methodology for creating building energy model occupancy schedules using personal location metadata

Parker, James; Hardy, Adam; Glew, David; Gorse, Christopher

doi:10.1016/j.enbuild.2017.06.014

Cited by 19 publications

(5 citation statements)

References 41 publications

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“…The focus of this learning is to develop a better understanding of collaboration in a complex built environment. (Gianfrate et al 2017), (van Krugten et al 2016), (Broto 2015), (Cosmi et al 2015), (Dall'O' et al 2013), Head 2010 (Fonseca et al 2016), (Glackin and Dionisio 2016), (Dixon et al 2014), (Eames et al 2013), (Mills 2003 (Cao et al 2017), (Heidarinejad et al 2017), (R. Wu et al 2017), (Alwan 2016), (García Kerdan et al 2016), (Marasco and Kontokosta 2016), (Munarim and Ghisi 2016), (Bomberg, Gibson, and Zhang 2015), (De Lieto Vollaro et al 2015), (Dineen, Rogan, and Ó Gallachóir 2015), (Hsu 2015), (Fawcett and Killip 2014), (Wang et al 2014) (Mohareb et al 2017), (Roberti et al 2017), (Parker et al 2017), (Gupta and Gregg 2016), (Tianzhen Hong et al 2016), (Terés-Zubiaga et al 2016), (Yan et al 2015), (Rhodes et al 2015), (Tianzhen Hong et al 2014), …”

Section: Discussionmentioning

confidence: 99%

“…Process (Mohareb et al, 2017), (Roberti et al, Occupant Occupant 2017), (Parker et al, 2017), (Gupta and Gregg, 2016), (Hong et al, 2016), (Terés-Zubiaga et al, 2016), (Yan et al, 2015), (Rhodes et al, 2015), (Tianzhen Hong et al, 2014), (Chuah et al, 2013), (Neto and Fiorelli, 2008), (Yalcintas, 2008), (Bazjanac, 2004) Data Behaviour Behaviour Modelling (Fedoruk et al, 2015), (Beccali et al, 2013), (Peuportier et al, 2013), (Ardente et al, 2011), (Dong et al, 2005)…”

Section: Er and Information Modellingmentioning

confidence: 99%

“…(Roberti et al, 2017) a methodology that permits finding and comparing optimal retrofits for historic buildings in a trans-disciplinary and quantitative way. (Parker et al, 2017) a protocol for extracting and using freely available metadata to create occupancy schedules that are used as inputs for dynamic simulation models. (Gupta and Gregg, 2016) a socio-technical building performance evaluation approach to assess the preand post-actual performance of two discrete deep low energy retrofits.…”

Section: Line Of Researc Hmentioning

confidence: 99%

See 2 more Smart Citations

Transdisciplinarity in energy retrofit. A Conceptual Framework

Sibilla

Kurul

2020

Journal of Cleaner Production

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This study explores the role of Energy Retrofit (ER) in Low Carbon Transition (LCT). The literature recognises the need to move towards a transdisciplinary approach in ER, which encompasses multidisciplinarity and interdisciplinarity. However, the fragmentation between different disciplines remains a significant problem, mainly due to challenges associated with knowledge exchange across the allied disciplines that play a role in ER. The authors posit that ER projects has been conceptualised and implemented using a Systems perspective so that an integrated approach that is akin to transdisciplinarity could become commonplace. Against this background, the aim of this paper is to establish to what extent ER has been conceptualised as a System in the literature so that complexities can effectively be managed through a transdisciplinary approach. This work is based on a literature review of 136 peer-reviewed journal papers. The content analysis demonstrates that current research on transdisciplinarity in ER can be conceptualised in five categories and 15 lines of research. They are presented as a Conceptual Framework, which is this paper's main contribution to existing knowledge. It reveals the direction of innovation in ER for LCT, and is illustrated as a cognitive map. This map exposes the current fragmentation implicit in the literature, and proposes critical connections that need to be established for a transdisciplinary approach. It also shows that the discourse on LCT changed by moving beyond the building scale; and recognising the need to embrace disruptive and local technologies, and integrating the social and technical aspects of ER. Innovative technical solutions and robust information modelling approaches emerge as key vehicles towards making decisions that pay regard to the economic, social and technical factors and that empower the prosumers to play an active role in LCT.

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Section: Discussionmentioning

confidence: 99%

Section: Er and Information Modellingmentioning

confidence: 99%

Section: Line Of Researc Hmentioning

confidence: 99%

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Transdisciplinarity in energy retrofit. A Conceptual Framework

Sibilla

Kurul

2020

Journal of Cleaner Production

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“…In [46], the authors already directly extracted such data for simulation of a supermarket in the UK, due to lacking standard schedule for a 24-h operating retail building [46]. In Japan, researchers used Google popular times data to estimate quasi-real-time energy demand in a commercial district in Tokyo.…”

Section: New Data Sources For Building Occupancymentioning

confidence: 99%

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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“…This accuracy is achieved, thanks to regular updates on the operational status of Google Places, made both by Google itself and by business owners and casual Google Maps users. For these reasons, Google Maps data have also been used in the past to conduct UBEM simulations (Happle et al, 2020; Parker et al, 2017; Yoshida et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Defining archetypes of mixed-use developments using Google Maps API data

Shi

Silvennoinen²,

Chadzynski

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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Urban planning relies on the definition, modelling and evaluation of multidimensional phenomena for informed decision-making. Urban building energy modelling, for instance, usually requires knowledge about the energy use profile and surface area of each use that takes place within a building. We do not have a detailed understanding of such information for mixed-use developments, which are gaining prominence in urban planning. In this paper, we developed a methodology to quantitatively define the characteristics of mixed-use developments using archetypes of programme profiles (ratios of each programme type) of a city’s mixed-use plots. We applied our methodology in Singapore, resulting in 163 mixed-use zoning archetypes using Singapore’s master plan data and Google Maps API data. In a case study, we demonstrated how these archetypes can be used to provide more detailed data for urban building energy modelling, including energy demand forecasts and energy supply system design. To enable future automation of the workflow, the archetype definitions were represented and stored as a machine-readable ontology. This ontology can later be extended with for example, the mobility properties of archetypes; thus, enabling the archetypes' use in other urban planning applications beyond building energy modelling.

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A methodology for creating building energy model occupancy schedules using personal location metadata

Cited by 19 publications

References 41 publications

Transdisciplinarity in energy retrofit. A Conceptual Framework

Transdisciplinarity in energy retrofit. A Conceptual Framework

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

Defining archetypes of mixed-use developments using Google Maps API data

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