Simulating occupants' impact on building energy performance at different spatial scales

Gilani, Sara; O’Brien, William; Gunay, H. Burak

doi:10.1016/j.buildenv.2018.01.040

Cited by 54 publications

(33 citation statements)

References 26 publications

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“…Recent publications for the building-scale researched on the sensitivity of simulation results to modeling techniques [63] and the effects of using stochastic models for occupant presence at different spatial scales within a single-use building [64]. They concluded that reliable estimations of real occupant behavior are more important than the question of modeling technique [63] and that for a large number of buildings deterministic models suffice due to the averaging effects of stochastic models [64]. However, both studies focused on annual energy demand and single building peak loads and not on temporal characteristics of aggregated load patterns.…”

Section: Stochastic Modelsmentioning

confidence: 99%

A review on occupant behavior in urban building energy models

Happle

Fonseca

Schlueter

2018

Energy and Buildings

161

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Urban building energy models aspire to become key planning tools for the holistic optimization of buildings, urban design, and energy systems in neighborhoods and districts. The energy demand of buildings is largely influenced by the behavior of the occupants. The insufficient consideration of occupant behavior is one of the causes to the "performance gap" in buildings -the difference between the simulated and the actually observed energy consumption. On the urban-scale impacts of different occupant behavior modeling approaches onto the various purposes of urban building energy models are still largely unknown. Research shows that the inappropriate choice of occupant behavior model could result in oversized district energy systems, leading to over-investment and low operational efficiency. This work therefore reviews urban building energy models in terms of their occupant behavior modeling approaches. Three categories of approaches are established and discussed: (1) deterministic space-based approaches, (2) stochastic space-based approaches, and (3) stochastic person-based approaches. They are further assessed in terms of their strategy to consider diversity in occupant behavior. Stochastic models, especially stochastic person-based models, seem to be superior to deterministic models. However, there are no stochastic models available yet that can be used for case studies of mixed-districts, comprising buildings of various occupancy types. In the reviewed urban-scale approaches, only single-use residential or office districts are modeled with stochastic techniques. However, people interact with various buildings on a daily basis. Their activities relate to their presence in different spaces at the urban-scale and to their use of appliances in those spaces. Their individual levels of comfort and behavioral patterns govern the control actions towards building systems. Therefore, a novel activity-based multi-agent approach for urban occupant behavior modeling is proposed as alternative to current approaches.Keywords: urban building energy model, energy-related occupant behavior, occupancy, occupant behavior model IntroductionUrban areas house more than half of the global population and cities are responsible for more than 70% of the global carbon dioxide emissions. At the same time, cities are crucial actors in the transition towards sustainable energy Email address: happle@arch.ethz.ch (Gabriel Happle)

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Section: Stochastic Modelsmentioning

confidence: 99%

A review on occupant behavior in urban building energy models

Happle

Fonseca

Schlueter

2018

Energy and Buildings

161

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“…Only a few researchers reported energy uncertainty related to manual shades. For example, Sara et al [20] simulated occupants' impact on lighting performance at different spatial scales. They compared Haldi's shade model with other schedule and rule-based shade models, however, this research did not consider cooling and heating energy uncertainty.…”

Section: Yearmentioning

confidence: 99%

“…Using the above fitting analysis, the distribution of predicted energy demand (cooling, heating, and total, in this study) can be determined and then the Monte Carlo analysis can be conducted by sampling from these normal distributions to determine energy uncertainty, which can be calculated as follows using the coefficient of variation index [20]:…”

Section: Energy Performancementioning

confidence: 99%

Uncertainty of Energy and Economic Performance of Manual Solar Shades in Hot Summer and Cold Winter Regions of China

Yao

Zheng

2019

Sustainability

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Occupant behavior is recognized as a major source of discrepancy between simulated and actual energy consumption. This study investigates the uncertainty of energy and economic performance of manual solar shades for the south facade. A developed stochastic model for manual solar shades based on a discrete-time Markov chain method was constructed in Building Controls Virtual Test Bed (BCVTB) for co-simulation with EnergyPlus. The stochastic shade model was compared with deterministic models concerning energy savings potential and life cycle economic performance at different building scales (i.e., from a single room to a whole building). The results show that annual energy uncertainty, due to occupant behavior, on manual shades can be neglected at the building level, whereas for sizing heating equipment, energy uncertainty should be considered. The payback period for manual shades is about 10 years and, in general, a larger building has a higher economic performance. Comparative analysis shows that there is a relatively big performance overestimation or underestimation by commonly used deterministic models in building simulation tools, and thus may lead to a biased economic analysis or even an inappropriate design decision when comparing different energy-saving measures.

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“…However, several studies have demonstrated that the use of existing occupant behaviour models involves considerable uncertainties and does not necessarily lead to a more reliable building performance assessment (e.g. [2,3]). Specifically, it is shown that without proper treatment of the diversity in occupants' behaviour, probabilistic occupancy-related models fail to provide representative ranges of occupant behaviour possibilities as intended [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…To address this issue, different approaches for inclusion of diversity in occupant behaviour modelling efforts have been examined [4,6,7,8]. However, it has been also suggested that for specific cases, such as large open-plan offices [3], a detailed treatment of occupants' diversity may not be beneficial. Consequently, further studies in this area are needed, as the potential benefits of supplying probabilistic occupant behaviour models with inter-occupant diver-oural diversity in building simulation contribute to better performance assessments?…”

Section: Introductionmentioning

confidence: 99%

Revisiting the benefits of diversity representation in window operation models for building performance simulation

Tahmasebi¹,

Mahdavi

2019

Bauphysik

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Die Vorteile der Darstellung der Vielfalt in Fensteröffnungsmodellen für die Gebäudesimulation. Um die potenziellen Vorteile der Bereitstellung von Nutzerverhaltensmodellen mit verschiedenen Nutzerinformationen zu untersuchen, wurde der Fensteröffnungsbetrieb durch Nutzer eines Großraumbüros aggregiert und auf individueller Ebene modelliert. Anschließend wurde ein Modell des Großraumbüros unter Einbeziehung der entwickelten Modelle und Ströme aufgezeichneter Daten kalibriert, um die Vorhersageleistung der Modelle und ihren Beitrag zur Verbesserung der Zuverlässigkeit von Gebäude performance‐Bewertungen zu analysieren. Den Ergebnissen zufolge übertrafen die individuellen Fensteroperationsmodelle das aggregierte Modell, indem sie den Spitzenwert und die Variation des Fensterbetriebs über alle Nutzer im freilaufenden Betrieb erfassten, was zu einer besseren Bewertung des thermischen Komforts führte. Die individuellen Modelle ergaben jedoch eine Überschätzung des Spitzenwärmebedarfs im Vergleich zum Referenzwert, der auf dem tatsächlichen Fensterbetrieb in einem einzigen Jahr basiert.

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Simulating occupants' impact on building energy performance at different spatial scales

Cited by 54 publications

References 26 publications

A review on occupant behavior in urban building energy models

A review on occupant behavior in urban building energy models

Uncertainty of Energy and Economic Performance of Manual Solar Shades in Hot Summer and Cold Winter Regions of China

Revisiting the benefits of diversity representation in window operation models for building performance simulation

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