A novel stochastic modeling method to simulate cooling loads in residential districts

An, Jingjing; Yan, Da; Hong, Tianzhen; Sun, Kaiyu

doi:10.1016/j.apenergy.2017.08.038

Cited by 90 publications

(32 citation statements)

References 56 publications

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“…It is for example well known that the maximal total power demand in a district is different from the sum of the individual buildings' maximal power demands [17]. For a case study in China An et al [18] demonstrated that oversimplified assumptions about occupant behavior leads to a significant overestimation of the peak cooling loads resulting in the design of oversized, inefficient district cooling systems.…”

Section: Key Challenges In Simulating On the Urban-scale Versus The Bmentioning

confidence: 99%

“…The main concerns with occupant behavior on the urban-scale are the diversity among buildings of the same occupancy type, that should be accounted for to obtain realistic energy demand patterns [36,37] and the impacts of behavioral changes over time onto building energy demand [9]. Preliminary research on urban-scale occupant behavior models concludes that stochastic approaches, rather than deterministic, should be considered when high resolution temporal behavior and realistic peak loads are important [18,38,39]. On the other hand, for studies based on monthly or yearly energy consumption, the effect of human behavior might not be significant due to averaging effects [39].…”

Section: Occupant Behavior In Urban-scale Building Energy Modelsmentioning

confidence: 99%

“…Recent publications point out that appropriate occupant behavior models and the impact of occupant behavior on energy use at various temporal and spatial resolutions have to be further studied [35,18]. This work aims to be a contribution towards research on occupant behavior models for urban building energy simulations.…”

Section: Occupant Behavior In Urban-scale Building Energy Modelsmentioning

confidence: 99%

“…In person-based approaches we only encountered variations of diversity strategy 2. Persons are assigned typical behavioral patterns according to their observed prevalence in surveys using clustering methods e.g., [18,73]. Approaches considering synthetically sampled persons or persons exhibiting changing or emerging behavioral patterns were not encountered.…”

Section: Diversity In Person-based Approachesmentioning

confidence: 99%

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A review on occupant behavior in urban building energy models

Happle

Fonseca

Schlueter

2018

Energy and Buildings

160

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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: Key Challenges In Simulating On the Urban-scale Versus The Bmentioning

confidence: 99%

Section: Occupant Behavior In Urban-scale Building Energy Modelsmentioning

confidence: 99%

Section: Occupant Behavior In Urban-scale Building Energy Modelsmentioning

confidence: 99%

Section: Diversity In Person-based Approachesmentioning

confidence: 99%

See 2 more Smart Citations

A review on occupant behavior in urban building energy models

Happle

Fonseca

Schlueter

2018

Energy and Buildings

160

View full text Add to dashboard Cite

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“…Generally speaking, a heat wave refers to a period of time with extremely hot weather. According to the WMO's Task Team on the Definition of Extreme Weather and Climate Events (TT-DEWCE), a heat wave should meet these conditions: (1) the weather is unusually hot and exceeds some given threshold; (2) the event lasts for at least two consecutive days; and (3) occurs during the hot period in one year (WMO, 2015). In different countries or regions, the indices used to define hot, thresholds and duration varies.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for selecting typical hot-year (THY) weather data

et al. 2019

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The global climate change has resulted in not only warmer climate conditions but also more frequent extreme weather events, such as heat waves. However, the impact of heat waves on the indoor environment has been investigated in a limited manner. In this research, the indoor thermal environment is analyzed using a building performance simulation tool for a typical residential building in multiple cities in China, over a time period of 60 years using actual measured weather data, in order to gain a better understanding of the effect of heat wave events. The simulation results were used to analyze the indoor environment during hot summers. A new kind of weather data referred to as the typical hot year was defined and selected based on the simulated indoor environment during heat waves. The typical hot-year weather data can be used to simulate the indoor environment during extreme heat events and for the evaluation of effective technologies and strategies to mitigate against the impact of heat waves on the energy demand of buildings and human health. The limitations of the current study and future work are also discussed.

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Stochastic nature of occupants' behavior toward air‐conditioning operation in residential buildings

Fukami

Hagishima

Tanimoto

et al. 2022

Japan Architectural Review

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Occupants' behaviors (OBs) toward air conditioning (AC) usage in residential buildings have a strong influence on diverse time patterns of load profiles; thus, various stochastic OB modeling has been proposed. However, the validity of such models has not been fully assessed with metered data, particularly for modeling AC switching‐off actions. In this study, the AC operation behavior of 20 dwellings in Japan was observed during the summer. The occurrence of AC‐use events identified from the measured data was analyzed in relation to the time slot of an event, thermal conditions, and frequency of AC usage in each dwelling. The results exhibited a difference between switch‐on and switch‐off actions; the former is primarily dominated by indoor temperature because of thermal adaptation behavior, while the latter is more habitual. Based on this analysis, this study proposed a revised algorithm to synthesize AC operation schedules, which expresses the switch‐off action as the probability of the duration of an AC‐use event rather than a state transition probability function. The proposed algorithm can be incorporated into dynamic building energy simulations. The validity of the revised algorithm was also demonstrated through comparison with the measured data.

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A novel stochastic modeling method to simulate cooling loads in residential districts

Cited by 90 publications

References 56 publications

A review on occupant behavior in urban building energy models

A review on occupant behavior in urban building energy models

A novel approach for selecting typical hot-year (THY) weather data

Stochastic nature of occupants' behavior toward air‐conditioning operation in residential buildings

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