Simulation and visualization of energy-related occupant behavior in office buildings

Chen, Yixing; Liang, Xin; Hong, Tianzhen; Luo, Xuan

doi:10.1007/s12273-017-0355-2

Cited by 67 publications

(24 citation statements)

References 30 publications

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“…Recently, Sun and Hong (Sun & Hong, 2016) used the occupancy simulator to support the analysis of energy saving potential of occupant behavior measures. Chen et al (Chen, Liang, Hong, & Luo, 2017) applied the occupancy simulator to support the simulation and visualization of energy-related occupant behavior in office buildings. In the future, the Occupancy Simulator may be used to generate schedules supporting assessment of new measures for the development of building energy code and standards.…”

Section: Discussionmentioning

confidence: 99%

An agent-based stochastic Occupancy Simulator

2017

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Occupancy has significant impacts on building performance. However, in current building performance simulation programs, occupancy inputs are static and lack diversity, contributing to discrepancies between the simulated and actual building performance. This paper presents an Occupancy Simulator that simulates the stochastic behavior of occupant presence and movement in buildings, capturing the spatial and temporal occupancy diversity. Each occupant and each space in the building are explicitly simulated as an agent with their profiles of stochastic behaviors. The occupancy behaviors are represented with three types of models: (1) the status transition events (e.g., first arrival in office) simulated with Reinhart's LIGHTSWITCH-2002 model, (2) the random moving events (e.g., from one office to another) simulated with Wang's homogeneous Markov chain model, and (3) the meeting events simulated with a new stochastic model. A hierarchical data model was developed for the Occupancy Simulator, which reduces the amount of data input by using the concepts of occupant types and space types. Finally, a case study of a small office building is presented to demonstrate the use of the Simulator to generate detailed annual sub-hourly occupant schedules for individual spaces and the whole building.The Simulator is a web application freely available to the public and capable of performing a detailed stochastic simulation of occupant presence and movement in buildings. Future work includes 1 enhancements in the meeting event model, consideration of personal absent days, verification and validation of the simulated occupancy results, and expansion for use with residential buildings.

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Section: Discussionmentioning

confidence: 99%

An agent-based stochastic Occupancy Simulator

2017

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“…The aggregated presence and actions of all persons in a certain space yield the same model output as a space-based approach. Examples of person-based approaches include the agent-based stochastic occupancy simulator for office buildings by Chen et al [65,66].…”

Section: Person-based Approachesmentioning

confidence: 99%

A review on occupant behavior in urban building energy models

Happle

Fonseca

Schlueter

2018

Energy and Buildings

142

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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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“…1-2007 [18] recommends designing diverse occupancy factors for different building types and this designed occupancy schedule is useful for size operation when actual data are unknown, but it might cause 40% of variation compared with the actual occupancy information [19]. To simulate occupancy, some researchers built an agent-based stochastic occupancy simulator and also provided a web application for occupancy information simulation and visualization [20][21][22]. Using CO2 concentration, Wang et al developed several dynamic CO2-based models to estimate and predict occupancy in commercial buildings [23][24][25].…”

Section: Presence Count and Identity Of Occupancy Studiesmentioning

confidence: 99%

Modeling occupancy distribution in large spaces with multi-feature classification algorithm

Wang

Chen

Hong

2018

Building and Environment

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Occupancy information enables robust and flexible control of heating, ventilation, and airconditioning (HVAC) systems in buildings. In large-sized spaces, multiple HVAC terminals are usually installed to provide cooperative services for different thermal zones. Occupancy information for large spaces, such as people counts, determines the cooperation among terminals. However, a people count at room-level is not adequate to optimize HVAC system operation due to occupant movement within the room leading to uneven distribution of loads. Without an accurate knowledge of occupants' spatial distribution, uneven distribution of occupants often results in under-cooling/heating or over-cooling/heating in some thermal zones. Therefore, the lack of high-resolution occupancy distribution is often perceived as a bottleneck for future improvement in HVAC operation efficiency. To fill this gap, this study proposes a multi-feature based k-Nearest-Neighbors (k-NN) classification algorithm to extract occupancy distribution through reliable and low-cost Bluetooth Low Energy (BLE) networks. To demonstrate the proposed methods, an on-site experiment was conducted in a typical office of an institutional building. To validate the detection accuracy, the experiment outcomes were examined in three case studies, and one method based on City Block Distance (CBD) is used to measure the distance between detected occupancy distribution and ground truth and assess the results of occupancy distribution. The results show that the accuracy of CBD = 1 is over 71.4% and accuracy of CBD = 2 can reach 92.9%.

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Simulation and visualization of energy-related occupant behavior in office buildings

Cited by 67 publications

References 30 publications

An agent-based stochastic Occupancy Simulator

An agent-based stochastic Occupancy Simulator

A review on occupant behavior in urban building energy models

Modeling occupancy distribution in large spaces with multi-feature classification algorithm

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