A simulation approach to estimate energy savings potential of occupant behavior measures

Sun, Kaiyu; Hong, Tianzhen

doi:10.1016/j.enbuild.2016.12.010

Cited by 101 publications

(46 citation statements)

References 46 publications

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“…As a web-based application, it is easy to maintain as no software installation is needed on the client side, and it is available for all operation systems and devices (even for mobile phones). 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.…”

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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“…An average whole-building occupant schedule is normalized and not able to reflect the realistic occupant movement and the variations between different zones within the buildings. Especially for occupant-based control, occupant schedules are critical input for accurately estimating the energy performance, as elaborated in Sun's study [66].…”

Section: Occupant Schedulesmentioning

confidence: 99%

A framework for quantifying the impact of occupant behavior on energy savings of energy conservation measures

Sun

Hong

2017

Energy and Buildings

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173

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To improve energy efficiency-during new buildings design or during a building retrofit-evaluating the energy savings potential of energy conservation measures (ECMs) is a critical task. In building retrofits, occupant behavior significantly impacts building energy use and is a leading factor in uncertainty when determining the effectiveness of retrofit ECMs. Current simulation-based assessment methods simplify the representation of occupant behavior by using a standard or representative set of static and homogeneous assumptions ignoring the dynamics, stochastics, and diversity of occupant's energy-related behavior in buildings. The simplification contributes to significant gaps between the simulated and measured actual energy performance of buildings.This study presents a framework for quantifying the impact of occupant behaviors on ECM energy savings using building performance simulation. During the first step of the study, three occupant behavior styles (austerity, normal, and wasteful) were defined to represent different levels of energy consciousness of occupants regarding their interactions with building energy systems (HVAC, windows, lights and plugin equipment). Next, a simulation workflow was introduced to determine a range of the ECM energy savings. Then, guidance was provided to interpret the range of ECM savings to support ECM decision making. Finally, a pilot study was performed in a real building to demonstrate the application of the framework. Simulation results show that the impact of occupant behaviors on ECM savings vary with the type of ECM. Occupant behavior minimally affects energy savings for ECMs that are technology-driven (the relative savings differ by less than 2%) and have little interaction with the occupants; for ECMs with strong occupant interaction, such as the use of zonal control variable refrigerant flow system and natural ventilation, energy savings are significantly affected by occupant behavior (the relative savings differ by up to 20%). The study framework provides a novel, holistic approach to assessing the uncertainty of ECM energy savings related to occupant behavior, enabling stakeholders to understand and assess the risk of adopting energy efficiency technologies for new and existing buildings.

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“…Static occupancy schedules, such as the average results for all occupancy schedules or fixed schedules from design standards, are widely used in building performance simulations. However, static schedules could not reflect the realistic occupant movement and the variations between spaces within the buildings owing to their temporally and spatially stochastic nature [20]. 6 This study adopted the approach for building occupancy simulation proposed by Wang et al [41].…”

Section: Occupancy Schedulesmentioning

confidence: 99%

“…(2) For the environment/temperature triggered behavior modes, i.e., switch on air-conditioning when feeling hot, switch off air-conditioning when feeling cold, open window when feeling hot, and close window when feeling cold, this study adopted the method in Sun et al [20] to determine the parameters. This study assumed that the probability of turning on air-conditioning is about 20% at the suggested cooling temperature set point in the Chinese design standard (i.e., 26°C) [8] and about 50%…”

Section: Typical Modes Of Occupant Behavior Typesmentioning

confidence: 99%

“…Gilani et al [18] and Hoes et al [19] examined the importance of occupant behavior models in simulation-aided design and code compliance in Canada and the Netherlands. Sun et al [20] concluded that energy-saving occupant behaviors could achieve overall energy savings as high as 22.9% for individual behaviors and up to 41.0% for integrated behaviors.…”

Section: Introductionmentioning

confidence: 99%

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

Yan

Hong

et al. 2017

Applied Energy

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District cooling systems are widely used in urban residential communities in China. Most district cooling systems are oversized; this leads to wasted investment and low operational efficiency and thus energy wastage. The accurate prediction of district cooling loads that supports rightsizing cooling plant equipment remains a challenge. This study developed a new stochastic modeling method that includes (1) six prototype house models representing a majority of apartments in the district, (2) occupant behavior models in residential buildings reflecting the temporal and spatial diversity and complexity based on a large-scale residential survey in China, and (3) a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was employed in a case study using the DeST simulation engine to simulate the cooling loads of a real residential district in Wuhan, China. The simulation results agree well with the actual measurement data based on five performance metrics representing the aggregated cooling loads, the peak cooling loads as well as the spatial load distribution, and the load profiles. Two currently used simulation methods were also employed to simulate the district cooling loads. The simulation results showed that oversimplified occupant behavior assumptions lead to significant overestimations of the peak cooling load and total district cooling loads. Future work will aim to simplify the workflow and data requirements of the stochastic method to enable its practical application as well as explore its application in predicting district heating loads and in commercial or mixed-use districts.

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A simulation approach to estimate energy savings potential of occupant behavior measures

Cited by 101 publications

References 46 publications

An agent-based stochastic Occupancy Simulator

An agent-based stochastic Occupancy Simulator

A framework for quantifying the impact of occupant behavior on energy savings of energy conservation measures

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

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