A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings

Azar, Elie; Menassa, Carol C.

doi:10.1016/j.enbuild.2012.10.002

Cited by 216 publications

(98 citation statements)

References 22 publications

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“…The first point that the current literature failed to consider is the effect of ambient temperature and humidity on occupant behavior in commercial buildings. Occupancy energy-use behavior varies according to weather conditions [73,150]. Individual occupant's behaviors may have a larger impact on energy consumption in hot-dry climates than in mild-humid ones [8].…”

Section: Overview Of Current Approachesmentioning

confidence: 99%

“…It is noteworthy that the developed energy-modeling and simulation tools for modeling occupants' energy-related characteristics and behaviors (discussed below) are mainly used during the early phase (i.e., design phase) of buildings [73,[76][77][78]. Such tools could help users to choose the correct size and most energy-efficient building systems and the appliances that are proportionate to the number of occupants.…”

Section: Simulating Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

“…These tools, therefore, help to improve overall building simulation capabilities. However, to achieve the best results, the application of these tools should be very sensitive to occupants' input parameters to accurately represent occupants' actions [73,79]. In fact, these tools could be used to analyze the specific dynamics for all individual occupants, and could be calibrated to ensure that they can be used for all sizes of commercial buildings with different numbers of occupants.…”

Section: Simulating Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

“…The main limitation is that they assume the same energy use pattern for all occupants in a building, and this pattern is constant over time [18,24,28,[71][72][73]. In fact, they are not able to account for dynamic aspects of occupancy.…”

Section: Simulating Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

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A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

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Abstract:Buildings currently account for 30-40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States' energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants' energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings' energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants' energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants' energy-consuming behaviors in order to assess and predict such characteristics' impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change OPEN ACCESSEnergies 2015, 8 10997 occupants' energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided.

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Section: Overview Of Current Approachesmentioning

confidence: 99%

Section: Simulating Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

Section: Simulating Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

Section: Simulating Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

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A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

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“…Meanwhile, activity recognition has been utilized for energy management systems and user convenience in buildings using ontology [7]. Device control based on occupant behavior, according to the conditions and size of the building and weather [8] and predicting the user behavior using embedded and wearable sensors [9] have been done for energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Design of Control Model for Multi-User Preference and Activity in Smart Building System for Energy Saving

Fauzi¹,

Sulistyo²,

Widyawan³

2018

IJIES

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Abstract:The environmental adaptability of the smart building is required for energy efficiency. The schedulingbased control model that widely implemented, have a user dependence and assumes maximum occupancy regardless of the occupant's desire in the energy usage requirement during the activity (i.e., ignoring user preferences). In this paper, we present our study on a building control model based on user preference, presence, location, and activity. We present it in a formal model, including conflict resolution techniques on multi-user preference (minimum, maximum, and average preference models). The contribution of this study is the optimization of control model for energy efficiency that also meet multi-user preference. The evaluation of the proposed control model is done through simulation and is compared with the scheduling-based control models. The results show that the minimum, maximum, and average preferences have an energy consumption of 73.5 kWh/day, 44.5 kWh/day, and 58.9 kWh/day, respectively, which are more efficient than the scheduling-based control model. If the Euclidean distance is used to estimate the error value between temperature and light actuation to multi-user preference, the lowest error is the average preference.

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Uncertainty in Refurbishment Investment

Menassa

Ortiz-Vega

2013

Nearly Zero Energy Building Refurbishment

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A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings

Cited by 216 publications

References 22 publications

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Design of Control Model for Multi-User Preference and Activity in Smart Building System for Energy Saving

Uncertainty in Refurbishment Investment

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