Agent-Based Modeling of Occupants and Their Impact on Energy Use in Commercial Buildings

Azar, Elie; Menassa, Carol C.

doi:10.1061/(asce)cp.1943-5487.0000158

Cited by 215 publications

(128 citation statements)

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“…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%

“…A single occupancy-driven energy parameter-e.g., heating, ventilation, and air conditioning (HVAC) set-points-can impact building energy performance up to 40 percent [26,27], and uncertainties in occupancy energy-use behaviors can significantly impact total annual energy use on the order of 150 percent for the commercial sector [8]. Occupant actions can also lead to excessive and unnecessary energy consumption [28]. In the United States' commercial built environment, less than half of most buildings' appliances and systems are turned off by occupants after operational hours [29].…”

Section: Introductionmentioning

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: Simulating Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2015

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“…However, some studies e.g. [24][25][26] show that more than 50% of occupants leave their computers on when leaving a space. In order to address this concern, Milenkovic and Amft [27] proposed a dual technology occupancy detection system that combined PIR sensors and plug monitors for different equipment.…”

Section: Wired Network-based and Energy-related Solutionsmentioning

confidence: 99%

Effectiveness of using WiFi technologies to detect and predict building occupancy

et al. 2017

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Abstract. This paper presents findings of a case-study demonstrating the effectiveness of using WiFi networks to detect occupancy as opposed to CO 2 sensors, commonly used for demand-controlled heating, ventilation and air conditioning (HVAC) systems. The study took place in one building at the University of Manitoba Fort Garry campus in Canada. In a classroom, the number of WiFi connections was collected on an hourly basis over one-week, simultaneously with CO 2 concentration levels at 10-min intervals. The number of occupants in this classroom was also counted on an hourly basis over the same study period. Data analysis showed that WiFi counts predicted actual occupancy levels more accurately than CO 2 concentration levels, thus validating the use of this technology to track occupancy. This study was the first to use both CO 2 concentration and WiFi counts simultaneously as indicators for occupancy. Results demonstrated the possibility of using WiFi counts in large buildings for controlling HVAC systems at a higher accuracy and lower cost than other sensor technologies. Implications and influences: Given the large contribution of HVAC systems to overall buildings' energy consumption, this study presents a new method for efficiently operating HVAC systems. Results highlighted the accuracy of using WiFi connections as predictors for occupancy patterns to be used for controlling HVAC systems instead of CO 2 sensors. These findings provide a foundation for further research on using WiFi networks to manage and operate HVAC systems in new buildings. Efficient operation of these systems based on real-time occupancy as opposed to static schedules provides facility managers with an opportunity for significant energy savings at a relatively low cost.Keywords: buildings energy management / occupancy and energy consumption / smart buildings systems / green buildings / sensor-based HVAC systems

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“…Figure 1 (Menassa et al 2013) illustrates how two federates, a building energy prediction federate (DOE2 Federate) and a building occupancy federate (Anylogic Federate), exchange an energy consumption parameter and a behavior level parameter. The Anylogic Federate is a computational agent-based model that simulates occupancy as a variable element by assigning attributes and characteristics to building occupants such as energy consumption estimates that correspond to different and changing energy consumption behaviors of occupants over time (Azar and Menassa 2012). The DOE2 federate uses BIM (Building Information Modeling) to generate the initial DOE2 energy simulation federate input (Kim and Anderson 2012).…”

Section: Model Descriptionmentioning

confidence: 99%

“…On the other hand, the use of Energy Management Control Systems (EMCS), which consists of both hardware and software components to control and monitor building operations, allows facility managers to perform activities such as reducing demand charges by managing and scheduling equipment loads (EIA 2012;Andrews and Krogmann 2009). Another approach to ensure energy efficiency during operations phase focuses on occupancy education and feedback in residential buildings (Peschiera and Taylor 2012;Peschiera et al 2010;Abrahamse et al 2005) and commercial buildings (Azar and Menassa 2012). Whether it is energy management or occupancy interventions, building stakeholders can significantly benefit from coupling these resources under one simulation model where they can test strategies and analyze their impact on the building before their actual implementation.…”

Section: Introductionmentioning

confidence: 99%

Coupling Distributed Energy Simulation and Occupancy Models for Comprehensive Building Energy Consumption Analysis

Menassa

Kamat

Lee

et al. 2013

Computing in Civil Engineering

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We propose a conceptual framework that couples energy modeling with occupancy characteristics and energy use data to achieve comprehensive building energy consumption analysis. Specifically, we aim: 1) to couple distinct and spatially distributed simulation models and synchronize their data exchange; and 2) to demonstrate the coupled simulation through a hypothetical case example of a building.This framework has been developed based on the principles defined in the High-Level Architecture (HLA) that enables distributed computing. In other words, the HLA-compliant federation allows federates (e.g., models) to communicate with each other and exchange relevant information to achieve the global objective of analyzing and reducing the building"s energy use. A case study of a typical commercial building illustrates how the federates coordinate data synchronization and run in a distributed fashion. This example tests feedback frequency to building occupants on the building"s energy use and illustrates the potential application of the framework to study energy interventions in buildings. 275Computing in Civil Engineering (2013) Downloaded from ascelibrary.org by University of Michigan on 09/11/13.

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Agent-Based Modeling of Occupants and Their Impact on Energy Use in Commercial Buildings

Cited by 215 publications

References 3 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

Effectiveness of using WiFi technologies to detect and predict building occupancy

Coupling Distributed Energy Simulation and Occupancy Models for Comprehensive Building Energy Consumption Analysis

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