Energies2015,8,10996-11029:doi:10.3390/en81010996 OPEN ACCESS energies ISSN1996-1073 www.mdpi.com/journal/energies Review A Review of Approaches for Sensing,Understanding,and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings Hamed Nabizadeh Rafsanjani,Changbum R.Ahn and Mahmoud Alahmad The Durham School of Architectural Engineering and Construction,University of Nebraska-Lincoln, 113 NH,Lincoln,NE 68588-0500,USA;E-Mails:hnabizadehrafsanj2@unl.edu(H.N.R.); malahmad2@unl.edu (M.A.) Author to whom correspondence should be addressed;E-Mail:cahn2@unl.edu; Tel.:+1-402-472-7431;Fax:+1-402-472-3742. Academic Editor:Hossam A.Gabbar Received:2 June 2015/Accepted:24 September 2015/Published:1 October 2015 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
Energies 2015, 8, 10996-11029; doi:10.3390/en81010996 energies ISSN 1996-1073 www.mdpi.com/journal/energies Review A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings Hamed Nabizadeh Rafsanjani, Changbum R. Ahn * and Mahmoud Alahmad The Durham School of Architectural Engineering and Construction, University of Nebraska-Lincoln, 113 NH, Lincoln, NE 68588-0500, USA; E-Mails: hnabizadehrafsanj2@unl.edu (H.N.R.); malahmad2@unl.edu (M.A.) * Author to whom correspondence should be addressed; E-Mail: cahn2@unl.edu; Tel.: +1-402-472-7431; Fax: +1-402-472-3742. Academic Editor: Hossam A. Gabbar Received: 2 June 2015 / Accepted: 24 September 2015 / Published: 1 October 2015 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 ACCESS
Energies 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. Keywords:commercial building;energy consumption;occupant energy use behavior; occupancy related approaches;review 1.Introduction The world's growing energy use raises concerns about energy consumption and its impacts,particularly in terms of resource consumption and environmental degradation.In the last two decades,global energy use has increased by 50 percent,and current predictions show an increasing trend of 2 percent in annual global energy consumption [1,2].Currently,residential and commercial buildings share 40 percent of this total global energy consumption [3]and are responsible for a similar percentage of CO2 emissions [4,5]. Such facts are particularly visible in the United States and European Union,where total energy-use in built environments is more pronounced than in other major energy end-use sectors-e.g.,industry and transportation [2,3].Contributing to this rising building energy use are population growth,increasing demand for maintaining a comfortable environment,and increasing time spent inside of buildings [2]. These factors point to the significance of residential and commercial building sectors in energy consumption [6,7].The commercial building sector currently consumes about 12 percent of global energy use and 21 percent of United States'total energy use [3].Its energy use intensity (energy per unit floor area per year)increased by 12 percent [8],and it has the greatest intensity rate when compared to residential or industrial sectors [9].In addition,the energy demands of the commercial sector currently has an increasing rate of 2.9 percent and continues to grow faster than other major sectors:industry, residential buildings,and transportation [3,10].Such energy use intensity and its increasing rate raise a critical concern about improving the energy performance of commercial buildings,which has brought about a greater emphasis on the importance of maximizing energy savings during the operational phase The need for improved operational efficiency has attracted attention from industry,research,and government to address energy saving approaches.Overall energy consumption in buildings during the operational phase generally depends on four main characteristics [2,11-17]:(1)climate characteristics (2)the building's physical characteristics;(3)appliances'and systems'characteristics and;(4)occupants' energy behavior characteristics.Improving climate characteristics is not possible at a given location. Enhancing the building's characteristics (building envelope)and appliance and system approaches require large capital investments and sometimes are infeasible for existing commercial buildings [13] This leaves occupants'energy behavior characteristics as a prime target for energy conservation [18-20]. The commercial built environment's energy use is highly connected to the energy-use behavior of its occupants [21-23].This behavior includes individual occupant's presence in a building and such occupants' actions and interactions that influence the energy-use of the building [24].These occupancy actions and interactions use up to 70 percent of the United States'total electricity of built environments [25]. A single occupancy-driven energy parameter-e.g.,heating,ventilation,and air conditioning (HVAC) set-pointscan impact building energy performance up to 40 percent [26,27],and uncertainties in
Energies 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. Keywords: commercial building; energy consumption; occupant energy use behavior; occupancy related approaches; review 1. Introduction The world’s growing energy use raises concerns about energy consumption and its impacts, particularly in terms of resource consumption and environmental degradation. In the last two decades, global energy use has increased by 50 percent, and current predictions show an increasing trend of 2 percent in annual global energy consumption [1,2]. Currently, residential and commercial buildings share 40 percent of this total global energy consumption [3] and are responsible for a similar percentage of CO2 emissions [4,5]. Such facts are particularly visible in the United States and European Union, where total energy-use in built environments is more pronounced than in other major energy end-use sectors—e.g., industry and transportation [2,3]. Contributing to this rising building energy use are population growth, increasing demand for maintaining a comfortable environment, and increasing time spent inside of buildings [2]. These factors point to the significance of residential and commercial building sectors in energy consumption [6,7]. The commercial building sector currently consumes about 12 percent of global energy use and 21 percent of United States’ total energy use [3]. Its energy use intensity (energy per unit floor area per year) increased by 12 percent [8], and it has the greatest intensity rate when compared to residential or industrial sectors [9]. In addition, the energy demands of the commercial sector currently has an increasing rate of 2.9 percent and continues to grow faster than other major sectors: industry, residential buildings, and transportation [3,10]. Such energy use intensity and its increasing rate raise a critical concern about improving the energy performance of commercial buildings, which has brought about a greater emphasis on the importance of maximizing energy savings during the operational phase. The need for improved operational efficiency has attracted attention from industry, research, and government to address energy saving approaches. Overall energy consumption in buildings during the operational phase generally depends on four main characteristics [2,11–17]: (1) climate characteristics (2) the building’s physical characteristics; (3) appliances’ and systems’ characteristics and; (4) occupants’ energy behavior characteristics. Improving climate characteristics is not possible at a given location. Enhancing the building’s characteristics (building envelope) and appliance and system approaches require large capital investments and sometimes are infeasible for existing commercial buildings [13]. This leaves occupants’ energy behavior characteristics as a prime target for energy conservation [18–20]. The commercial built environment’s energy use is highly connected to the energy-use behavior of its occupants [21–23]. This behavior includes individual occupant’s presence in a building and such occupants’ actions and interactions that influence the energy-use of the building [24]. These occupancy actions and interactions use up to 70 percent of the United States’ total electricity of built environments [25]. 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
Energies 2015,8 10998 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].Due to the fact that there are more non-working hours in a week than working hours,such behaviors can lead to more energy wasted during non-working hours than energy used during working hours [30].In this context,therefore,a growing number of recent studies emphasize the importance of improving occupant energy-use behaviors as a cost-effective approach for saving energy in commercial buildings;such work spans various research communities,including psychology and economics [31].It is of interest to explore how these studies address occupants'behaviors. A glance at the current literature shows that a considerable number of approaches of varying complexity have been proposed to address problems related to occupants'energy-use behaviors in commercial buildings.These approaches in the current literature can be grouped into the following three categories: 1.Monitoring occupant-specific energy consumption:This approach provides individual occupant energy-use information in order to understand the energy behavior of individual occupants. 2. Simulating occupant energy-consuming behaviors:This approach simulates realistic occupancy energy-use behaviors in order to capture and predict how such behaviors influence energy consumption in built environments and how such behaviors impact change over time. 3.Improving occupant energy-consuming behaviors:This approach aims to adjust energy-consuming behaviors among occupants in order to achieve the most ideal energy-saving potential in buildings. These three categories share the ultimate goal of improving occupant energy-use behaviors,and advances in one area are expected to lead to advances in another area.However,despite the clear attention given to research in each category,there has been no attempt to comprehensively review these three areas in order to identify the gaps between them and the potential areas for further research. Motivated by this lack in knowledge,the objective of this paper is to present a detailed,up-to-date review of various algorithms,models,and techniques employed in each area and to provide in-depth understanding on how the current literature in each area can be connected. In the subsequent sections,we will review the literature of each main approach,discuss the gaps within and between each area,and conclude with directions for future research. 2.Monitoring Occupant-Specific Energy Consumption Generally,commercial buildings contain a large number of end-users(i.e.,occupants and appliances). In buildings with a single tenant,a single meter is installed at the main electrical service to measure the total aggregate energy consumption of all end-users.In buildings with multiple tenants,a meter is installed to measure each tenant's aggregate consumption.In either case,the fact that the monitored energy consumption is an aggregate of all the users'and building's appliance(mechanical load,lighting, etc.)load significantly complicates the breakdown of observed energy loads to individual appliances or occupants [32,33]
Energies 2015, 8 10998 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]. Due to the fact that there are more non-working hours in a week than working hours, such behaviors can lead to more energy wasted during non-working hours than energy used during working hours [30]. In this context, therefore, a growing number of recent studies emphasize the importance of improving occupant energy-use behaviors as a cost-effective approach for saving energy in commercial buildings; such work spans various research communities, including psychology and economics [31]. It is of interest to explore how these studies address occupants’ behaviors. A glance at the current literature shows that a considerable number of approaches of varying complexity have been proposed to address problems related to occupants’ energy-use behaviors in commercial buildings. These approaches in the current literature can be grouped into the following three categories: 1. Monitoring occupant-specific energy consumption: This approach provides individual occupant energy-use information in order to understand the energy behavior of individual occupants. 2. Simulating occupant energy-consuming behaviors: This approach simulates realistic occupancy energy-use behaviors in order to capture and predict how such behaviors influence energy consumption in built environments and how such behaviors impact change over time. 3. Improving occupant energy-consuming behaviors: This approach aims to adjust energy-consuming behaviors among occupants in order to achieve the most ideal energy-saving potential in buildings. These three categories share the ultimate goal of improving occupant energy-use behaviors, and advances in one area are expected to lead to advances in another area. However, despite the clear attention given to research in each category, there has been no attempt to comprehensively review these three areas in order to identify the gaps between them and the potential areas for further research. Motivated by this lack in knowledge, the objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in each area and to provide in-depth understanding on how the current literature in each area can be connected. In the subsequent sections, we will review the literature of each main approach, discuss the gaps within and between each area, and conclude with directions for future research. 2. Monitoring Occupant-Specific Energy Consumption Generally, commercial buildings contain a large number of end-users (i.e., occupants and appliances). In buildings with a single tenant, a single meter is installed at the main electrical service to measure the total aggregate energy consumption of all end-users. In buildings with multiple tenants, a meter is installed to measure each tenant’s aggregate consumption. In either case, the fact that the monitored energy consumption is an aggregate of all the users’ and building’s appliance (mechanical load, lighting, etc.) load significantly complicates the breakdown of observed energy loads to individual appliances or occupants [32,33]
Energies 2015,8 10999 In order to estimate electrical consumption information for individual appliances,intrusive and non-intrusive load monitoring techniques have been widely employed in the related literature [34-41]. Intrusive load monitoring techniques require a meter to be installed at each point of interest (i.e.,at a specific appliance,in a specific office,at a specific receptacle and so forth).However,non-intrusive load monitoring (NILM)techniques rely on the existing available data from the building's electrical meter and employ techniques that identify specific signatures in order to associate energy use with the appliances in operation.In this context,NILM is considered a cost-effective tool to monitor appliance-specific energy consumption,and the current prevalence of NILM indicates its success and feasibility [34,41-43]. It is worth mentioning that the effectiveness of NILM in commercial buildings is quite limited due to the number and abundance of similar appliances in use simultaneously(e.g.,personal computers). Though NILM techniques work at an aggregate scale,there is still a need for effective tools to obtain detailed energy information regarding the consumption behaviors of individual occupants [44].Using individual plug-in level meters in order to find the energy consumption of each occupant at his or her workspace has been used to address this challenge [45,46].One criticism of this approach,though,is that this method is not reasonable in practice as it requires a large initial investment on the part of the business,which thereby decreases the likelihood that companies will adopt the approach.For this reason, researchers have begun looking for alternative means of tracking individual energy use.In their foundational work on this topic,Chen and Ahn [13]attempted to link energy-consuming data with occupancy-sensing data in order to track occupant-specific energy use without the need for capital-intensive plug-in meters.They proposed a coupled system that uses occupants'wireless devices'Wi-Fi connection/disconnection events to collect occupancy-sensing data and then correlates energy-load variations with these events to track occupant-specific energy use.This system confirmed that Wi-Fi connection information could be an effective indicator of energy load variations in commercial buildings.Therefore,this research capitalized on the breadth of research available regarding occupant detection in commercial buildings. Detection technologies typically include cameras [47],CO2 sensors [48],cellular phone control-channel traffic sensors [49],humidity sensors [50],infrared (IR)sensors [51],light sensors [52], motion sensors [53],radio frequency identification (RFID)[54],sound sensors [55],switch door sensors [56],telephone sensors [57],temperature sensors [50],ultra-wideband(UWB)[58],wireless sensor networks (WSN)[59],and Wi-Fi infrastructures [60].These detection technologies can be divided to two main groups [61]:(1)precise technologies with incomplete coverage (e.g.,cameras); and (2)imprecise technologies with full coverage (e.g.,Wi-Fi infrastructures).Cost efficiency, resolution,accuracy,non-intrusiveness,and occupants'privacy are criteria that must be evaluated for occupancy-detection techniques.For instance,some researchers point out that since there are usually multiple overlapping Wi-Fi access points in commercial buildings,Wi-Fi-based occupancy sensing could act as a cost-effective option [13]. In addition,the occupant resolution level of occupancy-sensing is significant for distinguishing the energy-load of a single occupant from a large group of people since the process of coupling occupancy with energy-load data aggregates energy-consumption for all persons within a specified location.There are four levels ofoccupant resolution(see Figure 1)[62]:(1)occupancy:a zone has at least one occupant in it;(2)count:the number of occupants in a zone;(3)identity:who they are;and (4)activity:what they
Energies 2015, 8 10999 In order to estimate electrical consumption information for individual appliances, intrusive and non-intrusive load monitoring techniques have been widely employed in the related literature [34–41]. Intrusive load monitoring techniques require a meter to be installed at each point of interest (i.e., at a specific appliance, in a specific office, at a specific receptacle and so forth). However, non-intrusive load monitoring (NILM) techniques rely on the existing available data from the building’s electrical meter and employ techniques that identify specific signatures in order to associate energy use with the appliances in operation. In this context, NILM is considered a cost-effective tool to monitor appliance-specific energy consumption, and the current prevalence of NILM indicates its success and feasibility [34,41–43]. It is worth mentioning that the effectiveness of NILM in commercial buildings is quite limited due to the number and abundance of similar appliances in use simultaneously (e.g., personal computers). Though NILM techniques work at an aggregate scale, there is still a need for effective tools to obtain detailed energy information regarding the consumption behaviors of individual occupants [44]. Using individual plug-in level meters in order to find the energy consumption of each occupant at his or her workspace has been used to address this challenge [45,46]. One criticism of this approach, though, is that this method is not reasonable in practice as it requires a large initial investment on the part of the business, which thereby decreases the likelihood that companies will adopt the approach. For this reason, researchers have begun looking for alternative means of tracking individual energy use. In their foundational work on this topic, Chen and Ahn [13] attempted to link energy-consuming data with occupancy-sensing data in order to track occupant-specific energy use without the need for capital-intensive plug-in meters. They proposed a coupled system that uses occupants’ wireless devices’ Wi-Fi connection/disconnection events to collect occupancy-sensing data and then correlates energy-load variations with these events to track occupant-specific energy use. This system confirmed that Wi-Fi connection information could be an effective indicator of energy load variations in commercial buildings. Therefore, this research capitalized on the breadth of research available regarding occupant detection in commercial buildings. Detection technologies typically include cameras [47], CO2 sensors [48], cellular phone control-channel traffic sensors [49], humidity sensors [50], infrared (IR) sensors [51], light sensors [52], motion sensors [53], radio frequency identification (RFID) [54], sound sensors [55], switch door sensors [56], telephone sensors [57], temperature sensors [50], ultra-wideband (UWB) [58], wireless sensor networks (WSN) [59], and Wi-Fi infrastructures [60]. These detection technologies can be divided to two main groups [61]: (1) precise technologies with incomplete coverage (e.g., cameras); and (2) imprecise technologies with full coverage (e.g., Wi-Fi infrastructures). Cost efficiency, resolution, accuracy, non-intrusiveness, and occupants’ privacy are criteria that must be evaluated for occupancy-detection techniques. For instance, some researchers point out that since there are usually multiple overlapping Wi-Fi access points in commercial buildings, Wi-Fi-based occupancy sensing could act as a cost-effective option [13]. In addition, the occupant resolution level of occupancy-sensing is significant for distinguishing the energy-load of a single occupant from a large group of people since the process of coupling occupancy with energy-load data aggregates energy-consumption for all persons within a specified location. There are four levels of occupant resolution (see Figure 1) [62]: (1) occupancy: a zone has at least one occupant in it; (2) count: the number of occupants in a zone; (3) identity: who they are; and (4) activity: what they
Energies 2015,8 11000 are doing.Considering all of these levels of occupancy resolution in conjunction with temporal and spatial resolution leads to correct and successful occupancy sensing. Days Hours Minutes Seconds Activity Identity 6u!pl!ng Count Occupancy Spatial resolution Temporal resolution Figure 1.Dimensions of occupancy sensing resolution [62] In commercial buildings,building management systems typically dedicate operational settings of main end-users-such as HVAC-according to assumed occupied and unoccupied periods during a day [63].However,it has been found that average building occupancy for commercial buildings is at most a third of its maximum designed-for occupancy,even among office spaces at their peak working hours [64].In this regards,occupancy-sensing data provides significant information for building management systems to adapt their system-e.g.,HVAC and lighting-according to the exact number of occupants in a building at a given time [65-67].The current status of sensing technologies therefore provides opportunities to economically monitor individual occupants and their energy consumption [68,69]. Concerning the linkage between aggregated energy data and occupancy-sensing data in commercial buildings,in order to find the energy use of individual occupants,Kavulya and Becerik-Gerber [70] linked the results of occupants'observations with NILM to study individual occupant's energy-consuming behaviors in an office environment.They employed visual observation in order to collect occupancy-sensing data.Their research was conducted for five weeks in an office space containing five occupants,and their results identified the energy consumption and potential waste ofeach occupant. The outcome of their research indicated the ability of the linkage concept to monitor occupant-specific energy consumption.Although visual observation is not an effective method for collecting occupancy-sensing data,this research revealed opportunities for further research into the concept of coupling NILM with occupancy-sensing technologies to track the energy consumption of individual occupants. 3.Simulating Occupant Energy-Consuming Behaviors Nowadays,simulation approaches are widely used in various branches of science in order to model a real process over time.In built environments,a number of simulation models and software exist to predict energy consumption during the operational phase.These common,traditional energy software (e.g.,BLAST,DOE-2.2,eQUEST,EnergyPlus,and ENERGY-10)are typically employed during the construction phase of buildings to simulate and predict the energy use within the operational phase
Energies 2015, 8 11000 are doing. Considering all of these levels of occupancy resolution in conjunction with temporal and spatial resolution leads to correct and successful occupancy sensing. Figure 1. Dimensions of occupancy sensing resolution [62]. In commercial buildings, building management systems typically dedicate operational settings of main end-users—such as HVAC—according to assumed occupied and unoccupied periods during a day [63]. However, it has been found that average building occupancy for commercial buildings is at most a third of its maximum designed-for occupancy, even among office spaces at their peak working hours [64]. In this regards, occupancy-sensing data provides significant information for building management systems to adapt their system—e.g., HVAC and lighting—according to the exact number of occupants in a building at a given time [65–67]. The current status of sensing technologies therefore provides opportunities to economically monitor individual occupants and their energy consumption [68,69]. Concerning the linkage between aggregated energy data and occupancy-sensing data in commercial buildings, in order to find the energy use of individual occupants, Kavulya and Becerik-Gerber [70] linked the results of occupants’ observations with NILM to study individual occupant’s energy-consuming behaviors in an office environment. They employed visual observation in order to collect occupancy-sensing data. Their research was conducted for five weeks in an office space containing five occupants, and their results identified the energy consumption and potential waste of each occupant. The outcome of their research indicated the ability of the linkage concept to monitor occupant-specific energy consumption. Although visual observation is not an effective method for collecting occupancy-sensing data, this research revealed opportunities for further research into the concept of coupling NILM with occupancy-sensing technologies to track the energy consumption of individual occupants. 3. Simulating Occupant Energy-Consuming Behaviors Nowadays, simulation approaches are widely used in various branches of science in order to model a real process over time. In built environments, a number of simulation models and software exist to predict energy consumption during the operational phase. These common, traditional energy software (e.g., BLAST, DOE-2.2, eQUEST, EnergyPlus, and ENERGY-10) are typically employed during the construction phase of buildings to simulate and predict the energy use within the operational phase