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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Energy consumption in office buildings highly depends on occupant energy-use behaviors and intervening these behaviors could function as a cost-effective approach to enhance energy savings. Current behavior-intervention techniques extensively rely on occupant-specific energy-use information at the workstation level and often ignore shared appliances. It is because an occupant typically has full responsibility for her workstation appliances energy consumption and shares the responsibility of the shared appliances energy consumption.However, understanding energy-use behavior of both workstation and shared appliances is necessary for applying appropriate behavior-intervention techniques. Despite this importance, there is still no practical and scalable method to capture personalized energy-use information of workstation and shared appliances since the conventional methods use plug-in power meters that are extremely expensive and difficult to maintain over long period of time. To address this gap, we propose a comprehensive occupant-level energy-usage approach which utilizes the data from the internet of things devices in office buildings to provide information related to energy-use behavior of workstation and shared appliances of each occupant in an economical and feasible manner. In particular, we introduce an energy behavior index which quantitatively compares individual occupants' energy-consuming data to identify high energy consumers and inefficient behaviors. Results from an experiment conducted in an office building equipped with internet of things devices demonstrate the feasibility of the proposed approach to classify occupants to different energy-usage categories. Our proposed approach along with appropriate behavior-intervention techniques could be used to impact occupant energy-use behaviors.

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