• Energy Research

Abstract Office-building occupants' behaviors during their arrivals and departures foreseeably have a large impact on a building's energy consumption since many occupants in control of appliances will turn their devices on or off at these entry and departure events. Consequently, occupants would have various types of energy-use patterns that coincide with their entry and departure events and that repeat over time. Despite the value that knowledge of such patterns would have on better tracking energy-use behaviors, these patterns have not been well explored with empirical data in the literature. Therefore, this paper studies occupants' energy-use behaviors in office buildings to identify and investigate energy-use patterns at entry and departure events. In particular, this research evaluates (1) the delay intervals that manifest between the occupants' entry/departure events and the beginning/end of the occupants' energy-consuming behaviors, and (2) changes in electricity consumption caused by occupants at entry/departure events to identify recurring—and thereby predictable—energy-use patterns associated with individual occupants. In the pursuit of this objective, the energy-use behaviors of 12 occupants in two office buildings were tracked during a four-month period. Results from statistical analyses performed on the collected data reveal that an occupant in an office building typically follows a consistent, recurring delay-interval pattern. In addition, the results show each occupant also follows a recurring pattern of power changes at entry/departure events. By identifying recurring, occupant-specific energy-use behavior patterns, this study significantly contributes to the current body of research and can be used to support research efforts into energy-load disaggregation.

Abstract Providing energy-consumption feedback has proven to be an effective approach for changing people's behavior and has led to significant energy-consumption reductions in residential buildings. However, providing feedback in commercial and educational buildings is challenging because of the difficulty in tracking occupants’ behaviors and their corresponding energy usage – especially for temporary occupants. To make providing such feedback possible in commercial and educational buildings, this paper presents the framework for a coupled system that uses residents’ wireless devices’ Wi-Fi connection and disconnection events to detect occupancy and then benchmarks energy loads against these events to monitor the energy use of occupants. A preliminary experiment implemented the proposed approach in a small-scale educational building to ascertain whether Wi-Fi network connection/disconnection events can be an effective indicator of energy load variation. The experiment's results confirmed the positive relationship between the Wi-Fi connection events and energy load increase; these results also indicated that the number of Wi-Fi connections cannot directly represent the magnitude of the energy load. A validation test was also conducted to assess the robustness of the coupled system in terms of the impact of users’ schedules (AM/PM), their length of stay (long-term/temporary), and the locations of access points.

Abstract Occupants’ energy-consuming behaviors have a significant influence on overall energy consumption in commercial buildings. Accordingly, understanding and intervening in these behaviors offers a significant opportunity for energy savings in commercial buildings. Current approaches to behavior modification rely on available occupant-specific energy consumption data, but capturing such data is generally expensive. One possible solution to this challenge is to link energy consumption to individual occupants’ energy-use behaviors in commercial buildings. In this context, this study proposes a non-intrusive occupant load monitoring (NIOLM) approach that couples occupancy-sensing data—captured from existing Wi-Fi infrastructures—with power changes in aggregate building-wide energy data to thereby disaggregate building-wide data down to the individual. This paper describes two case studies that investigate the feasibility of using the NIOLM approach to identify occupant-specific energy consumption information. Tracking eleven occupants’ energy-use behaviors using NIOLM over a four-month period resulted in an average F-measure of 0.778 and Accuracy of 0.955. The case studies thereby demonstrated that NIOLM successfully tracks individual occupants’ energy-consuming behaviors at minimal cost by utilizing existing high-resolution metering devices and Wi-Fi network infrastructures in commercial buildings.

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 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.

Abstract Studies indicate that providing building occupants with personalized energy-use feedback effectively triggers energy-use behavior modifications. However, gathering such personalized information in a commercial building using conventional techniques is currently extremely expensive. Accordingly, this study proposes a novel framework that disaggregates building-wide energy data down to the level of individual occupants by harnessing recurring patterns in occupants’ energy-use behaviors. To achieve such disaggregation, the framework utilizes a density-based clustering algorithm that deciphers patterns amidst occupants’ sensed entry/exit events and the building's corresponding changes in energy-load magnitudes, load-change timings, and energy-use locations. Experimental results of two commercial buildings with an average F-measure of 0.807 and Accuracy of 0.958 demonstrate the feasibility and accuracy of the framework in generating personalized information. By gathering such data in an economically feasible manner, the framework can provide a cost-effective means for individualizing feedback, which has been shown to yield long-term decreases in commercial buildings’ energy consumption.

In the building and construction sector, most efforts related to sustainable development have concentrated on the environmental performance of the operation of buildings and infrastructure. However, several studies have called for the need to mitigate the considerable environmental impacts, especially air pollutant emissions and energy consumption, generated by construction processes. To provide a point of reference for initiating the development of environmentally sustainable construction processes, this article identifies energy consumption and air emissions resulting from construction activities and examines previous approaches utilized to assess such environmental impact. This research also identifies the opportunities and challenges to mitigate such environmental impact from construction processes, based on the investigation of current technology policies, regulations, incentives, and guidelines.

AbstractStudies indicate that occupancy-related energy-use behaviors have a significant influence on overall energy consumption in commercial buildings. In this context, understanding and improving occupants’ energy-consuming behaviors shows promise as a cost-effective approach to decreasing commercial buildings’ energy demands. Current behavior-modification pursuits rely on the data availability of occupant-specific energy consumption, but it is still quite challenging to track occupant-specific energy-consuming behaviors in commercial buildings. On the other hand, individual occupants have unique energy-consumption patterns at their entry and departure events and will typically follow such patterns consistently over time. Thus, analyzing occupants’ energy-use patterns at the time of their entry and departure events plays a critical role in understanding individual occupants’ energy-use behaviors. To this end, this paper aims to develop a non-intrusive occupant load monitoring (NIOLM) approach that profiles individual occupants’ energy-use behaviors at their entry and departure events. The NIOLM approach correlates occupancy-sensing data captured from existing Wi-Fi networks with aggregated building energy-monitoring data in order to disaggregate building energy loads to the level of individual occupants. Results from a 3-month long period of tracking individual occupants validate the feasibility of the NIOLM approach by comparing the framework's outcomes with the individual metering data captured from plug-load sensors. By utilizing existing devices and Wi-Fi network infrastructure, NIOLM provides a new opportunity for current industry and research efforts to track individual occupants’ energy-use behaviors at a minimal cost.