Rimas Gulbinas

Segmentation and Classification of Commercial Building Occupants by Energy-Use Efficiency and Predictability

Drawing inspiration from utility-scale customer segmentation research initiatives, a new set of metrics is introduced that serve as quantitative measures of building occupant energy efficiency and energy-use predictability. Building occupant energy-use data is segmented to facilitate the construction of independent energy-use profiles for workdays, nonworkdays, work hours, and nonwork hours, which in turn enable further classification of building occupants according to their energy-use patterns. The three new metrics, building occupant energy-use efficiency, entropy, and intensity enable the design of more targeted energy conservation campaigns. Building occupants with relatively low energy-use efficiency scores can be individually targeted for behavioral interventions aimed at increasing the efficiency of their energy-use. Furthermore, building occupants with relatively low energy-use entropy scores can be sent timely behavior intervention notifications based on predictions of their future energy-use. Finally, building occupants with relatively high energy-use intensity scores can be targeted for equipment upgrades in order to reduce their overall energy consumption. We present the methodology behind the construction of these metrics and demonstrate how they can be applied to classify commercial building occupants based on their energy-use.

Web-based eco-feedback visualization of building occupant energy consumption in support of quantifying social network effects on conservation

Exposing building occupants to information about their energy use and the energy use of others in their social network through eco-feedback systems has been shown to significantly impact occupant energy consumption. In this paper, we describe the design and development of a web-based visualization system that exposes building occupants to real-time information about their individual energy utilization, the utilization of their peers in the building, and the average energy utilization of all monitored individuals in the building. The system also monitors and records user interaction with the system and other users of the system providing relevant usage data for conducting analysis to quantify network effects on user energy consumption. A description of the system’s physical and virtual architecture and how its design enables meaningful analysis, visualization and effective user monitoring is provided. We conclude by presenting the methods and challenges related to how our visualization system was developed to facilitate and monitor social interaction around energy conservation, and how it enables research into the underlying mechanisms that drive these actions.

An Empirical Comparison of Internal and External Load Profile Codebook Coverage of Building Occupant Energy-Use Behavior

Technology independent elements such as the behavior of building occupants are significant factors responsible for energy consumption and associated CO2 emissions of the buildings. Predicting building occupants’ energy-use has been identified as one of the most challenging processes due to high intra-class variability of occupant behavior. Previous research has benefited from classification techniques that serve to simplify the energy profiling by creating a codebook of energy-use patterns for each user. Nevertheless, the optimum level and type of energy-use behavior classification system (i.e., energy-use codebook) remains unknown. In this paper, we build on previous work and compare the relative effectiveness of various approaches to simplified representations, or codebooks, of building occupant energyuse behavior. We introduce the methodologies behind the construction of the codebooks and conclude that individually generated codebooks are a more accurate approach to model occupants’ energy-use compared to the globally generated codebooks. This contributes to the creation of more reliable energy-use profiling methods that can enhance the efficacy of automated energy efficiency programs.

OESPG: Computational Framework for Multidimensional Analysis of Occupant Energy Use Data in Commercial Buildings

AbstractCommercial buildings account for much of the energy use both in the United States and globally. The role of occupant behavior within the physical building has been found to be an important ...