Time filter

Source Type

Stanford, CA, United States

Kavousian A.,Stanford University | Rajagopal R.,Stanford University | Rajagopal R.,Stanford Sustainable Systems Laboratory | Fischer M.,Stanford University | Fischer M.,Center for Integrated Facility Engineering
Energy and Buildings | Year: 2015

This paper offers a novel method to rank residential appliance energy efficiency utilizing energy efficiency frontiers. The method is validated using a real-world case study of 4231 buildings in Ireland. Our results show that structural factors have the largest impact on energy efficiency, followed by socioeconomic factors and behavioral factors. For example, households with high penetration of efficient lightbulbs and double-glazed windows were on average 4 and 3.5% more efficient than others. Households with the head of household having higher education are on average 1.3% more efficient than their peers. Finally, households that track their energy savings are on average 0.4% more efficient than others. Furthermore, installing heater timers, wall insulation, and living in owned residences were correlated with higher efficiency. Generally, families with kids who have full-time employment and are highly-educated are more efficient compared to families with no kids, or families with retirees or unemployed members. This result has important implications for both targeting and messaging of energy efficiency programs. Some behavioral factors demonstrated significant impact on appliance energy efficiency. For instance, households that expressed interest in making major energy-saving lifestyle changes scored higher efficiency ranks on average. Conversely, households that expressed doubt about their motivation to save energy ranked lower in efficiency. This finding validates the role of educational programs to increase awareness about energy efficiency and its importance. In short, our results show that a data-driven analysis of a population is needed to develop a balanced view of the drivers of energy efficiency, and to devise a targeted approach to improve homes' energy efficiency. © 2015 Elsevier B.V. All rights reserved.

Garcia-Lopez N.P.,Stanford University | Fischer M.,Center for Integrated Facility Engineering
IIE Annual Conference and Expo 2014 | Year: 2014

To coordinate work effectively in a construction site, supervisors need to know who is doing what, where, and whether the work is progressing according to plan. Similarly, workers need to have clear instructions to execute the work effectively. Currently available solutions do not support information-sharing between the different participants in a construction site, resulting in decision bottlenecks and high coordination costs. In this paper we present a Work Tracking System (WTS) for construction. This system manages the information flows between project participants to support better communication about task scope, progress, and completion. The WTS automatically reports work progress and compares it to the original plan, allowing supervisors and workers to have a common understanding of the project status. We describe the implementation of the WTS system prototype, which leverages mobile devices and cloud computing to bring the technology to the field. We also present the feedback received from different participants in a mid-rise residential building project who evaluated the system. The evaluators liked the prototype's 4D visualization of the work performed, its facilitation of setting work priorities, and its support for accelerating decision-making. The feedback that we received also stressed the need to include filters that allow users to sort through information more efficiently and manage tasks at different levels of detail.

Kavousian A.,Center for Integrated Facility Engineering | Rajagopal R.,Center for Integrated Facility Engineering | Rajagopal R.,Stanford Sustainable Systems Laboratory | Fischer M.,Center for Integrated Facility Engineering
Energy | Year: 2013

We propose a method to examine structural and behavioral determinants of residential electricity consumption, by developing separate models for daily maximum (peak) and minimum (idle) consumption. We apply our method on a data set of 1628 households' electricity consumption. The results show that weather, location and floor area are among the most important determinants of residential electricity consumption. In addition to these variables, number of refrigerators and entertainment devices (e.g., VCRs) are among the most important determinants of daily minimum consumption, while number of occupants and high-consumption appliances such as electric water heaters are the most significant determinants of daily maximum consumption. Installing double-pane windows and energy-efficient lights helped to reduce consumption, as did the energy-conscious use of electric heater. Acknowledging climate change as a motivation to save energy showed correlation with lower electricity consumption. Households with individuals over 55 or between 19 and 35 years old recorded lower electricity consumption, while pet owners showed higher consumption. Contrary to some previous studies, we observed no significant correlation between electricity consumption and income level, home ownership, or building age. Some otherwise energy-efficient features such as energy-efficient appliances, programmable thermostats, and insulation were correlated with slight increase in electricity consumption. © 2013 Elsevier Ltd.

Discover hidden collaborations