Feasibility of plug-load monitoring and energy-saving interventions in residential and office buildings on the University of Washington campus

The University of Washington (UW) is aiming to reduce the overall electricity consumption on campus as part of its Climate Action Plan launched in 2009. To achieve this goal, UW installed 216 smart grid meters and automatic heating, ventilation, and cooling control systems across the entire campus and acquired over 200 sets of plug-load monitoring equipment. The university used the smart grid data and the monitored plug-load data to test how occupants in selected residence halls responded to receiving detailed information about their energy usage patterns, its environmental impacts, and associated costs. The experiment demonstrated that in residence halls, plug-load monitoring did not have any significant impact on the occupants' electricity consumption. Hence, there is still a need to further assess which strategies are effective in achieving long-term electricity reduction goals for the university. The goal of this study was to conduct a comparative analysis by replicating the plug-load analysis conducted in residence halls in a faculty/staff office setting. The study entailed interviewing university administrators that were involved in the residence hall plug-load study. Interviewees were asked questions about the findings, shortcomings, and recommendations for future studies. Also, this study characterized the load profiles of the faculty/staff offices by monitoring the plug-load consumption in four offices for nine weeks and explored plug-load reduction interventions applicable to office settings. The study found that the unreliable network connection caused frequent disruptions in data collection and strong bias in the individual electricity consumption data. The inventory of electronic appliances in the monitored offices revealed a high variability in the number of devices which lead to variations in base consumption and peak plug loads between faculty offices, and lack of occupant engagement was found to be the main challenge in the implementation of plug load monitoring campaigns. The results provide universities around the country with valuable information and insights on how to design and implement an on campus plug-load reduction intervention with quantifiable energy-saving potential.

[1]  James A. Landay,et al.  The design of eco-feedback technology , 2010, CHI.

[2]  John E. Taylor,et al.  Investigating the impact eco-feedback information representation has on building occupant energy consumption behavior and savings , 2013 .

[3]  Sanem Sergici,et al.  The Impact of Informational Feedback on Energy Consumption -- A Survey of the Experimental Evidence , 2009 .

[4]  Masanori Shukuya,et al.  Comparative effects of building envelope improvements and occupant behavioural changes on the exergy consumption for heating and cooling , 2010 .

[5]  John E. Taylor,et al.  Assessing Eco-Feedback Interface Usage and Design to Drive Energy Efficiency in Buildings , 2012 .

[6]  O. T. Masoso,et al.  The dark side of occupants’ behaviour on building energy use , 2010 .

[7]  Ahmad Faruqui,et al.  The impact of informational feedback on energy consumption d A survey of the experimental evidence , 2010 .

[8]  Girish Ghatikar,et al.  Miscellaneous and Electronic Loads Energy Efficiency Opportunities for Commercial Buildings: A Collaborative Study by the United States and India , 2014 .

[9]  Michael Nye,et al.  Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term , 2013 .

[10]  A. Marcus,et al.  Introduction to the Special Research Forum on the Management of Organizations in the Natural Environment: A Field Emerging From Multiple Paths, With Many Challenges Ahead , 2000 .

[11]  Frederick G. Conrad,et al.  Does Conversational Interviewing Reduce Survey Measurement Error , 1997 .

[12]  F. Siero,et al.  Changing organizational energy consumption behaviour through comparative feedback , 1996 .

[13]  A. S. Getchell,et al.  Agent-based Modeling , 2008 .

[14]  Tham Kwok Wai,et al.  A literature survey on measuring energy usage for miscellaneous electric loads in offices and commercial buildings , 2014 .

[15]  Filipe Quintal,et al.  Understanding the Limitations of Eco-feedback: A One-Year Long-Term Study , 2013, CHI-KDD.

[16]  John E. Taylor,et al.  The impact of combined water and energy consumption eco-feedback on conservation , 2014 .

[17]  R. Tourangeau,et al.  Sensitive questions in surveys. , 2007, Psychological bulletin.

[18]  Therese Peffer,et al.  Revealing Occupancy Diversity Factors in Buildings Using Sensor Data , 2014 .

[19]  I. Vassileva,et al.  The impact of consumers’ feedback preferences on domestic electricity consumption , 2012 .

[20]  Muhammad Imran,et al.  Individual energy use and feedback in an office setting: A field trial , 2013 .

[21]  W. Abrahamse,et al.  Factors Related to Household Energy Use and Intention to Reduce It : The Role of Psychological and Socio-Demographic Variables , 2011 .

[22]  Osamu Saeki,et al.  Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data , 2006 .

[23]  Corinna Fischer Feedback on household electricity consumption: a tool for saving energy? , 2008 .

[24]  Peter Morris,et al.  The Effectiveness of Energy Feedback for Conservation and Peak Demand: A Literature Review , 2013 .

[25]  Michael Nye,et al.  Making energy visible: A qualitative field study of how householders interact with feedback from smart energy monitors , 2010 .

[26]  Rishee K. Jain,et al.  Can social influence drive energy savings? Detecting the impact of social influence on the energy consumption behavior of networked users exposed to normative eco-feedback , 2013 .

[27]  Elie Azar,et al.  A conceptual framework to energy estimation in buildings using agent based modeling , 2010, Proceedings of the 2010 Winter Simulation Conference.

[28]  Dirk Helbing,et al.  Agent-Based Modeling , 2012 .

[29]  James A. Davis,et al.  Occupancy diversity factors for common university building types , 2010 .