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Utilizing the Interconnectivity of Multi-Sector Communities for Innovative Building Energy Efficiency Methods

Ketchman, Kevin (2018) Utilizing the Interconnectivity of Multi-Sector Communities for Innovative Building Energy Efficiency Methods. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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While each new generation of buildings harness technological advancements in building design and operation, existing generations of buildings must adapt through innovative building efficiency improvement methods. At an annual cost of $380 billion, even minimal incremental improvements can have significant impacts on national energy consumption (U.S. EIA 2017). This dissertation focuses on three communities, students, homeowners, and small commercial stakeholders, each instrumental in building energy curtailment.
The research performed in the student community evaluated: (1) the efficacy of flipped-classroom pedagogy to deliver residential energy content – students’ responses to questionnaires indicated increased confidence in knowledge – and (2) student outcomes in two approaches to integrating sustainable engineering to curricula – evaluation of student projects showed increased cognitive thinking in stand-alone sustainable engineering courses over senior design.
In the homeowner community, the propensity of energy audits to stimulate energy investments is ambiguous. The research implemented a survey to evaluate the efficacy of an innovative holistic energy assessment approach to induce energy efficiency improvements. Analysis of survey results indicated homeowners had a more positive perception of motivators for measures adopted versus not adopted (p-value<0.5).
The small commercial community consists of 94% of all commercial building stock with the majority of those buildings smaller than 465 m2 (5,001 ft2). A whole building energy disaggregation resource (BEAR) was developed and implemented in thirteen small commercial buildings with 28 tenants to measure accuracy of calculated energy estimations. BEAR was accurate to within 4.7% of electricity bills and 13.3% of natural gas bills in the examined tenants. Moreover, BEAR demonstrated robustness and scalability, design objectives intended for broader implementation across small commercial enterprises. Smart meter data revealed an average error in appliance-level estimation between BEAR and stochastic measurements of 66% for weekdays and 40% for weekends, with uncertainty in estimating appliance parameters driving error. However, improved power or operation data, separately, could reduce these errors by as much as half.
This research employs technical methods in complex environments to identify appropriate methods for disseminating educational materials across the three communities. Results present a larger case for the continued exploration of energy education, in particular through classrooms and energy conservation programs.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Ketchman, Kevinkjk72@pitt.edukjk72
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee MemberCasson,
Committee MemberKhanna,
Committee MemberParrish,
Committee MemberRiley,
Thesis AdvisorBilec,
Date: 25 January 2018
Date Type: Publication
Defense Date: 3 August 2017
Approval Date: 25 January 2018
Submission Date: 28 September 2017
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 260
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Civil and Environmental Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Learning Network, Commercial, Residential, Energy, Audit, BEAR
Date Deposited: 25 Jan 2018 13:11
Last Modified: 25 Jan 2019 06:15


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