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Understanding the Impact of Occupancy Trends in Sustainable Housing Designs

[+] Author Affiliations
Joseph Piacenza, Salvador Mayoral, Sean Lin, Lauren Won, Xava Grooms

California State University Fullerton, Fullerton, CA

Paper No. DETC2016-59588, pp. V004T05A020; 8 pages
doi:10.1115/DETC2016-59588
From:
  • ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 4: 21st Design for Manufacturing and the Life Cycle Conference; 10th International Conference on Micro- and Nanosystems
  • Charlotte, North Carolina, USA, August 21–24, 2016
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5014-5
  • Copyright © 2016 by ASME

abstract

As sustainable building mandates become more prevalent in new commercial buildings, it is a challenge to create a broad, one-size-fits-all certification process. While designers can estimate energy usage with computation tools such as model based design, anticipating the post occupancy usage is more difficult. Understanding energy usage trends is especially complicated in university student housing buildings, where occupancy varies significantly as a function of enrollment and course scheduling. This research explores the effect of student occupancy on both predicted and actual energy usage in a LEED (Leadership in Energy and Environmental Design) Platinum certified student housing complex. A case study is presented from the California State University Fullerton (CSUF) campus, and examines diversity factor, defined as a building’s instantaneous energy usage as a percentage of the maximum allowable usage during a period of time, trends throughout the academic year. The CSUF case diversity factor is compared to the diversity factor used in predictive models for obtaining LEED certification, and the mandates that govern the models (e.g., ASHRAE 90.1). The results of the analysis show the benefits of considering post occupancy usage in sustainable building designs, and recommendations are presented for creating unique and application based computational models, early in the design process. This research has broad applications, and can extend to sustainable building design in other organizations, whose operational schedule falls outside of current prediction methods for sustainability mandates.

Copyright © 2016 by ASME
Topics: Sustainability

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