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An analysis of occupant normalized energy consumption as a benchmark for residential energy and emissions mitigation

Pease, Kyle Richard (2019) An analysis of occupant normalized energy consumption as a benchmark for residential energy and emissions mitigation. Masters thesis, Northern Arizona University.

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ABSTRACT AN ANALYSIS OF OCCUPANT NORMALIZED ENERGY CONSUMPTION AS A BENCHMARK FOR RESIDENTIAL ENERGY AND EMISSIONS MITIGATION KYLE PEASE Residential energy demand has a major influence on electricity generation and emissions, and therefore must be mitigated. Energy usage intensity (EUI), commonly measured in kBTU per square foot of building space, as a benchmark within the residential energy sector allows only narrow comparisons, penalizes high occupant density, and is not intuitive for broad audiences. While EUI was initially developed for energy analysis of commercial building designs, by precedent within building energy science, EUI is working its way into residential building energy applications and may cause problems in the future as the metric is built into new energy policies. Energy usage per person (EUP) could provide a solution to these problems and help progress residential energy efficiency efforts, however the differences between EUP and EUI in the context of residential building energy data are not well understood. This study evaluates EUI and EUP in terms of occupancy, floor space, and climate within the context of Residential Energy Consumption Survey (RECS) data as well as eQuest simulations. To generate a building energy modeling (BEM) dataset for comparison with RECS data, 660 households were simulated in eQuest for a range of occupancy from zero to five persons, with floor spaces ranging from 1000 to 5000 ft^2 across 21 locations in the United States including all Building America Climate Regions (BACR). Key predictor variables are distilled down to floor space per person (FSP) and degree days (DD). Multiple regression models predicting EUP and EUI for both RECS and eQuest simulation datasets are developed. EUP is found to have greater correlation than EUI in both datasets through power regression models with FSP and DD as covariates. Interpretation of a power regression model predicting EUP based on 2009 RECS data shows that with a 10% reduction in FSP there is estimated to be a 6.5% reduction in EUP. Combining hourly energy demand in eQuest simulations with New York independent system operator (NYISO) and California independent system operator (CAISO) hourly generation mixes, as well as eGrid emissions factors, annual emissions per person estimations are made for a range of FSP. Occupant normalized missions are observed to be proportional to FSP to varying degrees depending on climate, generation mix, and appliance fuel type, with eQuest simulations of all electric appliances in NYISO showing over double the emissions per FSP than CAISO. Considering EUP and focusing on reductions in FSP may be an effective strategy for residential building energy policy. In the past 120 years the US population has increased nearly fivefold, while the number of homes has increased by a factor of ten according to research by Moura et al. According to the 2015 RECS there are 237 billion square feet of residential housing with single family housing averaging at 918 sf per household member. For mitigation of energy and emissions within the residential sector, an information-based policy is recommended as a low cost and quickly implemented first step. Occupant normalizations may help residential energy consumers, analysts, and policymakers alike to better understand energy performance and resulting emissions.

Item Type: Thesis (Masters)
Publisher’s Statement: © Copyright is held by the author. Digital access to this material is made possible by the Cline Library, Northern Arizona University. Further transmission, reproduction or presentation of protected items is prohibited except with permission of the author.
Keywords: Buildings; Energy; EUI; EUP; Policy; Residential
Subjects: T Technology > TJ Mechanical engineering and machinery
NAU Depositing Author Academic Status: Student
Department/Unit: Graduate College > Theses and Dissertations
College of Engineering, Informatics, and Applied Sciences > Mechanical Engineering
Date Deposited: 28 Jan 2022 23:30
Last Modified: 28 Jan 2022 23:30
URI: https://openknowledge.nau.edu/id/eprint/5598

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