Using inverse regression models to create gray box models for industrial facilities

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dc.contributor MacPhee, David W.
dc.contributor Midkiff, K. Clark
dc.contributor Barrett, Bruce E.
dc.contributor.advisor Woodbury, Keith A.
dc.contributor.advisor O'Neill, Zheng
dc.contributor.author Carpenter, Joseph
dc.date.accessioned 2019-02-12T14:31:15Z
dc.date.available 2019-02-12T14:31:15Z
dc.date.issued 2018
dc.identifier.other u0015_0000001_0003179
dc.identifier.other Carpenter_alatus_0004D_13700
dc.identifier.uri http://ir.ua.edu/handle/123456789/5362
dc.description Electronic Thesis or Dissertation
dc.description.abstract Industrial facilities account for approximately one third of energy usage in the world, and effective energy assessments of these facilities require a reliable baseline energy model. Commercial and residential buildings are baselined with both simple change-point models and models that are more complex, such as Gaussian process and artificial neural networks, and these models are developed and tested with dense high-frequency data. However, industrial facilities are only baselined using change-point models, and data for the models are typically restricted to monthly utility bills and, therefore, generally sparse data. This investigation first compares the effectiveness of change-point models with that of Gaussian process models for baselining industrial facilities using only monthly utility billing information as data. Two case studies are presented to predict electricity usage and two case studies are presented to predict natural gas usage. Both change-point and Gaussian process models provided similar results, and both models meet the recommended NMBE and CV-RMSE from ASHRAE Guideline 14. Due to the simplicity and straight-forward equations of change-point models, they are better for regression analysis unless uncertainty is required. This study then investigates using three parameter cooling change-point regression models to determine the physical parameters, specifically overall heat transfer coefficient, surface area, and outdoor air mass flow rate of an industrial building through a simulation-based emulator. A simplified industrial building similar in size, energy usage, and physical parameters as a typical industrial facility was simulated in fifteen of the climate zones defined by ASHRAE using a whole building simulation program (i.e., EnergyPlus) to produce hourly data to illustrate and demonstrate the proposed approach. The change-point models showed poor results for finding the physical parameters using ambient air temperature as the independent variable. When using sol-air temperatures as the independent variable the change-point models were able to predict a lumped capacity of building envelope and outdoor air infiltration/ventilation within +/– 25 % error of actual (UA+ṀCP)/COP for most of the climate zones in the U.S.
dc.format.extent 177 p.
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.ispartof The University of Alabama Libraries Digital Collections
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated.
dc.subject.other Mechanical engineering
dc.title Using inverse regression models to create gray box models for industrial facilities
dc.type thesis
dc.type text
etdms.degree.department University of Alabama. Department of Mechanical Engineering
etdms.degree.discipline Mechanical Engineering
etdms.degree.grantor The University of Alabama
etdms.degree.level doctoral
etdms.degree.name Ph.D.


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