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Data Mining Approach for Estimating Residential Attic Thermal Resistance From Aerial Thermal Imagery, Utility Data, and Housing Data

[+] Author Affiliations
Salahaldin F. Alshatshati, Kevin P. Hallinan, Abdulrahman Arlobaian, Badr Altarhuni, Adel Naji

University of Dayton, Dayton, OH

Paper No. ES2017-3092, pp. V001T09A001; 9 pages
  • ASME 2017 11th International Conference on Energy Sustainability collocated with the ASME 2017 Power Conference Joint With ICOPE-17, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum
  • ASME 2017 11th International Conference on Energy Sustainability
  • Charlotte, North Carolina, USA, June 26–30, 2017
  • Conference Sponsors: Advanced Energy Systems Division, Solar Energy Division
  • ISBN: 978-0-7918-5759-5
  • Copyright © 2017 by ASME


Conventional residential building energy auditing needed to identify opportunities for energy savings is expensive and time consuming. On-site energy audits require quantification of envelope R-values, air and duct leakage, and heating and cooling system efficiencies. There is a need to advance lower cost automated approaches, which could include aerial and drive-by thermal imaging at-scale in an effort to measure the building R-value. However, single-point in time thermal images are generally qualitative, subject to errors stemming from building dynamics, background radiation, wind speed variation, night sky thermal radiation, and error in extracting temperature estimates from thermal images from surfaces with generally unknown emissivity. This work proposes two alternative approaches for estimating roof R-values from thermal imaging, one a physics based approach and the other a data-mining based approach. Both approaches employ aerial visual imagery to estimate the roof emissivity based on the color and type of roofing material, from which the temperature of the envelope can be estimated. The physics-based approach employs a dynamic energy model of the envelope with unknown R-value and thermal capacitance. These are tuned in order to predict the measured surface temperature at the time of the imaging, given the transient weather conditions prior to the imaging. The data-mining approach integrates the inferred temperature measurement, historical utility data, and easily accessible or potentially easily accessible housing data. A data mining regression model, trained from this data using residences with known R-values, is used to predict the roof R-value in the unknown houses. The data mining approach was shown to be a far superior approach, demonstrating an ability to estimate attic/roof R-value with an r-squared value of greater than 0.88 using as few as nine training houses. The implication of this research is significant, offering the possibility of auditing residences remotely at-scale via aerial and drive-by thermal imaging coupled with utility analysis.

Copyright © 2017 by ASME



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