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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 U-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 U-value. However, the thermal imaging approaches implemented to date, all based upon thermal-physical models of the envelopes, to estimate the U-values of walls require additional measurements and analysis prohibiting low-cost, at-scale implementation. This research focuses on interpreting aerial thermal images to estimate the U-value of roofs. A thermal-physics model of a ceiling is developed to show the difficulty in using the same approach used by others for walls, as new parameter estimates and thus more measurements would be required. A data-based methodology instead is posed. This approach integrates the inferred roof temperature measurement, historical utility data, and easily accessible or potentially easily accessible housing data. A Random Forest model is developed from a training subset of residences for which the ceiling U-values are known. This model is used to predict the roof U-values in a validation set of houses with unknown U-value. Demonstrated is an ability to estimate the attic/roof U-value with an R-squared value in the range of 0.96 using as few as 24 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

Publication Date

4-18-2018

Project Designation

Graduate Research

Primary Advisor

Kevin P Hallinan

Primary Advisor's Department

Mechanical and Aerospace Engineering

Keywords

Stander Symposium poster

Comments

Presenter: Salahaldin Faraj Alshatshati

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

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