Estimating Residential Wall Thermal Resistance From Exterior Thermal Imaging

Building energy audits are both expensive, on the order of $0.50(US)/sf [1], and there aren’t enough auditors to survey the entire building stock in the U.S. Needed are lower cost automated approaches for rapidly evaluating the energy effectiveness of buildings. A key element of such an approach would be automated measurements of envelope R-values. Proposed is the use of single point-in-time thermal images potentially obtainable from drive-by thermal imaging to infer wall and window R-values. A data mining based approach is proposed, which seeks to calibrate the measured exterior wall temperatures to known and measured R-values for a small subset of residences. In this approach, visual imagery is first used to determine the wall emissivity based on the color of the wall siding in order to yield an estimate of the wall temperature. A Random Forest model is developed using the training set comprised of the residences with known R-value. This model can then be used to estimate R- and C-values of other houses based upon their measured exterior temperatures. The results show that the proposed approach is capable of accurately estimating envelope thermal characteristics over a spectrum of envelope R-values and thermal capacitances present in residences nationally. The resulting error for the houses considered is maximally 12% using as few as nine training houses. The data mining approach has significantly greater accuracy than modeling-based approaches in the literature.

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

ASME 2017 11th International Conference on Energy Sustainability ◽

10.1115/es2017-3092 ◽

2017 ◽

Author(s):

Salahaldin F. Alshatshati ◽

Kevin P. Hallinan ◽

Abdulrahman Arlobaian ◽

Badr Altarhuni ◽

Adel Naji

Keyword(s):

Data Mining ◽

Thermal Imaging ◽

Energy Savings ◽

Background Radiation ◽

Cooling System ◽

Single Point ◽

Weather Conditions ◽

Thermal Images ◽

Data Mining Approach ◽

R Value

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.

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Estimating Building Envelope Thermal Characteristics From Single-Point-in-Time Thermal Images

Volume 1: Biofuels, Hydrogen, Syngas, and Alternate Fuels; CHP and Hybrid Power and Energy Systems; Concentrating Solar Power; Energy Storage; Environmental, Economic, and Policy Considerations of Advanced Energy Systems; Geothermal, Ocean, and Emerging Energy Technologies; Photovoltaics; Posters; Solar Chemistry; Sustainable Building Energy Systems; Sustainable Infrastructure and Transportation; Thermodynamic Analysis of Energy Systems; Wind Energy Systems and Technologies ◽

10.1115/es2016-59091 ◽

2016 ◽

Author(s):

Salahaldin Alshatshati ◽

Kevin P. Hallinan ◽

Robert J. Brecha

Keyword(s):

Surface Temperature ◽

Thermal Imaging ◽

Experimental Validation ◽

Single Point ◽

Thermal Characteristics ◽

Weather Conditions ◽

Building Envelope ◽

Thermal Images ◽

Thermal Capacitance ◽

R Value

Energy efficiency programs implemented by utilities in the U.S. have rendered savings costing on average $0.03/kWh [1]. This cost is still well below energy generation costs. However, as the lowest cost energy efficiency measures are adopted, the cost effectiveness of further investment declines. Thus, there is a need to develop large-scale and relatively inexpensive energy auditing techniques to more efficiently find opportunities for savings. Currently, on-site building energy audits process are expensive, in the range of US$0.12/sf – $0.53/sf, and there is an insufficient number of professionals to perform the audits. Here we present research that addresses at community-wide scales the characterization of building envelope thermal characteristics via drive-by and fly-over GPS linked thermal imaging. A central question drives this research: Can single point-in-time thermal images be used to infer R-values and thermal capacitances of walls and roofs? Previous efforts to use thermal images to estimate R-values have been limited to stable exterior weather conditions. The approach posed here is based upon the development of a dynamic model of a building envelope component with unknown R-value and thermal capacitance. The weather conditions prior to the thermal image are used as inputs to the model. The model is solved to determine the exterior surface temperature, ultimately predicted the temperature at the thermal measurement time. The model R-value and thermal capacitance are tuned to force the error between the predicted surface temperature and the measured surface temperature from thermal imaging to be near zero. The results show that this methodology is capable of accurately estimating envelope thermal characteristics over a realistic spectrum of envelope R-values and thermal capacitance present in buildings nationally. With an assumed thermal image accuracy, thermal characteristics are predicted with a maximum error of respectively 20% and 14% for high and low R-values when the standard deviation of outside temperature over the previous 48 hours is as much as 5°C. Experimental validation on a test facility with variable surface materials was attempted under variable weather conditions, e.g., where the outdoor air temperature experiences varying fluctuations prior to imaging. The experimental validation realized errors less than 20% in predicting the R-value even when the standard deviation of outdoor temperature over the 48 hours prior to a measurement was approximately 5°C.

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