Detecting Common Insulation Problems in Built Environments using Thermal Images

Proper thermal insulation yields optimum energy expenses in buildings by maintaining necessary heat gain or loss through the built envelope. However, improper thermal insulation causes significant energy wastage along with infusing various damages on indoor and outdoor walls of the buildings, for example, damp areas, black stains, cracks, paint bubbles etc. Therefore, it is important to inspect the temperature variations in different areas of the built environments in regular basis. We propose a method for identifying temperature variance in building thermal images based on Symbolic Aggregated Approximation (SAX). Our process helps detect the temperature variation over different image segments and infers the fault prone segments of leakages. We have collected about 50 thermal images associated with different types of wall specific insulation problems in indoor built environment and were able to identify the affected area with approximately 75% accuracy using our proposed method based on temperature variation detection approach.

[1]  Umberto Berardi.  The impact of temperature dependency of the building insulation thermal conductivity in the Canadian climate , 2017 .

[2]  Max H. Sherman.  Superposition in Infiltration Modeling , 1992 .

[3]  Tetsuhisa Miwa,et al.  Relations between Surface Temperature and Air Temperature on a Local Scale during Winter Nights , 2000 .

[4]  Nirmalya Roy,et al.  Longitudinal energy waste detection with visualization , 2017, BuildSys@SenSys.

[5]  Ming-Syan Chen,et al.  HeatProbe: a thermal-based power meter for accounting disaggregated electricity usage , 2011, UbiComp '11.

[6]  David Coley,et al.  Time-lapse thermography for building defect detection , 2015 .

[7]  Matthew Louis Mauriello,et al.  A temporal thermography system for supporting longitudinal building energy audits , 2017, UbiComp/ISWC Adjunct.

[8]  Iakovos Michailidis,et al.  Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage , 2016 .

[9]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[10]  Ricardo M.S.F. Almeida,et al.  An infrared thermography passive approach to assess the effect of leakage points in buildings , 2017 .

[11]  Qingyan Chen,et al.  A novel method for measuring air infiltration rate in buildings , 2018, Energy and Buildings.

[12]  Matthew Louis Mauriello,et al.  Understanding the Role of Thermography in Energy Auditing: Current Practices and the Potential for Automated Solutions , 2015, CHI.

[13]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[14]  Matthew Louis Mauriello.  Scalable methods and tools to support thermographic data collection and analysis for energy audits , 2017, UbiComp/ISWC Adjunct.

[15]  Li Wei,et al.  Efficiently finding unusual shapes in large image databases , 2008, Data Mining and Knowledge Discovery.

[16]  Henrik Madsen,et al.  Identifying suitable models for the heat dynamics of buildings , 2011 .

[17]  Matthew Louis Mauriello,et al.  Towards automated thermal profiling of buildings at scale using unmanned aerial vehicles and 3D-reconstruction , 2014, UbiComp Adjunct.

[18]  D. Dockery,et al.  The relationship between indoor and outdoor temperature, apparent temperature, relative humidity, and absolute humidity. , 2013, Indoor air.

[19]  Hom B. Rijal,et al.  Development of a window opening algorithm based on adaptive thermal comfort to predict occupant behavior in Japanese dwellings , 2018, Japan Architectural Review.