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Thermography methodologies for detecting energy related building defects

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  • Fox, Matthew
  • Coley, David
  • Goodhew, Steve
  • de Wilde, Pieter

Abstract

Thermography is becoming more widely used amongst construction professionals for energy related defect detection in buildings. Until quite recently, most of the research and practical use of building thermography has centred on employing a building walk-around or walk-through methodology to detect sources of unacceptable energy use. However, thermographers are now creating new building thermography methodologies that seek to address some of the known limitations, such as camera spatial resolution, transient climatic conditions and differences in material properties. Often such limitations are misunderstood and sometimes ignored.

Suggested Citation

  • Fox, Matthew & Coley, David & Goodhew, Steve & de Wilde, Pieter, 2014. "Thermography methodologies for detecting energy related building defects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 296-310.
    • Handle: RePEc:eee:rensus:v:40:y:2014:i:c:p:296-310
    DOI: 10.1016/j.rser.2014.07.188
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    References listed on IDEAS

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    1. Fokaides, Paris A. & Kalogirou, Soteris A., 2011. "Application of infrared thermography for the determination of the overall heat transfer coefficient (U-Value) in building envelopes," Applied Energy, Elsevier, vol. 88(12), pages 4358-4365.
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    5. Aïssani, A. & Chateauneuf, A. & Fontaine, J.-P. & Audebert, Ph., 2016. "Quantification of workmanship insulation defects and their impact on the thermal performance of building facades," Applied Energy, Elsevier, vol. 165(C), pages 272-284.
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    7. Lucchi, Elena, 2018. "Applications of the infrared thermography in the energy audit of buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3077-3090.
    8. Sukjoon Oh & Suyeon Ham & Seongjin Lee, 2021. "Drone-Assisted Image Processing Scheme using Frame-Based Location Identification for Crack and Energy Loss Detection in Building Envelopes," Energies, MDPI, vol. 14(19), pages 1-19, October.
    9. Gertsvolf, David & Horvat, Miljana & Aslam, Danesh & Khademi, April & Berardi, Umberto, 2024. "A U-net convolutional neural network deep learning model application for identification of energy loss in infrared thermographic images," Applied Energy, Elsevier, vol. 360(C).

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