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Revolutionising building inspection techniques to meet large-scale energy demands: A review of the state-of-the-art

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  • Shariq, M. Hasan
  • Hughes, Ben Richard

Abstract

The building sector is responsible for 40% of the overall energy consumption in the EU. Building defects, such as heat losses, moisture, and air leakages, inevitably causes inefficient space heating or cooling, which accounts considerably towards this high energy consumption and associated greenhouse gas emissions. In order to meet the EU's 2050 carbon reduction targets, building inspection techniques need to be revolutionised. Current methods rely on terrestrial or hand-held infrared thermography (IRT) to detect building defects. However, for a large-scale inspection, these methods are generally labour-intensive, time-consuming, costly and often inefficient. The aim of this paper is to highlight the possibility of integrating various state-of-the-art technologies and computational methods with IRT including drones, photogrammetry and AI. This paper presents a comprehensive review of relevant scientific papers and recent developments in such technologies that can retrofit the existing manually intensive methods. Among the findings of this research, feasibility of monocular thermographic photogrammetry integrated on a drone (quadcopter) promises a time-efficient, cost-effective and near-autonomous solution to large-scale building inspections.

Suggested Citation

  • Shariq, M. Hasan & Hughes, Ben Richard, 2020. "Revolutionising building inspection techniques to meet large-scale energy demands: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
  • Handle: RePEc:eee:rensus:v:130:y:2020:i:c:s1364032120302707
    DOI: 10.1016/j.rser.2020.109979
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    References listed on IDEAS

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    1. Gallardo-Saavedra, Sara & Hernández-Callejo, Luis & Duque-Perez, Oscar, 2018. "Technological review of the instrumentation used in aerial thermographic inspection of photovoltaic plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 566-579.
    2. Guo, Yabin & Tan, Zehan & Chen, Huanxin & Li, Guannan & Wang, Jiangyu & Huang, Ronggeng & Liu, Jiangyan & Ahmad, Tanveer, 2018. "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving," Applied Energy, Elsevier, vol. 225(C), pages 732-745.
    3. 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.
    4. 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.
    5. Li, Y. & Kubicki, S. & Guerriero, A. & Rezgui, Y., 2019. "Review of building energy performance certification schemes towards future improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    6. Garwood, Tom Lloyd & Hughes, Ben Richard & O'Connor, Dominic & Calautit, John K. & Oates, Michael R. & Hodgson, Thomas, 2018. "A framework for producing gbXML building geometry from Point Clouds for accurate and efficient Building Energy Modelling," Applied Energy, Elsevier, vol. 224(C), pages 527-537.
    7. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
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    Cited by:

    1. Lup Wai Chew & Xian-Xiang Li & Michael Y. L. Chew, 2023. "Climate Change Projection and Its Impacts on Building Façades in Singapore," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    2. Sandra Melo & Flavia Silva & Mohammad Abbasi & Parisa Ahani & Joaquim Macedo, 2023. "Public Acceptance of the Use of Drones in City Logistics: A Citizen-Centric Perspective," Sustainability, MDPI, vol. 15(3), pages 1-9, February.
    3. Wang, Junqi & Jiang, Lanfei & Yu, Hanhui & Feng, Zhuangbo & Castaño-Rosa, Raúl & Cao, Shi-jie, 2024. "Computer vision to advance the sensing and control of built environment towards occupant-centric sustainable development: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    4. Yu Hou & Rebekka Volk & Lucio Soibelman, 2021. "A Novel Building Temperature Simulation Approach Driven by Expanding Semantic Segmentation Training Datasets with Synthetic Aerial Thermal Images," Energies, MDPI, vol. 14(2), pages 1-16, January.

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