IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i20p6488-d653038.html
   My bibliography  Save this article

Roof Color-Based Warm Roof Evaluation in Cold Regions Using a UAV Mounted Thermal Infrared Imaging Camera

Author

Listed:
  • Kirim Lee

    (Department of Spatial Information, Kyungpook National University, Daegu 41566, Korea)

  • Jinhwan Park

    (Korea Land and Geospatial Informatix Corporation, Andong 36691, Korea)

  • Sejung Jung

    (Department of Convergence and Fusion System Engineering, Kyungpook National University, Sangju 37224, Korea)

  • Wonhee Lee

    (Department of Convergence and Fusion System Engineering, Kyungpook National University, Sangju 37224, Korea)

Abstract

Existing studies on reducing urban heat island phenomenon and building temperature have been actively conducted; however, studies on investigating the warm roof phenomenon to in-crease the temperature of buildings are insufficient. A cool roof is required in a high-temperature region, while a warm roof is needed in a low-temperature or cold region. Therefore, a warm roof evaluation was conducted in this study using the roof color (black, blue, green, gray, and white), which is relatively easier to install and maintain compared to conventional insulation materials and double walls. A remote sensing method via an unmanned aerial vehicle (UAV)-mounted thermal infrared (TIR) camera was employed. For warm roof evaluation, the accuracy of the TIR camera was verified by comparing it with a laser thermometer, and the correlation between the surface temperature and the room temperature was also confirmed using Pearson correlation. The results showed significant surface temperature differences ranging from 8 °C to 28 °C between the black-colored roof and the other colored roofs and indoor temperature differences from 1 °C to 7 °C. Through this study, it was possible to know the most effective color for a warm roof according to the color differences. This study gave us an idea of which color would work best for a warm roof, as well as the temperature differences from other colors. We believe that the results of this study will be helpful in heating load research, providing an objective basis for determining whether a warm roof is applied.

Suggested Citation

  • Kirim Lee & Jinhwan Park & Sejung Jung & Wonhee Lee, 2021. "Roof Color-Based Warm Roof Evaluation in Cold Regions Using a UAV Mounted Thermal Infrared Imaging Camera," Energies, MDPI, vol. 14(20), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6488-:d:653038
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/20/6488/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/20/6488/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    2. Sholahudin, S. & Han, Hwataik, 2016. "Simplified dynamic neural network model to predict heating load of a building using Taguchi method," Energy, Elsevier, vol. 115(P3), pages 1672-1678.
    3. Kirim Lee & Jihoon Seong & Youkyung Han & Won Hee Lee, 2020. "Evaluation of Applicability of Various Color Space Techniques of UAV Images for Evaluating Cool Roof Performance," Energies, MDPI, vol. 13(16), pages 1-12, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    2. Gao, Zhikun & Yang, Siyuan & Yu, Junqi & Zhao, Anjun, 2024. "Hybrid forecasting model of building cooling load based on combined neural network," Energy, Elsevier, vol. 297(C).
    3. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    4. Xiaoyu Gao & Chengying Qi & Guixiang Xue & Jiancai Song & Yahui Zhang & Shi-ang Yu, 2020. "Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR," Energies, MDPI, vol. 13(22), pages 1-19, November.
    5. Hyo-Jun Kim & Young-Hum Cho, 2021. "Optimal Control Method of Variable Air Volume Terminal Unit System," Energies, MDPI, vol. 14(22), pages 1-15, November.
    6. Roozbeh Vaziri & Akeem Adeyemi Oladipo & Mohsen Sharifpur & Rani Taher & Mohammad Hossein Ahmadi & Alibek Issakhov, 2021. "Efficiency Enhancement in Double-Pass Perforated Glazed Solar Air Heaters with Porous Beds: Taguchi-Artificial Neural Network Optimization and Cost–Benefit Analysis," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
    7. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
    8. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    9. Thomas Wu & Bo Wang & Dongdong Zhang & Ziwei Zhao & Hongyu Zhu, 2023. "Benchmarking Evaluation of Building Energy Consumption Based on Data Mining," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    10. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    11. Zhiyong Li & Shiping Pu & Yougen Chen & Renyong Wei, 2020. "An Integration Optimization Strategy of Line Voltage Cascaded Quasi-Z-Source Inverter Parameters Based on GRA-FA," Energies, MDPI, vol. 13(17), pages 1-24, August.
    12. Wenxiao Chu & Francesco Calise & Neven Duić & Poul Alberg Østergaard & Maria Vicidomini & Qiuwang Wang, 2020. "Recent Advances in Technology, Strategy and Application of Sustainable Energy Systems," Energies, MDPI, vol. 13(19), pages 1-29, October.
    13. Beccali, M. & Bonomolo, M. & Ciulla, G. & Lo Brano, V., 2018. "Assessment of indoor illuminance and study on best photosensors' position for design and commissioning of Daylight Linked Control systems. A new method based on artificial neural networks," Energy, Elsevier, vol. 154(C), pages 466-476.
    14. Hu, Jingfan & Zheng, Wandong & Zhang, Sirui & Li, Hao & Liu, Zijian & Zhang, Guo & Yang, Xu, 2021. "Thermal load prediction and operation optimization of office building with a zone-level artificial neural network and rule-based control," Applied Energy, Elsevier, vol. 300(C).
    15. Chanuk Lee & Dong Eun Jung & Donghoon Lee & Kee Han Kim & Sung Lok Do, 2021. "Prediction Performance Analysis of Artificial Neural Network Model by Input Variable Combination for Residential Heating Loads," Energies, MDPI, vol. 14(3), pages 1-19, February.
    16. Zare, Shahryar & Tavakolpour-saleh, A.R. & Aghahosseini, A. & Sangdani, M.H. & Mirshekari, Reza, 2021. "Design and optimization of Stirling engines using soft computing methods: A review," Applied Energy, Elsevier, vol. 283(C).
    17. Tomasz Szul & Sylwester Tabor & Krzysztof Pancerz, 2021. "Application of the BORUTA Algorithm to Input Data Selection for a Model Based on Rough Set Theory (RST) to Prediction Energy Consumption for Building Heating," Energies, MDPI, vol. 14(10), pages 1-13, May.
    18. Gu, Jihao & Wang, Jin & Qi, Chengying & Min, Chunhua & Sundén, Bengt, 2018. "Medium-term heat load prediction for an existing residential building based on a wireless on-off control system," Energy, Elsevier, vol. 152(C), pages 709-718.
    19. Xue, Guixiang & Qi, Chengying & Li, Han & Kong, Xiangfei & Song, Jiancai, 2020. "Heating load prediction based on attention long short term memory: A case study of Xingtai," Energy, Elsevier, vol. 203(C).
    20. Wang, Chuan'an & Pouramini, Somayeh, 2024. "Multi-objective modified satin Bowerbird optimization algorithm used for simulation-based energy consumption optimization of yearly energy demand of lighting and cooling in a test case room," Energy, Elsevier, vol. 292(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6488-:d:653038. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.