ZHANG Lingling, ZHANG Jiran, XU Ao, REN Panpan, DING Libin. Energy Consumption Analysis of Building Window Defects Based on Infrared Image Processing[J]. Infrared Technology , 2023, 45(9): 996-1004.
Citation: ZHANG Lingling, ZHANG Jiran, XU Ao, REN Panpan, DING Libin. Energy Consumption Analysis of Building Window Defects Based on Infrared Image Processing[J]. Infrared Technology , 2023, 45(9): 996-1004.

Energy Consumption Analysis of Building Window Defects Based on Infrared Image Processing

More Information
  • Received Date: June 10, 2022
  • Revised Date: July 20, 2022
  • The differential pressure method, which combines infrared thermal imaging and image processing technologies, is used to detect air infiltration of building exterior windows. Infrared images of the exterior windows of the building were collected using an infrared thermal imager and then processed using infrared image processing technology. Exterior window defects were detected from abnormal areas in the infrared images, and the area of the defects was calculated to establish an infrared detection model for exterior window defects. Based on the indoor and outdoor temperature difference, defect area of the outer window, and air infiltration amount measured in the experiment, a calculation model was established for the amount of air infiltration for the building's outer window. The model was combined with the infrared detection model for building window defects, to quantitatively analyze the energy consumption caused by the defects. The results show that the maintenance of exterior window defects can reduce energy consumption of the exterior window and improve energy savings. For every 1 cm2 reduction in the air infiltration area of exterior windows, 66146 kJ of energy can be saved annually. For each level of airtightness improvement of exterior windows, 110012 kJ of energy per unit area of exterior windows can be saved annually,
  • [1]
    中国建筑节能协会. 中国建筑能耗研究报告2020[J]. 建筑节能, 2021, 49(2): 1-6. https://www.cnki.com.cn/Article/CJFDTOTAL-FCYY202102001.htm

    China Association of Building Energy Efficiency. China building energy consumption annual report 2020[J]. Building Energy Efficiency, 2021, 49(2): 1-6. https://www.cnki.com.cn/Article/CJFDTOTAL-FCYY202102001.htm
    [2]
    GUO S, YAN D, HU S, et al. Modelling building energy consumption in China under different future scenarios[J]. Energy, 2021, 214: 119063. DOI: 10.1016/j.energy.2020.119063
    [3]
    郭兴忠, 杨闯, 张超, 等. 节能门窗热工性能对建筑能耗影响的模拟研究[J]. 建筑材料学报, 2014, 17(2): 261-265, 297. https://www.cnki.com.cn/Article/CJFDTOTAL-JZCX201402015.htm

    GUO Xingzhong, YANG Chuang, ZHANG Chao, et al. Simulation on thermal performance of energy-saving windows and doors and its influence on building energy consumption[J]. Journal of Building Materials, 2014, 17(2): 261-265, 297. https://www.cnki.com.cn/Article/CJFDTOTAL-JZCX201402015.htm
    [4]
    赖惠玲. 绿色节能塑料门窗的发展[J]. 塑料工业, 2018, 46(10): 22-25. https://www.cnki.com.cn/Article/CJFDTOTAL-SLGY201810009.htm

    LAI Huiling. Development of green and energy saving plastic doors and windows[J]. China Plastics Industry, 2018, 46(10): 22-25. https://www.cnki.com.cn/Article/CJFDTOTAL-SLGY201810009.htm
    [5]
    李伟, 东岩, 高强, 等. 我国建筑气密性研究现状与分析[J]. 消防科学与技术, 2019, 38(8): 1097-1098, 1101. https://www.cnki.com.cn/Article/CJFDTOTAL-XFKJ201908015.htm

    LI Wei, DONG Yan, GAO Qiang, et al. Current situation and analysis of research on building airtightness in China[J]. Fire Science and Technology, 2019, 38(8): 1097-1098, 1101. https://www.cnki.com.cn/Article/CJFDTOTAL-XFKJ201908015.htm
    [6]
    董子忠, 许永光, 温永玲, 等. 炎热地区夏季窗户的热过程研究[J]. 暖通空调, 2003(3): 93-96. https://www.cnki.com.cn/Article/CJFDTOTAL-NTKT200303027.htm

    DONG Zizhong, XU Yongguang, WEN Yongling, et al. Window thermal process in hot climate[J]. Heating Ventilating & Air Conditioning, 2003(3): 93-96. https://www.cnki.com.cn/Article/CJFDTOTAL-NTKT200303027.htm
    [7]
    李扬捷, 徐伟, 董宏. 压差法评价建筑常压下气密性能的实验研究[J]. 建筑科学, 2021, 37(6): 206-210. https://www.cnki.com.cn/Article/CJFDTOTAL-JZKX202106028.htm

    LI Yangjie, XU Wei, DONG Hong. Experimental study on the evaluation of building airtightness under normal pressure by fan pressurization method[J]. Building Science, 2021, 37(6): 206-210. https://www.cnki.com.cn/Article/CJFDTOTAL-JZKX202106028.htm
    [8]
    JI Y, LIN D. Airtightness field tests of residential buildings in Dalian, China[J]. Building and Environment, 2017, 119: 20-30. DOI: 10.1016/j.buildenv.2017.03.043
    [9]
    Barreira E, Almeida R M S F, Moreira M. An infrared thermography passive approach to assess the effect of leakage points in buildings[J]. Energy & Buildings, 2017, 140: 224-235.
    [10]
    Nathan V D B, Janssens A. Airtightness and water tightness of window frames: comparison of performance and requirements[J]. Building & Environment, 2016, 110: 129-139.
    [11]
    TU B, YANG X, ZHOU C, et al. Hyperspectral anomaly detection using dual window density[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(12): 8503-8517. DOI: 10.1109/TGRS.2020.2988385
    [12]
    Mohan A, Poobal S. Crack detection using image processing: a critical review and analysis[J]. Alexandria Engineering Journal, 2018, 57(2): 787-798. DOI: 10.1016/j.aej.2017.01.020
    [13]
    HAN Qinzhe, YIN Qian, ZHENG Xin, et al. Remote sensing image building detection method based on Mask R-CNN[J]. Complex & Intelligent Systems, 2021, 8(3): 1847-1855.
    [14]
    Riehm M, Gustavsson T, Bogren J, et al. Ice formation detection on road surfaces using infrared thermometry[J]. Cold Regions Science & Technology, 2012, 83: 71-76.
    [15]
    XU C H, XIE J, CHEN G M, et al. An infrared thermal image processing framework based on superpixel algorithm to detect cracks on metal surface[J]. Infrared Physics & Technology, 2014, 67(1): 266-272.
    [16]
    曹依蕾, 刘寅, 高龙, 等. 红外热像仪在被动式超低能耗建筑性能检测中的应用[J]. 激光与红外, 2020, 50(2): 174-178. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW202002009.htm

    CAO Yilei, LIU Yin, GAO Long, et al. Application of infrared thermal imager in the performance detection of passive ultra-low energy buildings[J]. Laser & Infrared, 2020, 50(2): 174-178. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW202002009.htm
    [17]
    Sharma S, Varma T. Graph signal processing based underwater image enhancement techniques[J]. Engineering Science and Technology, 2022, 32: 101059.
    [18]
    Ed Is E, Flores Colen I, Brito J D. Passive thermographic inspection of adhered ceramic claddings: limitation and conditioning factors[J]. Journal of Performance of Constructed Facilities, 2012, 27(6): 737-747.
    [19]
    Doshvarpassand S, WANG X. An automated pipeline for dynamic detection of sub-surface metal loss defects across cold thermography images[J]. Sensors, 2021, 21(14): 4811.
    [20]
    Gehri N, J Mata-Falcón, Kaufmann W. Automated crack detection and measurement based on digital image correlation[J]. Construction and Building Materials, 2020, 256: 119383.
    [21]
    陈劲, 陈晓东, 赵辉, 等. 基于红外热成像法和超声波法的钢管混凝土无损检测技术的试验研究与应用[J]. 建筑结构学报, 2021, 42(S2): 444-453. https://www.cnki.com.cn/Article/CJFDTOTAL-JZJB2021S2052.htm

    CHEN Jin, CHEN Xiaodong, ZHAO Hui, et al. Experimental research and application of non-destructive detecting techniques for concrete-filled steel tubes based on infrared thermal imaging and ultrasonic method[J]. Journal of Building Structures, 2021, 42(S2): 444-453. https://www.cnki.com.cn/Article/CJFDTOTAL-JZJB2021S2052.htm
    [22]
    范鹏, 冯万兴, 周自强, 等. 深度学习在绝缘子红外图像异常诊断的应用[J]. 红外技术, 2021, 43(1): 51-55. http://hwjs.nvir.cn/article/id/fe4d4626-dfdf-4db1-a2e0-7e13c8861258

    FAN Peng, FENG Wanxing, ZHOU Ziqiang, et al. Application of deep learning in abnormal insulator infrared image diagnosis[J]. Infrared Technology, 2021, 43(1): 51-55. http://hwjs.nvir.cn/article/id/fe4d4626-dfdf-4db1-a2e0-7e13c8861258
    [23]
    张玲玲, 许廒, 张继冉, 等. 基于红外图像处理技术的建筑外窗缺陷面积计算研究[J]. 红外技术, 2022, 44(12): 1358-1366. http://hwjs.nvir.cn/article/id/f13166bf-7f5c-4baa-b984-061eea11215c

    ZHANG Lingling, XU Ao, ZHANG Jiran, et al. Research on calculation of defect area of building exterior windows based on infrared image processing technology[J]. Infrared Technology, 2022, 44(12): 1358-1366. http://hwjs.nvir.cn/article/id/f13166bf-7f5c-4baa-b984-061eea11215c
    [24]
    孙晓斐, 祁卓, 孙王倩, 等. 基于特征融合的红外图像增强算法[J]. 光学技术, 2022, 48(2): 250-256. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS202202021.htm

    SUN Xiaofei, QI Zhuo, SUN Wangqian, et al. Infrared image enhancement algorithm based on feature fusion[J]. Optical Technique, 2022, 48(2): 250-256. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS202202021.htm
    [25]
    GAO C, MENG D, YANG Y, et al. Infrared patch-image model for small target detection in a single image[J]. IEEE Transactions on Image Processing, 2013, 22(12): 4996-5009.
    [26]
    张玲玲, 任攀攀, 许廒, 等. 基于红外图像处理的建筑外窗气密性能现场检测[J]. 红外技术, 2023, 45(4): 410-416. http://hwjs.nvir.cn/article/id/92e6c60a-48fb-43a9-adcd-72281c8943ab

    ZHANG Lingling, REN Panpan, XU Ao, et al. On-site detection of airtightness of building windows based on infrared image processing[J]. Infrared Technology, 2023, 45(4): 410-416. http://hwjs.nvir.cn/article/id/92e6c60a-48fb-43a9-adcd-72281c8943ab
    [27]
    Val A, Padilla-Marcos M N, Meiss A, et al. Air infiltration monitoring using thermography and neural networks[J]. Energy and Buildings, 2019, 191: 187-199.
    [28]
    López-Pérez L A, Flores-Prieto J J, C Ríos-Rojas. Comfort temperature prediction according to an adaptive approach for educational buildings in tropical climate using artificial neural networks[J]. Energy and Buildings, 2021, 251: 111328.
    [29]
    XIE L, PI D, ZHANG X, et al. Graph neural network approach for anomaly detection[J]. Measurement, 2021, 180(1): 109546.
    [30]
    中国建筑科学研究院. 居住建筑节能检测标准: JGJ/T 132-2009 [S]. 中华人民共和国住房和城乡建设部.

    China Academy of Building Research. Energy efficiency test standard for residential buildings: JGJ/T 132-2009 [S]. Ministry of Housing and Urban-Rural Development of the People's Republic of China.
    [31]
    中国建筑科学研究院. 建筑节能气象参数标准: JGJT346-2014 [S]. 中华人民共和国住房和城乡建设部.

    China Academy of Building Research. Standard for weather data of building energy efficiency: JGJT346-2014[S]. Ministry of Housing and Urban-Rural Development of the People's Republic of China.
    [32]
    中国建筑科学研究院. 民用建筑供暖通风与空气调节设计规范: GB50736-2012[S]. 中华人民共和国住房和城乡建设部.

    China Academy of Building Research. Design code for heating ventilation and air conditioning of civil buildings: GB50736-2012[S]. Ministry of Housing and Urban-Rural Development of the People's Republic of China.
    [33]
    中国建筑科学研究院, 中国建筑标准设计研究院. 建筑碳排放计算标准: GB/T51366-2019[S]. 中华人民共和国住房和城乡建设部, 国家市场监督管理总局.

    China Academy of Building Research, China Institute of Building Standard Design & Research. Standard for building carbon emission calculation: GB/T51366-2019 [S]. Ministry of Housing and Urban-Rural Development of the People's Republic of China, State Administration for Market Regulation.
  • Related Articles

    [1]ZHANG Xuesong, WU Nan, WANG Feng, CHU Sisi, LI Dongze. Analysis of Detection Ability of Missile-Borne Infrared Detector to Interceptor[J]. Infrared Technology , 2024, 46(5): 599-607.
    [2]LEI Yongchang, LI Jianlin, DONG Wei, ZHOU Jiading, HOU Likun, QIAN Kunlun. Redundant Object Damage and Prevention Method for Infrared Detectors[J]. Infrared Technology , 2023, 45(7): 790-797.
    [3]DENG Wei, SUN Hongsheng, ZHU Yingfeng, XU Dongmei, LI Ran, HUANG Yibin. Development Status of the Flexible Thermal Link Coupling Between Cryocooler and Long Linear Infrared Detector[J]. Infrared Technology , 2020, 42(1): 10-18.
    [4]CHI Guochun, SUN Hao, WANG Liang, LIU Xiangde, RAO Qichao. The Analysis of Cooling Parameters of Infrared Detector Assembly[J]. Infrared Technology , 2019, 41(7): 683-688.
    [5]FENG Hongwei, LIU Yuanyuan, XIE Linbo. Algorithm Design and Implementation for Dual-band Infrared Combustible Gas Detector[J]. Infrared Technology , 2019, 41(3): 227-231.
    [6]YANG Xiaole, SHI Manli, LING Long. Design of the Key Driving and Signal Processing Circuit for Cooled Infrared Detector[J]. Infrared Technology , 2016, 38(7): 556-560.
    [7]LI Jia-kun, JIN Wei-qi, WANG Xia, JIN Ming-lei, DUN Xiong, CHEN Ji. Review of Gas Leak Infrared Imaging Detection Technology[J]. Infrared Technology , 2014, (7): 513-520.
    [8]CHU Jun-hao, MENG Xian-jian. A Ferroelectric Polymer of Polyvinylidene Fluoride for the Application of Infrared Detection[J]. Infrared Technology , 2014, (1): 1-9.
    [9]Fundamentals of p-on-n HgCdTe Infrared Detectors and Their Detectivity Calculations[J]. Infrared Technology , 2013, (5): 249-258.
    [10]Relatively Spectrum Response Detection of Infrared Detector in 1~3μm[J]. Infrared Technology , 2004, 26(2): 64-67. DOI: 10.3969/j.issn.1001-8891.2004.02.017

Catalog

    Article views (197) PDF downloads (37) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return