IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v193y2020ics0360544219324132.html
   My bibliography  Save this article

Uncertainty study on thermal and energy performances of a deterministic parameters based optimal aerogel glazing system using machine-learning method

Author

Listed:
  • Zhou, Yuekuan
  • Zheng, Siqian

Abstract

Uncertainty and sensitivity analyses of deterministic parameters based optimal aerogel glazing system are necessary due to multi-dimensional uncertainties in the real working condition, whereas thermal and energy performances of aerogel glazing system, in the academia, are normally characterized by deterministic parameters. In this study, a generic uncertainty quantification methodology was proposed using the two-dimensional Markov Chain Monte Carlo to quantify both aleatory and epistemic uncertainties of scenario parameters in the aerogel glazing system. A surrogate model, trained by mathematical heat and optical models using the machine-learning based data-driven method, was developed to predict the thermal and energy performances under multi-level scenario uncertainties. Results showed that, the developed surrogate model is efficient to deal with computational complexity of sophisticated light and heat transfer processes. When considering scenario uncertainties, the annual value of heat flux is reduced from 237.2 to 185.3 kWh/(m2.a) by 21.9%, and the annual value of total heat gain is reduced from 267.2 to 209.5 kWh/m2.a by 21.6%. This study proposes a generic methodology for multi-dimensional uncertainties’ quantification and a surrogate model for thousands of cases-based uncertainty analysis. Approaches for the stochastic uncertainty analysis on aerogel glazing system were presented, which can promote the optimal design in buildings.

Suggested Citation

  • Zhou, Yuekuan & Zheng, Siqian, 2020. "Uncertainty study on thermal and energy performances of a deterministic parameters based optimal aerogel glazing system using machine-learning method," Energy, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:energy:v:193:y:2020:i:c:s0360544219324132
    DOI: 10.1016/j.energy.2019.116718
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219324132
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.116718?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhou, Yuekuan & Zheng, Siqian, 2020. "Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities," Applied Energy, Elsevier, vol. 262(C).
    2. F. Owen Hoffman & Jana S. Hammonds, 1994. "Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability," Risk Analysis, John Wiley & Sons, vol. 14(5), pages 707-712, October.
    3. Abdul Mujeebu, Muhammad & Ashraf, Noman & Alsuwayigh, Abdulkarim, 2016. "Energy performance and economic viability of nano aerogel glazing and nano vacuum insulation panel in multi-story office building," Energy, Elsevier, vol. 113(C), pages 949-956.
    4. Berardi, Umberto & Nosrati, Roya Hamideh, 2018. "Long-term thermal conductivity of aerogel-enhanced insulating materials under different laboratory aging conditions," Energy, Elsevier, vol. 147(C), pages 1188-1202.
    5. Buratti, C. & Moretti, E., 2012. "Glazing systems with silica aerogel for energy savings in buildings," Applied Energy, Elsevier, vol. 98(C), pages 396-403.
    6. Buratti, C. & Moretti, E., 2012. "Experimental performance evaluation of aerogel glazing systems," Applied Energy, Elsevier, vol. 97(C), pages 430-437.
    7. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
    8. Huang, Yu & Niu, Jian-lei, 2015. "Application of super-insulating translucent silica aerogel glazing system on commercial building envelope of humid subtropical climates – Impact on space cooling load," Energy, Elsevier, vol. 83(C), pages 316-325.
    9. Chen, Youming & Xiao, Yaling & Zheng, Siqian & Liu, Yang & Li, Yupeng, 2018. "Dynamic heat transfer model and applicability evaluation of aerogel glazing system in various climates of China," Energy, Elsevier, vol. 163(C), pages 1115-1124.
    10. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    11. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine learning-based optimal design of a phase change material integrated renewable system with on-site PV, radiative cooling and hybrid ventilations—study of modelling and application in five clima," Energy, Elsevier, vol. 192(C).
    12. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    13. Baillis, D. & Coquard, R. & Moura, L.M., 2015. "Heat transfer in cellulose-based aerogels: Analytical modelling and measurements," Energy, Elsevier, vol. 84(C), pages 732-744.
    14. Berardi, Umberto, 2015. "The development of a monolithic aerogel glazed window for an energy retrofitting project," Applied Energy, Elsevier, vol. 154(C), pages 603-615.
    15. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
    16. Ihara, Takeshi & Gao, Tao & Grynning, Steinar & Jelle, Bjørn Petter & Gustavsen, Arild, 2015. "Aerogel granulate glazing facades and their application potential from an energy saving perspective," Applied Energy, Elsevier, vol. 142(C), pages 179-191.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liang Tang & Zhengxuan Liu & Yuekuan Zhou & Di Qin & Guoqiang Zhang, 2020. "Study on a Dynamic Numerical Model of an Underground Air Tunnel System for Cooling Applications—Experimental Validation and Multidimensional Parametrical Analysis," Energies, MDPI, vol. 13(5), pages 1-20, March.
    2. Zhou, Yuekuan, 2022. "A multi-stage supervised learning optimisation approach on an aerogel glazing system with stochastic uncertainty," Energy, Elsevier, vol. 258(C).
    3. Domitr, Paweł & Włostowski, Mateusz & Laskowski, Rafał & Jurkowski, Romuald, 2023. "Comparison of inverse uncertainty quantification methods for critical flow test," Energy, Elsevier, vol. 263(PA).
    4. Zhong, Shengyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Deng, Shuai & Li, Yang & Hussain, Sajjad & Wang, Xiaoyuan & Zhu, Jiebei, 2021. "Quantitative analysis of information interaction in building energy systems based on mutual information," Energy, Elsevier, vol. 214(C).
    5. Zhou, Yuekuan & Zheng, Siqian, 2020. "Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization," Renewable Energy, Elsevier, vol. 153(C), pages 375-391.
    6. Zhou, Yuekuan & Zheng, Siqian, 2020. "Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities," Applied Energy, Elsevier, vol. 262(C).
    7. Qin, Di & Liu, Zhengxuan & Zhou, Yuekuan & Yan, Zhongjun & Chen, Dachuan & Zhang, Guoqiang, 2021. "Dynamic performance of a novel air-soil heat exchanger coupling with diversified energy storage components—modelling development, experimental verification, parametrical design and robust operation," Renewable Energy, Elsevier, vol. 167(C), pages 542-557.

    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. Zhou, Yuekuan & Zheng, Siqian, 2020. "Stochastic uncertainty-based optimisation on an aerogel glazing building in China using supervised learning surrogate model and a heuristic optimisation algorithm," Renewable Energy, Elsevier, vol. 155(C), pages 810-826.
    2. Zhou, Yuekuan & Zheng, Siqian, 2020. "Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization," Renewable Energy, Elsevier, vol. 153(C), pages 375-391.
    3. Zhou, Yuekuan, 2022. "A multi-stage supervised learning optimisation approach on an aerogel glazing system with stochastic uncertainty," Energy, Elsevier, vol. 258(C).
    4. Liu, Yang & Chen, Youming & Lu, Lin & Peng, Jinqing & Zheng, Dongmei & Lu, Bin, 2023. "Optical path model and energy performance optimization of aerogel glazing system filled with aerogel granules," Applied Energy, Elsevier, vol. 334(C).
    5. Berardi, Umberto & Nosrati, Roya Hamideh, 2018. "Long-term thermal conductivity of aerogel-enhanced insulating materials under different laboratory aging conditions," Energy, Elsevier, vol. 147(C), pages 1188-1202.
    6. Liu, Yang & Lu, Lin & Chen, Youming & Lu, Bin, 2020. "Investigation on the optical and energy performances of different kinds of monolithic aerogel glazing systems," Applied Energy, Elsevier, vol. 261(C).
    7. Ghosh, Aritra & Norton, Brian, 2018. "Advances in switchable and highly insulating autonomous (self-powered) glazing systems for adaptive low energy buildings," Renewable Energy, Elsevier, vol. 126(C), pages 1003-1031.
    8. Mary K. Carroll & Ann M. Anderson & Sri Teja Mangu & Zineb Hajjaj & Margeaux Capron, 2022. "Aesthetic Aerogel Window Design for Sustainable Buildings," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    9. Ahmed Abdelrady & Mohamed Hssan Hassan Abdelhafez & Ayman Ragab, 2021. "Use of Insulation Based on Nanomaterials to Improve Energy Efficiency of Residential Buildings in a Hot Desert Climate," Sustainability, MDPI, vol. 13(9), pages 1-17, May.
    10. Berardi, Umberto, 2015. "The development of a monolithic aerogel glazed window for an energy retrofitting project," Applied Energy, Elsevier, vol. 154(C), pages 603-615.
    11. Chen, Youming & Xiao, Yaling & Zheng, Siqian & Liu, Yang & Li, Yupeng, 2018. "Dynamic heat transfer model and applicability evaluation of aerogel glazing system in various climates of China," Energy, Elsevier, vol. 163(C), pages 1115-1124.
    12. Paulos, Jason & Berardi, Umberto, 2020. "Optimizing the thermal performance of window frames through aerogel-enhancements," Applied Energy, Elsevier, vol. 266(C).
    13. Qu, Ke & Chen, Xiangjie & Wang, Yixin & Calautit, John & Riffat, Saffa & Cui, Xin, 2021. "Comprehensive energy, economic and thermal comfort assessments for the passive energy retrofit of historical buildings - A case study of a late nineteenth-century Victorian house renovation in the UK," Energy, Elsevier, vol. 220(C).
    14. Sun, Yanyi & Wilson, Robin & Wu, Yupeng, 2018. "A Review of Transparent Insulation Material (TIM) for building energy saving and daylight comfort," Applied Energy, Elsevier, vol. 226(C), pages 713-729.
    15. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    16. Cuce, Erdem & Cuce, Pinar Mert & Young, Chin-Huai, 2016. "Energy saving potential of heat insulation solar glass: Key results from laboratory and in-situ testing," Energy, Elsevier, vol. 97(C), pages 369-380.
    17. Qin, Di & Liu, Zhengxuan & Zhou, Yuekuan & Yan, Zhongjun & Chen, Dachuan & Zhang, Guoqiang, 2021. "Dynamic performance of a novel air-soil heat exchanger coupling with diversified energy storage components—modelling development, experimental verification, parametrical design and robust operation," Renewable Energy, Elsevier, vol. 167(C), pages 542-557.
    18. Abdul Mujeebu, Muhammad & Ashraf, Noman & Alsuwayigh, Abdulkarim, 2016. "Energy performance and economic viability of nano aerogel glazing and nano vacuum insulation panel in multi-story office building," Energy, Elsevier, vol. 113(C), pages 949-956.
    19. Ghosh, Aritra & Norton, Brian & Duffy, Aidan, 2016. "Measured thermal & daylight performance of an evacuated glazing using an outdoor test cell," Applied Energy, Elsevier, vol. 177(C), pages 196-203.
    20. Pacheco-Torgal, F., 2017. "High tech startup creation for energy efficient built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 618-629.

    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:eee:energy:v:193:y:2020:i:c:s0360544219324132. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.