IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i10p4211-d1151479.html
   My bibliography  Save this article

A Novel AI-Based Thermal Conductivity Predictor in the Insulation Performance Analysis of Signal-Transmissive Wall

Author

Listed:
  • Xiaolei Wang

    (Department of Electrical Engineering and Energy Technology, University of Vaasa, P.O. Box 700, FIN-65101 Vaasa, Finland)

  • Xiaoshu Lü

    (Department of Electrical Engineering and Energy Technology, University of Vaasa, P.O. Box 700, FIN-65101 Vaasa, Finland
    Department of Civil Engineering, Aalto University, P.O. Box 11000, FIN-02150 Espoo, Finland)

  • Lauri Vähä-Savo

    (Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, P.O. Box 13500, FIN-00076 Espoo, Finland)

  • Katsuyuki Haneda

    (Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, P.O. Box 13500, FIN-00076 Espoo, Finland)

Abstract

It is well known that thermal conductivity measurement is a challenging task, due to the weaknesses of the traditional methods, such as the high cost, complex data analysis, and limitations of sample size. Nowadays, the requirement of quality of life and tightening energy efficiency regulations of buildings promote the demand for new construction materials. However, limited by the size and inhomogeneous structure, the thermal conductivity measurement of wall samples becomes a demanding topic. Additionally, we find the thermal parameter values of the samples measured in the laboratory are different from those obtained by theoretical computation. In this paper, a novel signal-transmissive wall is designed to provide the problem solving of signal connectivity in 5G. We further propose a new thermal conductivity predictor based on the Harmony Search (HS) algorithm to estimate the thermal properties of laboratory-made wall samples. The advantages of our approach over the conventional methods are simplicity and robustness, which can be generalized to a wide range of solid samples in the laboratory measurement.

Suggested Citation

  • Xiaolei Wang & Xiaoshu Lü & Lauri Vähä-Savo & Katsuyuki Haneda, 2023. "A Novel AI-Based Thermal Conductivity Predictor in the Insulation Performance Analysis of Signal-Transmissive Wall," Energies, MDPI, vol. 16(10), pages 1-16, May.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4211-:d:1151479
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/10/4211/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/10/4211/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Palacios, Anabel & Cong, Lin & Navarro, M.E. & Ding, Yulong & Barreneche, Camila, 2019. "Thermal conductivity measurement techniques for characterizing thermal energy storage materials – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 32-52.
    2. Güven, Aykut Fatih & Yörükeren, Nuran & Samy, Mohamed Mahmoud, 2022. "Design optimization of a stand-alone green energy system of university campus based on Jaya-Harmony Search and Ant Colony Optimization algorithms approaches," Energy, Elsevier, vol. 253(C).
    3. Tao Lu & Lauri Vähä-Savo & Xiaoshu Lü & Katsuyuki Haneda, 2023. "Thermal Impact of 5G Antenna Systems in Sandwich Walls," Energies, MDPI, vol. 16(6), pages 1-17, March.
    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. Zhou, Jianguo & Xu, Zhongtian, 2023. "Optimal sizing design and integrated cost-benefit assessment of stand-alone microgrid system with different energy storage employing chameleon swarm algorithm: A rural case in Northeast China," Renewable Energy, Elsevier, vol. 202(C), pages 1110-1137.
    2. Park, Joungho & Kang, Sungho & Kim, Sunwoo & Kim, Hana & Kim, Sang-Kyung & Lee, Jay H., 2024. "Optimizing green hydrogen systems: Balancing economic viability and reliability in the face of supply-demand volatility," Applied Energy, Elsevier, vol. 368(C).
    3. Sascha Henninger & Darline Christmann, 2023. "Teaching about Climate-Efficient Buildings in the Context of Geographic Education for Sustainability," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    4. Khanahmadi, Abbas & Ghaffarpour, Reza, 2022. "A cost-effective and emission-Aware hybrid system considering uncertainty: A case study in a remote area," Renewable Energy, Elsevier, vol. 201(P1), pages 977-992.
    5. Jiaming Wang & Hailong He & Miles Dyck & Jialong Lv, 2020. "A Review and Evaluation of Predictive Models for Thermal Conductivity of Sands at Full Water Content Range," Energies, MDPI, vol. 13(5), pages 1-15, March.
    6. Cárdenas-Ramírez, Carolina & Gómez, Maryory A. & Jaramillo, Franklin & Fernández, Angel G. & Cabeza, Luisa F., 2021. "Experimental determination of thermal conductivity of fatty acid binary mixtures and their shape-stabilized composites," Renewable Energy, Elsevier, vol. 175(C), pages 1167-1173.
    7. Popescu, Daniela & Dragomirescu, Andrei, 2024. "Cost-benefit analysis of a hydro-solar microsystem with Archimedean screw hydro turbine sized for a prosumer building," Renewable Energy, Elsevier, vol. 226(C).
    8. Huang, Zhiliang & Wang, Huaixing & Gan, Zhouwang & Yang, Tongguang & Yuan, Cong & Lei, Bing & Chen, Jie & Wu, Shengben, 2024. "An mechanical/thermal analytical model for prismatic lithium-ion cells with silicon‑carbon electrodes in charge/discharge cycles," Applied Energy, Elsevier, vol. 365(C).
    9. Abid, Md. Shadman & Ahshan, Razzaqul & Al Abri, Rashid & Al-Badi, Abdullah & Albadi, Mohammed, 2024. "Techno-economic and environmental assessment of renewable energy sources, virtual synchronous generators, and electric vehicle charging stations in microgrids," Applied Energy, Elsevier, vol. 353(PA).
    10. Amad Ali & Hafiz Abdul Muqeet & Tahir Khan & Asif Hussain & Muhammad Waseem & Kamran Ali Khan Niazi, 2023. "IoT-Enabled Campus Prosumer Microgrid Energy Management, Architecture, Storage Technologies, and Simulation Tools: A Comprehensive Study," Energies, MDPI, vol. 16(4), pages 1-19, February.
    11. Rolka, Paulina & Przybylinski, Tomasz & Kwidzinski, Roman & Lackowski, Marcin, 2022. "Thermal properties of RT22 HC and RT28 HC phase change materials proposed to reduce energy consumption in heating and cooling systems," Renewable Energy, Elsevier, vol. 197(C), pages 462-471.
    12. Liu, Huan & Niu, Jinfei & Wang, Xiaodong & Wu, Dezhen, 2019. "Design and construction of mesoporous silica/n-eicosane phase-change nanocomposites for supercooling depression and heat transfer enhancement," Energy, Elsevier, vol. 188(C).
    13. Güven, Aykut Fatih, 2024. "Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management," Energy, Elsevier, vol. 303(C).
    14. Zhou, Yifei & Wang, Shunli & Xie, Yanxing & Zeng, Jiawei & Fernandez, Carlos, 2024. "Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm," Energy, Elsevier, vol. 300(C).
    15. Grzegorz Ostasz & Dominika Siwiec & Andrzej Pacana, 2022. "Model to Determine the Best Modifications of Products with Consideration Customers’ Expectations," Energies, MDPI, vol. 15(21), pages 1-21, October.
    16. Thirunavukkarasu, M. & Lala, Himadri & Sawle, Yashwant, 2023. "Techno-economic-environmental analysis of off-grid hybrid energy systems using honey badger optimizer," Renewable Energy, Elsevier, vol. 218(C).
    17. Farhad Salek & Aydin Azizi & Shahaboddin Resalati & Paul Henshall & Denise Morrey, 2022. "Mathematical Modelling and Simulation of Second Life Battery Pack with Heterogeneous State of Health," Mathematics, MDPI, vol. 10(20), pages 1-23, October.
    18. Tudorică, Bogdan-George & Bucur, Cristian & Panait, Mirela & Oprea, Simona-Vasilica & Bâra, Adela, 2024. "Energetic Equilibrium: Optimizing renewable and non-renewable energy sources via particle swarm optimization," Utilities Policy, Elsevier, vol. 87(C).
    19. Yu, Qiang & Zhang, Cancan & Lu, Yuanwei & Kong, Qinglong & Wei, Haijiao & Yang, Yanchun & Gao, Qi & Wu, Yuting & Sciacovelli, Adriano, 2021. "Comprehensive performance of composite phase change materials based on eutectic chloride with SiO2 nanoparticles and expanded graphite for thermal energy storage system," Renewable Energy, Elsevier, vol. 172(C), pages 1120-1132.
    20. Olabi, A.G. & Abdelkareem, Mohammad Ali & Wilberforce, Tabbi & Sayed, Enas Taha, 2021. "Application of graphene in energy storage device – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(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:16:y:2023:i:10:p:4211-:d:1151479. 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.