IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i6p2693-d1096337.html
   My bibliography  Save this article

Methodology for Modeling Multiple Non-Homogeneous Thermal Zones Using Lumped Parameters Technique and Graph Theory

Author

Listed:
  • Frank Florez

    (Faculty of Engineering, Universidad Autónoma de Manizales, Manizales 170003, Colombia)

  • Jesús Alejandro Alzate-Grisales

    (Faculty of Engineering, Universidad Autónoma de Manizales, Manizales 170003, Colombia)

  • Pedro Fernández de Córdoba

    (Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain)

  • John Alexander Taborda-Giraldo

    (Universidad del Magdalena, Santa Marta, Colombia)

Abstract

Asymmetric thermal zones or even non-rectangular structures are common in residential buildings. These types of structures are not easy to model with specialized programs, and it is difficult to know the heat flows and the relationships between the different variables. This paper presents a methodology for modeling structures with multiple thermal zones using the graph theory arrangement. The methodology allows for generating a mathematical model using all the walls of each thermal zone. The modeling method uses the lumped parameter technique with a structure of two resistors and two capacitors for each thermal zone. The walls and internal surfaces of each zone define the thermal resistances, and the elements for the network structure are created by reducing resistances. The structure selected as a case study is similar to a residential apartment, which demonstrates the possibility of modeling complex and non-traditional structures. The accuracy of the generated mathematical model is verified by comparison with experimental data recorded in a scaled-down model. The reduced model is constructed using a 1:10 ratio with a real apartment. The proposed methodology is used to generate a graph arrangement adjusted to the case study, using the surfaces to build the mathematical model. The experimental data allowed to adjust the simulation results with errors in the range of 1.88% to 6.63% for different thermal zones. This methodology can be used to model different apartments, offices, or non-asymmetric structures and to analyze individual levels in buildings.

Suggested Citation

  • Frank Florez & Jesús Alejandro Alzate-Grisales & Pedro Fernández de Córdoba & John Alexander Taborda-Giraldo, 2023. "Methodology for Modeling Multiple Non-Homogeneous Thermal Zones Using Lumped Parameters Technique and Graph Theory," Energies, MDPI, vol. 16(6), pages 1-20, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2693-:d:1096337
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/6/2693/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/6/2693/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chaudhuri, Tanaya & Soh, Yeng Chai & Li, Hua & Xie, Lihua, 2019. "A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings," Applied Energy, Elsevier, vol. 248(C), pages 44-53.
    2. Frank Florez & Pedro Fernández de Cordoba & John Taborda & Miguel Polo & Juan Carlos Castro-Palacio & María Jezabel Pérez-Quiles, 2020. "Sliding Modes Control for Heat Transfer in Geodesic Domes," Mathematics, MDPI, vol. 8(6), pages 1-15, June.
    3. Frank Florez & Pedro Fernández-de-Córdoba & John Taborda & Juan Carlos Castro-Palacio & José Luis Higón-Calvet & M. Jezabel Pérez-Quiles, 2021. "Passive Strategies to Improve the Comfort Conditions in a Geodesic Dome," Mathematics, MDPI, vol. 9(6), pages 1-15, March.
    4. Frank Florez & Pedro Fernández de Córdoba & José Luis Higón & Gerard Olivar & John Taborda, 2019. "Modeling, Simulation, and Temperature Control of a Thermal Zone with Sliding Modes Strategy," Mathematics, MDPI, vol. 7(6), pages 1-13, June.
    5. Mora, Luca & Gerli, Paolo & Ardito, Lorenzo & Messeni Petruzzelli, Antonio, 2023. "Smart city governance from an innovation management perspective: Theoretical framing, review of current practices, and future research agenda," Technovation, Elsevier, vol. 123(C).
    6. Lu, Yanyu & Dong, Jiankai & Liu, Jing, 2020. "Zonal modelling for thermal and energy performance of large space buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    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. Pedro Fernández de Córdoba & Frank Florez Montes & Miguel E. Iglesias Martínez & Jose Guerra Carmenate & Romeo Selvas & John Taborda, 2023. "Design of an Algorithm for Modeling Multiple Thermal Zones Using a Lumped-Parameter Model," Energies, MDPI, vol. 16(5), pages 1-22, February.
    2. Juana Isabel Méndez & Adán Medina & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2022. "Evolving Gamified Smart Communities in Mexico to Save Energy in Communities through Intelligent Interfaces," Energies, MDPI, vol. 15(15), pages 1-29, July.
    3. Yang, Zhen & Gao, Weijun & Han, Qing & Qi, Liyan, 2024. "Aggravating or alleviating? Smart city construction and urban inequality in China," Technology in Society, Elsevier, vol. 77(C).
    4. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    5. Wang, Cheng & Liu, Chuang & Lin, Yuzhang & Bi, Tianshu, 2020. "Day-ahead dispatch of integrated electric-heat systems considering weather-parameter-driven residential thermal demands," Energy, Elsevier, vol. 203(C).
    6. Qingwen, Wang & XiaoHui, Chu & Chao, Yu, 2024. "Modeling of heat gain through green roofs utilizing artificial intelligence techniques," Energy, Elsevier, vol. 303(C).
    7. López-Pérez, Luis Adrián & Flores-Prieto, José Jassón, 2023. "Adaptive thermal comfort approach to save energy in tropical climate educational building by artificial intelligence," Energy, Elsevier, vol. 263(PA).
    8. Chakraborty, Debaditya & Alam, Arafat & Chaudhuri, Saptarshi & Başağaoğlu, Hakan & Sulbaran, Tulio & Langar, Sandeep, 2021. "Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence," Applied Energy, Elsevier, vol. 291(C).
    9. Zhenzhong Guan & Xiang Xu & Yibing Xue & Chongjie Wang, 2022. "Multi-Objective Optimization Design of Geometric Parameters of Atrium in nZEB Based on Energy Consumption, Carbon Emission and Cost," Sustainability, MDPI, vol. 15(1), pages 1-24, December.
    10. Wenping Xue & Xiao Cao & Guangfa Zhang & Gang Tan & Zilong Liu & Kangji Li, 2022. "Structural Optimization of Heat Sink for Thermoelectric Conversion Unit in Personal Comfort System," Energies, MDPI, vol. 15(8), pages 1-16, April.
    11. Cheng, Fanyong & Cui, Can & Cai, Wenjian & Zhang, Xin & Ge, Yuan & Li, Bingxu, 2022. "A novel data-driven air balancing method with energy-saving constraint strategy to minimize the energy consumption of ventilation system," Energy, Elsevier, vol. 239(PB).
    12. Nastro, Francesco & Sorrentino, Marco & Trifirò, Alena, 2022. "A machine learning approach based on neural networks for energy diagnosis of telecommunication sites," Energy, Elsevier, vol. 245(C).
    13. 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).
    14. Liu, Qian & Gao, Jian & Li, Shijie, 2024. "The innovation model and upgrade path of digitalization driven tourism industry: Longitudinal case study of OCT," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    15. Sun, Yutao & Jiang, Lin & Cao, Cong & Tseng, Fang-Mei, 2024. "From contributors to boundary spanners: Evolving roles of government agencies in China’s innovation policy network (1980–2019)," Technovation, Elsevier, vol. 132(C).
    16. Yang, Ting & Zhao, Liyuan & Li, Wei & Wu, Jianzhong & Zomaya, Albert Y., 2021. "Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 300(C).
    17. Sha, Kritika & Taeihagh, Araz & De Jong, Martin, 2024. "Governing disruptive technologies for inclusive development in cities: A systematic literature review," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    18. Linye Song & Kaijun Li & Xinghui Zhang & Jing Hua & Cong Zhang, 2023. "Differentiated Control of Large Spatial Environments: Air Curtain Grid System," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    19. Chengxiang Chu & Zhenyang Shen & Hanyi Xu & Qizhi Wei & Cong Cao, 2024. "How to avoid sinking in swamp: exploring the intentions of digitally disadvantaged groups to use a new public infrastructure that combines physical and virtual spaces," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.
    20. Thiago Poleto & Thyago Celso Cavalcante Nepomuceno & Victor Diogho Heuer de Carvalho & Ligiane Cristina Braga de Oliveira Friaes & Rodrigo Cleiton Paiva de Oliveira & Ciro José Jardim Figueiredo, 2023. "Information Security Applications in Smart Cities: A Bibliometric Analysis of Emerging Research," Future Internet, MDPI, vol. 15(12), pages 1-36, December.

    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:6:p:2693-:d:1096337. 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.