IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v7y2019i6p503-d236587.html
   My bibliography  Save this article

Modeling, Simulation, and Temperature Control of a Thermal Zone with Sliding Modes Strategy

Author

Listed:
  • Frank Florez

    (Faculty of Engineering and Architecture, Universidad Nacional de Colombia, Campus la Nubia, 170003 Manizales, Colombia)

  • Pedro Fernández de Córdoba

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

  • José Luis Higón

    (Department of Architectural Graphic Expression, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain)

  • Gerard Olivar

    (Faculty of Exact and Natural Sciences, Universidad Nacional de Colombia, Campus la Nubia, 170003 Manizales, Colombia)

  • John Taborda

    (Faculty of Engineering, Universidad del Magdalena, 470003 Santa Marta, Colombia)

Abstract

To reduce the energy consumption in buildings is necessary to analyze individual rooms and thermal zones, studying mathematical models and applying new control techniques. In this paper, the design, simulation and experimental evaluation of a sliding mode controller for regulating internal temperature in a thermal zone is presented. We propose an experiment with small physical dimensions, consisting of a closed wooden box with heat internal sources to stimulate temperature gradients through operating and shut down cycles.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:6:p:503-:d:236587
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/7/6/503/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/7/6/503/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
    2. Huang, Yanjun & Khajepour, Amir & Ding, Haitao & Bagheri, Farshid & Bahrami, Majid, 2017. "An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 188(C), pages 576-585.
    3. Fiorentini, Massimo & Wall, Josh & Ma, Zhenjun & Braslavsky, Julio H. & Cooper, Paul, 2017. "Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage," Applied Energy, Elsevier, vol. 187(C), pages 465-479.
    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. 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. 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.

    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. Samy Faddel & Guanyu Tian & Qun Zhou, 2021. "Decentralized Management of Commercial HVAC Systems," Energies, MDPI, vol. 14(11), pages 1-18, May.
    2. Heidari, Amirreza & Maréchal, François & Khovalyg, Dolaana, 2022. "Reinforcement Learning for proactive operation of residential energy systems by learning stochastic occupant behavior and fluctuating solar energy: Balancing comfort, hygiene and energy use," Applied Energy, Elsevier, vol. 318(C).
    3. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    4. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    5. Lork, Clement & Li, Wen-Tai & Qin, Yan & Zhou, Yuren & Yuen, Chau & Tushar, Wayes & Saha, Tapan K., 2020. "An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management," Applied Energy, Elsevier, vol. 276(C).
    6. Cui, Can & Zhang, Xin & Cai, Wenjian, 2020. "An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model," Applied Energy, Elsevier, vol. 264(C).
    7. Giacomo Valente & Vittoriano Muttillo & Mirco Muttillo & Gianluca Barile & Alfiero Leoni & Walter Tiberti & Luigi Pomante, 2019. "SPOF—Slave Powerlink on FPGA for Smart Sensors and Actuators Interfacing for Industry 4.0 Applications," Energies, MDPI, vol. 12(9), pages 1-13, April.
    8. Filipe, Jorge & Bessa, Ricardo J. & Reis, Marisa & Alves, Rita & Póvoa, Pedro, 2019. "Data-driven predictive energy optimization in a wastewater pumping station," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    9. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    10. Wassim Salameh & Jalal Faraj & Elias Harika & Rabih Murr & Mahmoud Khaled, 2021. "On the Optimization of Electrical Water Heaters: Modelling Simulations and Experimentation," Energies, MDPI, vol. 14(13), pages 1-12, June.
    11. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Chatzis, Georgios V. & Biskas, Pandelis N. & Keranidis, Stratos D., 2021. "Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm," Applied Energy, Elsevier, vol. 299(C).
    12. Jing Zhao & Yu Shan, 2020. "A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System," Energies, MDPI, vol. 13(3), pages 1-17, January.
    13. Vo-Van Thanh & Wencong Su & Bin Wang, 2022. "Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch," Energies, MDPI, vol. 15(6), pages 1-19, March.
    14. Georgios D. Kontes & Georgios I. Giannakis & Víctor Sánchez & Pablo De Agustin-Camacho & Ander Romero-Amorrortu & Natalia Panagiotidou & Dimitrios V. Rovas & Simone Steiger & Christopher Mutschler & G, 2018. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings," Energies, MDPI, vol. 11(12), pages 1-23, December.
    15. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2019. "A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin," Applied Energy, Elsevier, vol. 241(C), pages 331-361.
    16. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
    17. Francesco Smarra & Giovanni Domenico Di Girolamo & Vincenzo Gattulli & Fabio Graziosi & Alessandro D’Innocenzo, 2020. "Learning Models for Seismic-Induced Vibrations Optimal Control in Structures via Random Forests," Journal of Optimization Theory and Applications, Springer, vol. 187(3), pages 855-874, December.
    18. Wang, L.X. & Zheng, J.H. & Li, M.S. & Lin, X. & Jing, Z.X. & Wu, P.Z. & Wu, Q.H. & Zhou, X.X., 2019. "Multi-time scale dynamic analysis of integrated energy systems: An individual-based model," Applied Energy, Elsevier, vol. 237(C), pages 848-861.
    19. 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).
    20. Suyang Zhou & Di He & Zhiyang Zhang & Zhi Wu & Wei Gu & Junjie Li & Zhe Li & Gaoxiang Wu, 2019. "A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network," Sustainability, MDPI, vol. 11(23), pages 1-18, November.

    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:jmathe:v:7:y:2019:i:6:p:503-:d:236587. 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.