IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v279y2020ics030626192031285x.html
   My bibliography  Save this article

Smart control of dynamic phase change material wall system

Author

Listed:
  • de Gracia, Alvaro
  • Tarragona, Joan
  • Crespo, Alicia
  • Fernández, Cèsar

Abstract

This work presents two different smart control algorithms to manage a novel phase change material system integrated into building walls and roofs. This system is able to move a phase change material layer with respect to the insulation layer inside the building component. With this ability, the system can increase solar benefits in winter and take profit from night free cooling in summer. During the heating season, the system places the phase change material facing outdoors during sunny hours to melt it, and it moves the phase change material back facing indoors to provide space heating when desired. In the cooling season, the phase change material is moved to the outer face of insulation at night time to enhance its solidification process, and it is moved back to face indoors during cooling peak hours. An appropriate control system, referring to the schedule of operation and placement of phase change material layer with respect to the insulation (when phase change material is facing outdoors or indoors) is critical to achieve savings and avoid malfunctioning of the system. In this work, we have developed and numerically compared two different control algorithms based on weather forecast data for space heating and cooling applications. Experimentation has been done under four different climate conditions: Athens, Madrid, Strasbourg, and Helsinki. One of the control algorithms, based on local search (Tabu), provided the set of activations of the dynamic system for a 24 h period. The other algorithm is based on model predictive control with an horizon of 2.5 and 5 h. Results proved the feasibility of the two smart control methods, as well as their capacity to improve the energy benefits of the dynamic phase change material system in days with suitable weather conditions. Moreover, the two control algorithms successfully avoided activating the system in days with non-appropriate weather conditions.

Suggested Citation

  • de Gracia, Alvaro & Tarragona, Joan & Crespo, Alicia & Fernández, Cèsar, 2020. "Smart control of dynamic phase change material wall system," Applied Energy, Elsevier, vol. 279(C).
    • Handle: RePEc:eee:appene:v:279:y:2020:i:c:s030626192031285x
    DOI: 10.1016/j.apenergy.2020.115807
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192031285X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115807?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. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    2. Kazmi, Hussain & Mehmood, Fahad & Lodeweyckx, Stefan & Driesen, Johan, 2018. "Gigawatt-hour scale savings on a budget of zero: Deep reinforcement learning based optimal control of hot water systems," Energy, Elsevier, vol. 144(C), pages 159-168.
    3. de Gracia, Alvaro, 2019. "Dynamic building envelope with PCM for cooling purposes – Proof of concept," Applied Energy, Elsevier, vol. 235(C), pages 1245-1253.
    4. 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.
    5. Omrany, Hossein & Ghaffarianhoseini, Ali & Ghaffarianhoseini, Amirhosein & Raahemifar, Kaamran & Tookey, John, 2016. "Application of passive wall systems for improving the energy efficiency in buildings: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1252-1269.
    6. Gholamibozanjani, Gohar & Tarragona, Joan & Gracia, Alvaro de & Fernández, Cèsar & Cabeza, Luisa F. & Farid, Mohammed M., 2018. "Model predictive control strategy applied to different types of building for space heating," Applied Energy, Elsevier, vol. 231(C), pages 959-971.
    7. Tarragona, Joan & Fernández, Cèsar & de Gracia, Alvaro, 2020. "Model predictive control applied to a heating system with PV panels and thermal energy storage," Energy, Elsevier, vol. 197(C).
    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. Sun, Xiaoqin & Lin, Yian & Zhu, Ziyang & Li, Jie, 2022. "Optimized design of a distributed photovoltaic system in a building with phase change materials," Applied Energy, Elsevier, vol. 306(PA).
    2. Cabeza, Luisa F. & de Gracia, Alvaro & Zsembinszki, Gabriel & Borri, Emiliano, 2021. "Perspectives on thermal energy storage research," Energy, Elsevier, vol. 231(C).

    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. 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).
    2. 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).
    3. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    4. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics," Applied Energy, Elsevier, vol. 326(C).
    5. 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.
    6. Hou, Juan & Li, Haoran & Nord, Natasa, 2022. "Nonlinear model predictive control for the space heating system of a university building in Norway," Energy, Elsevier, vol. 253(C).
    7. 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.
    8. Yuan, Jianjuan & Huang, Ke & Han, Zhao & Zhou, Zhihua & Lu, Shilei, 2021. "A new feedback predictive model for improving the operation efficiency of heating station based on indoor temperature," Energy, Elsevier, vol. 222(C).
    9. Gohar Gholamibozanjani & Mohammed Farid, 2021. "A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings," Energies, MDPI, vol. 14(7), pages 1-39, March.
    10. Löhr, Yannik & Wolf, Daniel & Pollerberg, Clemens & Hörsting, Alexander & Mönnigmann, Martin, 2021. "Supervisory model predictive control for combined electrical and thermal supply with multiple sources and storages," Applied Energy, Elsevier, vol. 290(C).
    11. Hlanze, Philani & Jiang, Zhimin & Cai, Jie & Shen, Bo, 2023. "Model-based predictive control of multi-stage air-source heat pumps integrated with phase change material-embedded ceilings," Applied Energy, Elsevier, vol. 336(C).
    12. Lešić, Vinko & Martinčević, Anita & Vašak, Mario, 2017. "Modular energy cost optimization for buildings with integrated microgrid," Applied Energy, Elsevier, vol. 197(C), pages 14-28.
    13. Drgoňa, Ján & Picard, Damien & Kvasnica, Michal & Helsen, Lieve, 2018. "Approximate model predictive building control via machine learning," Applied Energy, Elsevier, vol. 218(C), pages 199-216.
    14. 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).
    15. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    16. Tarragona, Joan & Pisello, Anna Laura & Fernández, Cèsar & de Gracia, Alvaro & Cabeza, Luisa F., 2021. "Systematic review on model predictive control strategies applied to active thermal energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    17. Gholamibozanjani, Gohar & Tarragona, Joan & Gracia, Alvaro de & Fernández, Cèsar & Cabeza, Luisa F. & Farid, Mohammed M., 2018. "Model predictive control strategy applied to different types of building for space heating," Applied Energy, Elsevier, vol. 231(C), pages 959-971.
    18. Germán Ramos Ruiz & Eva Lucas Segarra & Carlos Fernández Bandera, 2018. "Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model," Energies, MDPI, vol. 12(1), pages 1-18, December.
    19. Tarragona, Joan & Fernández, Cèsar & de Gracia, Alvaro, 2020. "Model predictive control applied to a heating system with PV panels and thermal energy storage," Energy, Elsevier, vol. 197(C).
    20. Zhou, Shiqiang & Razaqpur, A. Ghani, 2022. "Efficient heating of buildings by passive solar energy utilizing an innovative dynamic building envelope incorporating phase change material," Renewable Energy, Elsevier, vol. 197(C), pages 305-319.

    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:appene:v:279:y:2020:i:c:s030626192031285x. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.