IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i5p2154-d1351521.html
   My bibliography  Save this article

Balancing Sustainability and Comfort: A Holistic Study of Building Control Strategies That Meet the Global Standards for Efficiency and Thermal Comfort

Author

Listed:
  • Amal Azzi

    (Multidisciplinary Laboratory of Research and Innovation, Moroccan School of Engineering Sciences, Casablanca 20250, Morocco
    Laboratory of Advanced Systems Engineering, National School of Applied Sciences, Ibn Tofail Univesity Campus, Kenitra 14000, Morocco)

  • Mohamed Tabaa

    (Laboratory of Advanced Systems Engineering, National School of Applied Sciences, Ibn Tofail Univesity Campus, Kenitra 14000, Morocco)

  • Badr Chegari

    (I2M Laboratory, University of Bordeaux, Centre National de la Recherche Scientifique (CNRS), Arts et Métiers Paris Tech, 33400 Talence, France)

  • Hanaa Hachimi

    (Laboratory of Advanced Systems Engineering, National School of Applied Sciences, Ibn Tofail Univesity Campus, Kenitra 14000, Morocco)

Abstract

The objective of energy transition is to convert the worldwide energy sector from using fossil fuels to using sources that do not emit carbon by the end of the current century. In order to achieve sustainability in the construction of energy-positive buildings, it is crucial to employ novel approaches to reduce reliance on fossil fuels. Hence, it is essential to develop buildings with very efficient structures to promote sustainable energy practices and minimize the environmental impact. Our aims were to shed some light on the standards, building modeling strategies, and recent advances regarding the methods of control utilized in the building sector and to pinpoint the areas for improvement in the methods of control in buildings in hopes of giving future scholars a clearer understanding of the issues that need to be addressed. Accordingly, we focused on recent works that handle methods of control in buildings, which we filtered based on their approaches and relevance to the subject at hand. Furthermore, we ran a critical analysis of the reviewed works. Our work proves that model predictive control (MPC) is the most commonly used among other methods in combination with AI. However, it still faces some challenges, especially regarding its complexity.

Suggested Citation

  • Amal Azzi & Mohamed Tabaa & Badr Chegari & Hanaa Hachimi, 2024. "Balancing Sustainability and Comfort: A Holistic Study of Building Control Strategies That Meet the Global Standards for Efficiency and Thermal Comfort," Sustainability, MDPI, vol. 16(5), pages 1-36, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2154-:d:1351521
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/5/2154/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/5/2154/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Konstantina Siountri & Emmanouil Skondras & Dimitrios D. Vergados, 2020. "Developing Smart Buildings Using Blockchain, Internet of Things, and Building Information Modeling," International Journal of Interdisciplinary Telecommunications and Networking (IJITN), IGI Global, vol. 12(3), pages 1-15, July.
    2. DeQuante Rashon Mckoy & Raymond Charles Tesiero & Yaa Takyiwaa Acquaah & Balakrishna Gokaraju, 2023. "Review of HVAC Systems History and Future Applications," Energies, MDPI, vol. 16(17), pages 1-15, August.
    3. Farinaz Behrooz & Norman Mariun & Mohammad Hamiruce Marhaban & Mohd Amran Mohd Radzi & Abdul Rahman Ramli, 2018. "Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps," Energies, MDPI, vol. 11(3), pages 1-41, February.
    4. Naylor, Sophie & Gillott, Mark & Lau, Tom, 2018. "A review of occupant-centric building control strategies to reduce building energy use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 1-10.
    5. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2021. "Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control," Applied Energy, Elsevier, vol. 288(C).
    6. Adrian Chojecki & Arkadiusz Ambroziak & Piotr Borkowski, 2023. "Fuzzy Controllers Instead of Classical PIDs in HVAC Equipment: Dusting Off a Well-Known Technology and Today’s Implementation for Better Energy Efficiency and User Comfort," Energies, MDPI, vol. 16(7), pages 1-21, March.
    7. Jose A. Afonso & Vitor Monteiro & Joao L. Afonso, 2023. "Internet of Things Systems and Applications for Smart Buildings," Energies, MDPI, vol. 16(6), pages 1-3, March.
    8. Arroyo, Javier & Manna, Carlo & Spiessens, Fred & Helsen, Lieve, 2022. "Reinforced model predictive control (RL-MPC) for building energy management," Applied Energy, Elsevier, vol. 309(C).
    9. Bay, Christopher J. & Chintala, Rohit & Chinde, Venkatesh & King, Jennifer, 2022. "Distributed model predictive control for coordinated, grid-interactive buildings," Applied Energy, Elsevier, vol. 312(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. Wald, Dylan & King, Jennifer & Bay, Christopher J. & Chintala, Rohit & Johnson, Kathryn, 2022. "Integration of distributed controllers: Power reference tracking through charging station and building coordination," Applied Energy, Elsevier, vol. 314(C).
    2. Joanna Piotrowska-Woroniak & Krzysztof Cieśliński & Grzegorz Woroniak & Jonas Bielskus, 2022. "The Impact of Thermo-Modernization and Forecast Regulation on the Reduction of Thermal Energy Consumption and Reduction of Pollutant Emissions into the Atmosphere on the Example of Prefabricated Build," Energies, MDPI, vol. 15(8), pages 1-32, April.
    3. Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    4. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    5. Anass Berouine & Radouane Ouladsine & Mohamed Bakhouya & Mohamed Essaaidi, 2020. "Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings," Energies, MDPI, vol. 13(12), pages 1-16, June.
    6. Rafiq Asghar & Francesco Riganti Fulginei & Hamid Wadood & Sarmad Saeed, 2023. "A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
    7. Nweye, Kingsley & Nagy, Zoltan, 2022. "MARTINI: Smart meter driven estimation of HVAC schedules and energy savings based on Wi-Fi sensing and clustering," Applied Energy, Elsevier, vol. 316(C).
    8. Nweye, Kingsley & Sankaranarayanan, Siva & Nagy, Zoltan, 2023. "MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities," Applied Energy, Elsevier, vol. 346(C).
    9. Habib Sadri & Ibrahim Yitmen & Lavinia Chiara Tagliabue & Florian Westphal & Algan Tezel & Afshin Taheri & Goran Sibenik, 2023. "Integration of Blockchain and Digital Twins in the Smart Built Environment Adopting Disruptive Technologies—A Systematic Review," Sustainability, MDPI, vol. 15(4), pages 1-46, February.
    10. Trafczynski, Marian & Markowski, Mariusz & Urbaniec, Krzysztof, 2019. "Energy saving potential of a simple control strategy for heat exchanger network operation under fouling conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 355-364.
    11. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    12. Anh Tuan Phan & Thi Tuyet Hong Vu & Dinh Quang Nguyen & Eleonora Riva Sanseverino & Hang Thi-Thuy Le & Van Cong Bui, 2022. "Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network," Energies, MDPI, vol. 15(23), pages 1-16, December.
    13. Ntumba Marc-Alain Mutombo & Bubele Papy Numbi, 2022. "Development of a Linear Regression Model Based on the Most Influential Predictors for a Research Office Cooling Load," Energies, MDPI, vol. 15(14), pages 1-20, July.
    14. Tayyaba Nosheen & Ahsan Ali & Muhammad Umar Chaudhry & Dmitry Nazarenko & Inam ul Hasan Shaikh & Vadim Bolshev & Muhammad Munwar Iqbal & Sohail Khalid & Vladimir Panchenko, 2023. "A Fractional Order Controller for Sensorless Speed Control of an Induction Motor," Energies, MDPI, vol. 16(4), pages 1-15, February.
    15. Coma, Julià & Chàfer, Marta & Pérez, Gabriel & Cabeza, Luisa F., 2020. "How internal heat loads of buildings affect the effectiveness of vertical greenery systems? An experimental study," Renewable Energy, Elsevier, vol. 151(C), pages 919-930.
    16. Fu, Yangyang & Xu, Shichao & Zhu, Qi & O’Neill, Zheng & Adetola, Veronica, 2023. "How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system," Energy, Elsevier, vol. 273(C).
    17. Zhou, Xinlei & Xue, Shan & Du, Han & Ma, Zhenjun, 2023. "Optimization of building demand flexibility using reinforcement learning and rule-based expert systems," Applied Energy, Elsevier, vol. 350(C).
    18. Lee, Zachary E. & Zhang, K. Max, 2021. "Generalized reinforcement learning for building control using Behavioral Cloning," Applied Energy, Elsevier, vol. 304(C).
    19. Hua, Weiqi & Jiang, Jing & Sun, Hongjian & Tonello, Andrea M. & Qadrdan, Meysam & Wu, Jianzhong, 2022. "Data-driven prosumer-centric energy scheduling using convolutional neural networks," Applied Energy, Elsevier, vol. 308(C).
    20. Serafín Alonso & Antonio Morán & Miguel Ángel Prada & Perfecto Reguera & Juan José Fuertes & Manuel Domínguez, 2019. "A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study," Energies, MDPI, vol. 12(5), pages 1-28, March.

    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:jsusta:v:16:y:2024:i:5:p:2154-:d:1351521. 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.