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

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  • 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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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).
    7. 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.
    8. 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.
    9. 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).
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