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Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations

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  • Abhinandana Boodi

    (Laboratoire d’Innovation Numérique pour les Entreprises et les Apprentissages au service de la Compétitivité des Territoires, Centre des Études Supérieures Industrielles, 2 Avenue de Provence, 29200 Brest, France
    Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France)

  • Karim Beddiar

    (Laboratoire d’Innovation Numérique pour les Entreprises et les Apprentissages au service de la Compétitivité des Territoires, Centre des Études Supérieures Industrielles, 2 Avenue de Provence, 29200 Brest, France)

  • Malek Benamour

    (Laboratoire d’Innovation Numérique pour les Entreprises et les Apprentissages au service de la Compétitivité des Territoires, Centre des Études Supérieures Industrielles, 2 Avenue de Provence, 29200 Brest, France)

  • Yassine Amirat

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), ISEN Yncréa Ouest, 29200 Brest, France)

  • Mohamed Benbouzid

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
    Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

Abstract

Today, buildings consume more than 40% of primary energy in and produce more than 36% of CO 2 . So, an intelligent controller applied to the buildings for energy and comfort management could achieve significant reduction in energy consumption while improving occupant’s comfort. Conventional on/off controllers were only able to automate the tasks in building and were not well suited for energy optimization tasks. Therefore, building energy management has become a focal point in recent years, promising the development of various technologies for various scenarios. This paper deals with a state of the art review on recent developments in building energy management system (BEMS) and occupants comfort, focusing on three model types: white box, black box, and gray box models. Through a comparative study, this paper presents pros and cons of each model.

Suggested Citation

  • Abhinandana Boodi & Karim Beddiar & Malek Benamour & Yassine Amirat & Mohamed Benbouzid, 2018. "Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations," Energies, MDPI, vol. 11(10), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2604-:d:172901
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    References listed on IDEAS

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    2. 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.
    3. Golpîra, Hêriş & Khan, Syed Abdul Rehman, 2019. "A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty," Energy, Elsevier, vol. 170(C), pages 1113-1129.
    4. Giuseppe Anastasi & Carlo Bartoli & Paolo Conti & Emanuele Crisostomi & Alessandro Franco & Sergio Saponara & Daniele Testi & Dimitri Thomopulos & Carlo Vallati, 2021. "Optimized Energy and Air Quality Management of Shared Smart Buildings in the COVID-19 Scenario," Energies, MDPI, vol. 14(8), pages 1-17, April.
    5. Daniel Plörer & Sascha Hammes & Martin Hauer & Vincent van Karsbergen & Rainer Pfluger, 2021. "Control Strategies for Daylight and Artificial Lighting in Office Buildings—A Bibliometrically Assisted Review," Energies, MDPI, vol. 14(13), pages 1-18, June.
    6. 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.
    7. Lukas Lundström & Jan Akander & Jesús Zambrano, 2019. "Development of a Space Heating Model Suitable for the Automated Model Generation of Existing Multifamily Buildings—A Case Study in Nordic Climate," Energies, MDPI, vol. 12(3), pages 1-27, February.
    8. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2022. "Physically Consistent Neural Networks for building thermal modeling: Theory and analysis," Applied Energy, Elsevier, vol. 325(C).
    9. Abhinandana Boodi & Karim Beddiar & Yassine Amirat & Mohamed Benbouzid, 2022. "Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives," Energies, MDPI, vol. 15(4), pages 1-27, February.
    10. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    11. Giovanni Bianco & Stefano Bracco & Federico Delfino & Lorenzo Gambelli & Michela Robba & Mansueto Rossi, 2020. "A Building Energy Management System Based on an Equivalent Electric Circuit Model," Energies, MDPI, vol. 13(7), pages 1-23, April.
    12. Abhinandana Boodi & Karim Beddiar & Yassine Amirat & Mohamed Benbouzid, 2020. "Simplified Building Thermal Model Development and Parameters Evaluation Using a Stochastic Approach," Energies, MDPI, vol. 13(11), pages 1-23, June.
    13. Xu, Wenjie & Svetozarevic, Bratislav & Di Natale, Loris & Heer, Philipp & Jones, Colin N., 2024. "Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach," Applied Energy, Elsevier, vol. 358(C).

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