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Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings

Author

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  • Anass Berouine

    (College of Engineering and Architecture, International University of Rabat, LERMA Lab, Sala El Jadida 11100, Morocco
    ENSIAS, Mohamed V University, Rabat 10713, Morocco)

  • Radouane Ouladsine

    (College of Engineering and Architecture, International University of Rabat, LERMA Lab, Sala El Jadida 11100, Morocco)

  • Mohamed Bakhouya

    (College of Engineering and Architecture, International University of Rabat, LERMA Lab, Sala El Jadida 11100, Morocco)

  • Mohamed Essaaidi

    (ENSIAS, Mohamed V University, Rabat 10713, Morocco)

Abstract

Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have been widely studied in order to reduce the energy usage while enhancing the occupants’ comfort. In this paper, a generalized predictive control (GPC) algorithm based on controlled auto-regressive integrated moving average is investigated for standalone ventilation systems’ control. A building’s ventilation system is first modeled together with the GPC and MPC controllers. Simulations have been conducted for validation purposes and are structured into two main parts. In the first part, we compare the MPC with two traditional controllers, while the second part is dedicated to the comparison of the MPC against the GPC controller. Simulation results show the effectiveness of the GPC in reducing the energy consumption by about 4.34% while providing significant indoor air quality improvement.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3246-:d:375168
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    References listed on IDEAS

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    1. 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.
    2. Sofia Boulmrharj & Youssef NaitMalek & Abdellatif Elmouatamid & Mohamed Bakhouya & Radouane Ouladsine & Khalid Zine-Dine & Mohammed Khaidar & Mostapha Siniti, 2019. "Battery Characterization and Dimensioning Approaches for Micro-Grid Systems," Energies, MDPI, vol. 12(7), pages 1-13, April.
    3. Nejat, Payam & Jomehzadeh, Fatemeh & Taheri, Mohammad Mahdi & Gohari, Mohammad & Abd. Majid, Muhd Zaimi, 2015. "A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 843-862.
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    Cited by:

    1. Qiang, Guofeng & Tang, Shu & Hao, Jianli & Di Sarno, Luigi & Wu, Guangdong & Ren, Shaoxing, 2023. "Building automation systems for energy and comfort management in green buildings: A critical review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).

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