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Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

Author

Listed:
  • Pouria Bahramnia

    (Electrical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran)

  • Seyyed Mohammad Hosseini Rostami

    (Electrical Engineering Department, Shiraz University of Technology, Shiraz, Fars 7155713876, Iran
    West Mazandaran Electric Power Distribution Company, Nowshahr, Mazandaran 4651739948, Iran)

  • Jin Wang

    (Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410004, China
    School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China)

  • Gwang-jun Kim

    (Computer Engineering Department, Chonnam National University, Gwangju 61186, Korea)

Abstract

Nowadays, by huge improvements in industrial control and the necessity of efficient energy consumption for buildings, unified managing systems are established to monitor and control mechanical equipment and energy usage. One of the main portions of the building management system (BMS) is the cooling and heating equipment called heating and ventilation and air-conditioning (HVAC). Based on temperature slow dynamic and presented uncertainty in modeling, a model predictive control (MPC) strategy to track both temperature and humidity is proposed in this study. The main goal of this study is to provide a framework to describe temperature and humidity elements required for dynamic modeling. Following that, by utilizing a predictive approach, a control strategy is obtained, which optimizes the tracking error of two interactional channel and performs the effort control by minimizing the optimization index. Other articles have mostly only had control over the temperature variable, but in our article, we tried to study the equations of temperature and humidity as well as their interference and according to the ASHRAE standard, both temperature and humidity controls must be accurate. The humidity was the novelty in our article. Simulation results proved the effectiveness of the proposed approach compared to the conventional proportional-integral controller. Evidently, the key idea behind the control objective is providing the comfort condition while consuming the least possible energy.

Suggested Citation

  • Pouria Bahramnia & Seyyed Mohammad Hosseini Rostami & Jin Wang & Gwang-jun Kim, 2019. "Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control," Energies, MDPI, vol. 12(24), pages 1-24, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4805-:d:298787
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    References listed on IDEAS

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    1. Homod, Raad Z., 2018. "Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings," Renewable Energy, Elsevier, vol. 126(C), pages 49-64.
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    Cited by:

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    2. Thyago Estrabis & Gabriel Gentil & Raymundo Cordero, 2021. "Development of a Resolver-to-Digital Converter Based on Second-Order Difference Generalized Predictive Control," Energies, MDPI, vol. 14(2), pages 1-22, January.
    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. Wang, Kung-Jeng & Lin, Chiuhsiang Joe & Dagne, Teshome Bekele & Woldegiorgis, Bereket Haile, 2022. "Bilayer stochastic optimization model for smart energy conservation systems," Energy, Elsevier, vol. 247(C).
    5. Katarzyna Gładyszewska-Fiedoruk & Tomasz Janusz Teleszewski, 2020. "Modeling of Humidity in Passenger Cars Equipped with Mechanical Ventilation," Energies, MDPI, vol. 13(11), pages 1-14, June.
    6. 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.
    7. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    8. Li, Hangxin & Wang, Shengwei, 2022. "Comparative assessment of alternative MPC strategies using real meteorological data and their enhancement for optimal utilization of flexibility-resources in buildings," Energy, Elsevier, vol. 244(PA).
    9. Ali Saberi Derakhtenjani & Andreas K. Athienitis, 2021. "Model Predictive Control Strategies to Activate the Energy Flexibility for Zones with Hydronic Radiant Systems," Energies, MDPI, vol. 14(4), pages 1-19, February.

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