IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i1p67-d87174.html
   My bibliography  Save this article

Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model

Author

Listed:
  • Cihan Turhan

    (Mechanical Engineering, Izmir Institute of Technology, Gulbahce Campus, Urla, 35430 Izmir, Turkey)

  • Silvio Simani

    (Dipartimento di Ingegneria, Università degli Studi di Ferrara. Via Saragat 1E, 44122 Ferrara (FE), Italy)

  • Ivan Zajic

    (Control Theory and Applications Centre, Coventry University, Coventry CV1 5FB, UK)

  • Gulden Gokcen Akkurt

    (Energy Engineering Program, Izmir Institute of Technology, Gulbahce Campus, Urla, 35430 Izmir, Turkey)

Abstract

The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data-driven grey-box identification approach provided the physically–meaningful nonlinear continuous-time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important, since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte Carlo (MC) analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application of the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model-based and data-driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.

Suggested Citation

  • Cihan Turhan & Silvio Simani & Ivan Zajic & Gulden Gokcen Akkurt, 2017. "Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model," Energies, MDPI, vol. 10(1), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:1:p:67-:d:87174
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/1/67/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/1/67/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David Prentice, 2016. "From the Editor," Economic Papers, The Economic Society of Australia, vol. 35(1), pages 1-1, March.
    2. Dounis, A.I. & Caraiscos, C., 2009. "Advanced control systems engineering for energy and comfort management in a building environment--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1246-1261, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qingwei Miao & Shijun You & Wandong Zheng & Xuejing Zheng & Huan Zhang & Yaran Wang, 2017. "A Grey-Box Dynamic Model of Plate Heat Exchangers Used in an Urban Heating System," Energies, MDPI, vol. 10(9), pages 1-16, September.
    2. Jie Song & Xin Pan & Chao Lu & Hanchen Xu, 2017. "A Simulation-Based Optimization Method for Hybrid Frequency Regulation System Configuration," Energies, MDPI, vol. 10(9), pages 1-14, August.
    3. Umar Javed & Khalid Ijaz & Muhammad Jawad & Ejaz A. Ansari & Noman Shabbir & Lauri Kütt & Oleksandr Husev, 2021. "Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis," Energies, MDPI, vol. 14(17), pages 1-22, September.

    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. Muhammad Fayaz & DoHyeun Kim, 2018. "Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic," Energies, MDPI, vol. 11(1), pages 1-22, January.
    2. Murat Kunelbayev & Yedilkhan Amirgaliyev & Talgat Sundetov, 2022. "Improving the Efficiency of Environmental Temperature Control in Homes and Buildings," Energies, MDPI, vol. 15(23), pages 1-15, November.
    3. Bhowmik, Chiranjib & Bhowmik, Sumit & Ray, Amitava & Pandey, Krishna Murari, 2017. "Optimal green energy planning for sustainable development: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 796-813.
    4. Shunling Ruan & Haiyan Xie & Song Jiang, 2017. "Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization," Sustainability, MDPI, vol. 9(9), pages 1-22, September.
    5. Bridget Laging & Amanda Kenny & Michael Bauer & Rhonda Nay, 2018. "Recognition and assessment of resident’ deterioration in the nursing home setting: A critical ethnography," Journal of Clinical Nursing, John Wiley & Sons, vol. 27(7-8), pages 1452-1463, April.
    6. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    7. De Lorenzi, Andrea & Gambarotta, Agostino & Morini, Mirko & Rossi, Michele & Saletti, Costanza, 2020. "Setup and testing of smart controllers for small-scale district heating networks: An integrated framework," Energy, Elsevier, vol. 205(C).
    8. Manzano-Agugliaro, Francisco & Montoya, Francisco G. & Sabio-Ortega, Andrés & García-Cruz, Amós, 2015. "Review of bioclimatic architecture strategies for achieving thermal comfort," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 736-755.
    9. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    10. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
    11. Antonio Paone & Jean-Philippe Bacher, 2018. "The Impact of Building Occupant Behavior on Energy Efficiency and Methods to Influence It: A Review of the State of the Art," Energies, MDPI, vol. 11(4), pages 1-19, April.
    12. Altwies, Joy E. & Nemet, Gregory F., 2013. "Innovation in the U.S. building sector: An assessment of patent citations in building energy control technology," Energy Policy, Elsevier, vol. 52(C), pages 819-831.
    13. Juan-Carlos Fraile & Julio San-José & Ana González-Alonso, 2014. "A Boiler Room in a 600-Bed Hospital Complex: Study, Analysis, and Implementation of Energy Efficiency Improvements," Energies, MDPI, vol. 7(5), pages 1-22, May.
    14. Jiang, Lai & Yao, Runming & Liu, Kecheng & McCrindle, Rachel, 2017. "An Epistemic-Deontic-Axiologic (EDA) agent-based energy management system in office buildings," Applied Energy, Elsevier, vol. 205(C), pages 440-452.
    15. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    16. Wang, Zhu & Wang, Lingfeng & Dounis, Anastasios I. & Yang, Rui, 2012. "Multi-agent control system with information fusion based comfort model for smart buildings," Applied Energy, Elsevier, vol. 99(C), pages 247-254.
    17. Azar, Elie & Nikolopoulou, Christina & Papadopoulos, Sokratis, 2016. "Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling," Applied Energy, Elsevier, vol. 183(C), pages 926-937.
    18. Ghadi, Yazeed Yasin & Rasul, M.G. & Khan, M.M.K., 2016. "Design and development of advanced fuzzy logic controllers in smart buildings for institutional buildings in subtropical Queensland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 738-744.
    19. Byung-Ki Jeon & Eui-Jong Kim, 2021. "LSTM-Based Model Predictive Control for Optimal Temperature Set-Point Planning," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    20. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.

    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:jeners:v:10:y:2017:i:1:p:67-:d:87174. 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.