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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
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    References listed on IDEAS

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    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.
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    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.

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