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Research Article

Model-based adaptive controller for personalized ventilation and thermal comfort in naturally ventilated spaces

Dalia Ghaddar1, Mariam Itani1,2, Nesreen Ghaddar1( ), Kamel Ghali1, Joseph Zeaiter3
Mechanical Engineering Department, American University of Beirut, P.O. Box 11-0236, Beirut 1107-2020, Lebanon
Mechanical Engineering Department, Phoenicia University, District of Zahrani, Lebanon
Bahaa and Walid Bassatne Department of Chemical Engineering and Advanced Energy, American University of Beirut, Beirut, Lebanon
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Abstract

This work develops a standalone autonomously controlled personalized ventilation (PV) unit in a naturally ventilated (NV) office space to maintain acceptable thermal comfort (TC) under steady and transient indoor conditions and activity levels. The NV-PV proportional integral derivative (PID) controller adjusts the PV supply temperature (TSPV) at the occupant set flow rate (QSPV) based on predicted TC using a regression model. The target TC level that the controller attains at all times is between 0 (neutral) and 1 (slightly comfortable). Process transfer functions were developed and then used to find the adaptive PID tuning coefficients using the Internal Model Control (IMC) method. The controller was tested in a case study at indoor temperature range of 25 to 33 °C with relative humidity range of 55% and 80%. It was shown that the NV-PV controller adjusted TSPV to maintain acceptable TC under transients of indoor conditions and metabolic rates.

Keywords

natural ventilationthermal comfortpersonalized ventilationadaptive PID controller

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Building Simulation
Volume 14 Issue 6,
December 2021
Pages 1757-1771
DOI: 10.1007/s12273-021-0783-x
Cite this article:
Ghaddar D, Itani M, Ghaddar N, et al. Model-based adaptive controller for personalized ventilation and thermal comfort in naturally ventilated spaces. Building Simulation, 2021, 14(6): 1757-1771. https://doi.org/10.1007/s12273-021-0783-x

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Received: 31 October 2020
Revised: 02 February 2021
Accepted: 13 February 2021
Published: 24 March 2021
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
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