4.7 Article

Development of a personalized thermal comfort driven controller for HVAC systems

期刊

ENERGY
卷 237, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.121568

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HVAC systems; personalised thermal comfort; HVAC control; Fuzzy Logic; energy consumption

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The study aims to improve thermal comfort and energy savings in HVAC systems by developing a thermal comfort-driven control algorithm, which was tested in a real building and shown to increase thermal comfort satisfaction by 92% and reduce energy consumption by 13.2% compared to a conventional PID controller. This prototype is cost-effective and easily deployable in real environments, distinguishing it from other existing works in the field.
Increasing thermal comfort and reducing energy consumption are two main objectives of advanced HVAC control systems. In this study, a thermal comfort driven control (PTC-DC) algorithm was developed to improve HVAC control systems with no need of retrofitting HVAC system components. A case building located in Izmir Institute of Technology Campus-Izmir-Turkey was selected to test the developed system. First, wireless sensors were installed to the building and a mobile application was developed to monitor/ collect temperature, relative humidity and thermal comfort data of an occupant. Then, the PTC-DC algorithm was developed to meet the highest occupant thermal comfort as well as saving energy. The prototypes of the controller were tested on the case building from July 3rd, 2017 to November 1st, 2018 and compared with a conventional PID controller. The results showed that the developed control algorithm and conventional controller satisfy neutral thermal comfort for 92 % and 6 % of total measurement days, respectively. From energy consumption point of view, the PTC-DC decreased energy consumption by 13.2 % compared to the conventional controller. Consequently, the PTC-DC differs from other works in the literature that the prototype of PTC-DC can be easily deployed in real environments. Moreover, the PTC-DC is low-cost and user-friendly. (c) 2021 Elsevier Ltd. All rights reserved.

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