Energy Systems Engineering / Enerji Sistemleri Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/4752
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Article Citation - WoS: 33Citation - Scopus: 41Development of a Personalized Thermal Comfort Driven Controller for Hvac Systems(Elsevier Ltd., 2021) Turhan, Cihan; Simani, Silvio; Gökçen Akkurt, GüldenIncreasing 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.Conference Object Citation - WoS: 1Citation - Scopus: 2Analysis and Application of Advanced Control Strategies To a Heating Element Nonlinear Model(IOP Publishing Ltd., 2017) Turhan, Cihan; Simani, Silvio; Zajic, Ivan; Gökçen Akkurt, GüldenThis paper presents the design of different control strategies applied to a heating element nonlinear model. The description of this heating element was obtained exploiting a data-driven and physically meaningful nonlinear continuous-time model, which represents a test-bed used in passive air conditioning for sustainable housing applications. This model has low complexity while achieving high simulation performance. The physical meaningfulness of the model provides an enhanced insight into the performance and functionality of the system. In return, this information can be used during the system simulation and improved model- based and data-driven control designs for tight temperature regulation. The main purpose of this study is thus to give several examples of viable and practical designs of control schemes with application to this heating element model. Moreover, extensive simulations and Monte- Carlo analysis are the tools for assessing experimentally the main features of the proposed control schemes, in the presence of modelling and measurement errors. These developed control methods are also compared in order to evaluate advantages and drawbacks of the considered solutions. Finally, the exploited simulation tools can serve to highlight the potential application of the proposed control strategies to real air conditioning systems.Article Citation - WoS: 6Citation - Scopus: 9Performance Analysis of Data-Driven and Model-Based Control Strategies Applied To a Thermal Unit Model(MDPI Multidisciplinary Digital Publishing Institute, 2017) Turhan, Cihan; Simani, Silvio; Zajic, Ivan; Gökçen Akkurt, GüldenThe paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinearmodel 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.