Energy Systems Engineering / Enerji Sistemleri Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/4752

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Subject: search.filters.subject.Fuzzy logic

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  • Article
    Citation - WoS: 33
    Citation - Scopus: 41
    Development of a Personalized Thermal Comfort Driven Controller for Hvac Systems
    (Elsevier Ltd., 2021) Turhan, Cihan; Simani, Silvio; Gökçen Akkurt, Gülden
    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.
  • Article
    Citation - WoS: 7
    Citation - Scopus: 8
    Performance Indices of Soft Computing Models To Predict the Heat Load of Buildings in Terms of Architectural Indicators
    (Yıldız Teknik Üniversitesi, 2017) Turhan, Cihan; Kazanasmaz, Zehra Tuğçe; Gökçen Akkurt, Gülden
    This study estimates the heat load of buildings in Izmir/Turkey by three soft computing (SC) methods; Artificial Neural Networks (ANNs), Fuzzy Logic (FL) and Adaptive Neuro-based Fuzzy Inference System (ANFIS) and compares their prediction indices. Obtaining knowledge about what the heat load of buildings would be in architectural design stage is necessary to forecast the building performance and take precautions against any possible failure. The best accuracy and prediction power of novel soft computing techniques would assist the practical way of this process. For this purpose, four inputs, namely, wall overall heat transfer coefficient, building area/ volume ratio, total external surface area and total window area/total external surface area ratio were employed in each model of this study. The predicted heat load is evaluated comparatively using simulation outputs. The ANN model estimated the heat load of the case apartments with a rate of 97.7% and the MAPE of 5.06%; while these ratios are 98.6% and 3.56% in Mamdani fuzzy inference systems (FL); 99.0% and 2.43% in ANFIS. When these values were compared, it was found that the ANFIS model has become the best learning technique among the others and can be applicable in building energy performance studies.