Yazar "Oktem, Irfan" seçeneğine göre listele

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    Designing and performance analysis of solar tracker system: A case study of Çukurova region
    (Elsevier, 2021) Alici, Hakan; Esenboga, Burak; Oktem, Irfan; Demirdelen, Tugce; Tumay, Mehmet
    Today, solar energy has an important place among the renewable energy sources in the world due to its high energy potential. Therefore the share of solar energy is gradually increasing in electricity generation. It is difficult to get maximum efficiency from the solar photovoltaic (PV) panels installed permanently because they can’t benefit from solar energy continuously. Therefore one of the most effective ways to prevent loss of energy efficiency is the solar tracking systems that provide up to 40% efficiency. In this study, it is aimed to increase the efficiency of solar PV plants by following the sun throughout the day and to maximize the power produced by solar PV panels by exposing it to more light. Therefore a single-axis passive-controlled solar tracker system design is recommended for 42, 000 kWp solar PV power plants in the Çukurova region. The efficiency effect of the proposed tracker system for high-power solar PV plants is examined. By comparing the performance analysis of the proposed solar PV system having a solar tracker system with a fixed angle solar PV system, the effectiveness of the proposed system is revealed for high-power solar PV plants. © 2021 Elsevier Inc. All rights reserved.
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    Development of an IoT-supported predictive maintenance system for agricultural irrigation pumps: A machine learning approach
    (SAGE Publications Ltd, 2025) Aksu, Inayet Ozge; Oktem, Irfan; Demirdelen, Tugce
    Periodic maintenance of irrigation pumps used in rural areas is costly and time consuming. By obtaining real-time information, potential problems can be detected in advance. The aim of this study is to instantly monitor the performance of the pump installed for agricultural irrigation using the Internet of Things (IoT) and to apply regression models to the system data and to determine optimum parameter values for the models with genetic algorithm. First, the automation system was installed and the temperature, humidity, current, voltage and pump vibration data were transferred to the server named Thingspeak and the data were visualized in the application created with the Qt user interface creation toolkit. Faults occurring in the system were instantly notified to the user through this application. Predictive maintenance of the system consists of a two-stage process in the field of machine learning. In the first stage, linear regression, polynomial regression, random forest regression (RFR) and support vector regression (SVR) models were evaluated. In the second stage, the genetic algorithm was used in hybrid structure to determine the optimal parameters of the polynomial regression, SVR and RFR models. Improvement of model parameters for the irrigation pump has been achieved using genetic algorithm. As a result, evolutionary hybrid random forest regression model was found to be the most appropriate model for the dataset.