Yazar "Tolun, Omer Can" seçeneğine göre listele

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    A comprehensive benchmark of machine learning-based algorithms for medium-term electric vehicle charging demand prediction
    (Springer, 2025) Tolun, Omer Can; Zor, Kasim; Tutsoy, Onder
    The current difficulties faced by evolutionary smart grids, as well as the widespread electric vehicles (EVs) into the modernised electric power system, highlight the crucial balance between electricity generation and consumption. Focusing on renewable energy sources instead of fossil fuels can provide an enduring environment for future generations by mitigating the impacts of global warming. At this time, the popularity of EVs has been ascending day by day due to the fact that they have several advantages such as being environmentally friendly and having better mileage performance in city driving over conventional vehicles. Despite the merits of the EVs, there are also a few disadvantages consisting of the integration of the EVs into the existing infrastructure and their expensiveness by means of initial investment cost. In addition to those, machine learning (ML)-based techniques are usually employed in the EVs for battery management systems, drive performance, and passenger safety. This paper aims to implement an EV monthly charging demand prediction by using a novel technique based on an ensemble of Pearson correlation (PC) and analysis of variance (ANOVA) along with statistical and ML-based algorithms including seasonal auto-regressive integrated moving average with exogenous variables (SARIMAX), convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) decision trees, gated recurrent unit (GRU) networks, long short-term memory (LSTM) networks, bidirectional LSTM (Bi-LSTM) and GRU (Bi-GRU) networks for the Eastern Mediterranean Region of T & uuml;rkiye. The performance and error metrics, including determination coefficient (R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} ), mean absolute percentage error (MAPE), mean absolute error (MAE), and mean absolute scaled error (MASE), are evaluated in a benchmarking manner. According to the obtained results, in Scenario 1, a hybrid of PC and XGBoost decision trees model achieved an R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} of 96.21%, MAPE of 5.52%, MAE of 6.5, and MASE of 0.195 with a training time of 2.08 s and a testing time of 0.016 s. In Scenario 2, a combination of ANOVA and XGBoost decision trees model demonstrated an R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} of 96.83%, a MAPE of 5.29%, a MAE of 6.0, and a MASE of 0.180 with a training time of 1.62 s and a testing time of 0.012 s. These findings highlight the superior accuracy and computational efficiency of the XGBoost models for both scenarios compared to others and reveal XGBoost's suitability for EV charging demand prediction.
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    Öğe
    Estimation of DC Motor Parameters Using Least Square-based Optimization Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2023) Tolun, Omer Can; Tutsoy, Önder
    In daily life, Direct Current (DC) motors are employed in virtually all applications due to their ease of operation, simple construction, and affordability. Therefore, a proper mathematical model for DC motors is essential for developing model-based controllers and predicting system responses. In this paper, the Least Square-based Autoregressive-eXogenous (LS-ARX) optimization algorithm has been developed to estimate unknown parameters of the DC motor model. Furthermore, the unknown parameters of the motor have been estimated by using the Nonlinear Least Square (NLS) and Pattern Search (PS) methods from the MATLAB optimization toolbox. For the purpose of estimating the DC motor parameters, an experimental setup has been constructed to measure the angular velocity of the motor utilizing an Arduino and two different sensors (photon interrupter and hall-effect sensors). To transfer data from the Arduino program to the MATLAB environment, a serial connection has been established between the Arduino and the Python program. Utilizing the proposed algorithm and the MATLAB optimization toolbox, the accuracy of the estimation process has been evaluated and compared through coefficient of determination. As a result of the comparison, it can be observed that the LS-ARX optimization algorithm is extremely robust, and the unknown parameters of the DC motor have been estimated with a high degree of accuracy. © 2023 IEEE.
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    Öğe
    Modelling and performance-based PD controller of the electric autonomous vehicles with the environmental uncertainties
    (Academic Publication Council, 2023) Tolun, Omer Can; Tutsoy, Önder
    The popularity of Electric Autonomous Vehicles (EAVs) is rising continuously because they offer lower emissions, less energy consumption, safer, and more comfortable driving technologies. In this paper, a dynamic model of the EAV is derived, and analysed extensively in terms of the corresponding stability region for the linear and nonlinear EAV with a DC motor. Considering the dynamic environment, there are various uncertainties such as the puddles, bumps, and roundabout turnings that the vehicles must interact in real life applications. In order to model such uncertainties, the motions of the EAV have been analysed through observing the responses of the vehicles in the dynamic environments and then physical laws are utilized to accurately model them. Finally, a performance-based Proportional Derivative (PD) controller, specified with the desired maximum overshoot, settling time, rising time is constructed to handle such environmental uncertainties. To justify the developed model and controller, the corresponding motion, stability region and control of the EAV results obtained in the simulation environment are analysed comprehensively.