Yazar "Çakil, Fatih" seçeneğine göre listele

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    A Probabilistic Approach to Modelling Home Appliances for Demand Side Management
    (Institute of Electrical and Electronics Engineers Inc., 2023) Çakil, Fatih; Tekdemir, Ibrahim Gürsu
    Demand-Side Management is a set of various optimization techniques and/or strategies based on techno-economic factors which focuses on planning, implementation and monitoring in power systems. In this context, energy consumption behavior of residential users is focused on and electrical appliances are modelled in various studies in literature. Electrical energy usage patterns of residential consumers and consumption behaviors are intended to be modelled in this study. We conducted a survey for modelling electrical appliances in residential houses, findings of which form the basis for developing probabilistic models designed for demand-side management applications. After that, we applied Monte Carlo sampling method to make the statistical data and relevant probabilistic models enable a thorough probabilistic simulation. 300 virtual consumers are created by using this approach as part of the simulation and relevant outcomes are obtained finally. Besides that, a graphical user interface (GUI) is created in MATLAB to demonstrate results. It is concluded that results of the simulation carried out in this study are useful in demand side management context and they may be used for studying new dynamic price models or for testing some DSM functions in future. © 2023 IEEE.
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    Performance Analysis of Deterministic and Probabilistic Path Planning Algorithms in Complex Environments
    (Institute of Electrical and Electronics Engineers Inc., 2024) Aksoy, Necati; Çakil, Fatih; Tekdemir, Ibrahim Gürsu
    Path planning, in an other saying navigation, algorithms are vital in assorted applications, including robotics, autonomous vehicles, and drones. These algorithms can be broadly categorized into deterministic and probabilistic methods along with other branches. This study focuses and examines two classical deterministic path planning algorithms, A-star (A*) and Dijkstra's algorithm, alongside two prominent probabilistic path planning algorithms, Rapidly-exploring Random Trees Star (RRT*) and Probabilistic Roadmap (PRM). In the paper, with creating multi-level building interior floor maps and testing the performance of these four algorithms are performed on each level. Performance metrics included execution time, CPU usage, memory usage, and path distance. The results, presented in comparative tables, provide a comprehensive analysis of the efficiency and resource demands of each algorithm. Furthermore, this research offers valuable insights for selecting appropriate path planning algorithms in various autonomous navigation applications, guiding future implementations in robotics, autonomous electric vehicles, and drone technology. © 2024 IEEE.
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    Real-Time Smart Power Distribution in Electric Vehicle Chargers: Utilizing Rust Programming for Enhanced Efficiency
    (Institute of Electrical and Electronics Engineers Inc., 2024) Çakil, Fatih; Aksoy, Necati; Tekdemir, Ibrahim Gürsu
    The increase in the number of electric vehicles (EVs) and charging units generates a substantial power demand, necessitating the effective management of these loads. This study examines a model that employs the Priority-Based Power Distribution approach to prioritize EVs at charging stations based on real-time power demand. By leveraging the advantages of the Rust programming language, we developed and simulated a real-time model framework. This research introduces an innovative interface and design for controller systems, illustrating the critical role of Rust programming in optimizing EV charging station operations. The results underscore the efficiency and practicality of our proposed model in effectively managing dynamic power distribution. © 2024 IEEE.