Yazar "Kurucan, Mehmet" seçeneğine göre listele

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    A Modular Hybrid SOC-Estimation Framework with a Supervisor for Battery Management Systems Supporting Renewable Energy Integration in Smart Buildings
    (MDPI, 2025) Kurucan, Mehmet; Michailidis, Panagiotis; Michailidis, Iakovos; Minelli, Federico
    Accurate state-of-charge (SOC) estimation is crucial in smart-building energy management systems, where rooftop photovoltaics and lithium-ion energy storage systems must be coordinated to align renewable generation with real-time demand. This paper introduces a novel, modular hybrid framework for SOC estimation, which synergistically combines the predictive power of artificial neural networks (ANNs), the logical consistency of finite state automata (FSA), and an adaptive dynamic supervisor layer. Three distinct ANN architectures-feedforward neural network (FFNN), long short-term memory (LSTM), and 1D convolutional neural network (1D-CNN)-are employed to extract comprehensive temporal and spatial features from raw data. The inherent challenge of ANNs producing physically irrational SOC values is handled by processing their raw predictions through an FSA module, which constrains physical validity by applying feasible transitions and domain constraints based on battery operational states. To further enhance the adaptability and robustness of the framework, two advanced supervisor mechanisms are developed for model selection during estimation. A lightweight rule-based supervisor picks a model transparently using recent performance scores and quick signal heuristics, whereas a more advanced double deep Q-network (DQN) reinforcement-learning supervisor continuously learns from reward feedback to adaptively choose the model that minimizes SOC error under changing conditions. This RL agent dynamically selects the most suitable ANN+FSA model, significantly improving performance under varying and unpredictable operational conditions. Comprehensive experimental validation demonstrates that the hybrid approach consistently outperforms raw ANN predictions and conventional extended Kalman filter (EKF)-based methods. Notably, the RL-based supervisor exhibits good adaptability and achieves lower error results in challenging high-variance scenarios.
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    An improved roosters algorithm for constrained 3D UAV path planning in urban environments
    (Nature Portfolio, 2025) Gencal, Mashar Cenk; Ata, Baris; Kurucan, Mehmet; Kilinc, Emre
    Urban environments impose complex challenges for the navigation of unmanned aerial vehicles (UAVs), including dense obstacles, no-fly zones, energy constraints, and regulatory restrictions. Addressing these challenges requires efficient and robust optimization techniques. This study introduces the Improved Roosters Algorithm (IRA), a novel metaheuristic inspired by the natural dominance behavior of roosters, tailored for constrained 3D UAV path planning in urban scenarios. Unlike existing metaheuristics, IRA introduces a spiral dancing operator, adaptive constraint handling, and a hierarchical population structure. These innovations directly target the lack of adaptive mechanisms in constraint-rich urban environments, enabling more reliable and realistic UAV path planning. The performance of IRA is benchmarked against Particle Swarm Optimization (PSO), Standard Genetic Algorithm (SGA), Differential Evolution (DE), Grey Wolf Optimizer (GWO) and the original Roosters Algorithm (RA) across three increasingly complex simulation scenarios. Experimental results demonstrate that IRA consistently outperforms the baseline methods in terms of feasibility and optimality, validating its potential as a competitive tool for UAV mission planning in realistic urban environments.
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    Machine Learning for Energy Management in Buildings: A Systematic Review on Real-World Applications
    (MDPI, 2025) Michailidis, Panagiotis; Minelli, Federico; Michailidis, Iakovos; Kurucan, Mehmet; Coban, Hasan Huseyin; Kosmatopoulos, Elias
    Machine learning (ML) is becoming a key enabler in building energy management systems (BEMS), yet most existing reviews focus on simulations and fail to reflect the realities of real-world deployment. In response to this limitation, the present work aims to present a systematic review dedicated entirely to experimental, field-tested applications of ML in BEMS, covering systems such as Heating, Ventilation & Air-conditioning (HVAC), Renewable Energy Systems (RES), Energy Storage Systems (ESS), Ground Heat Pumps (GHP), Domestic Hot Water (DHW), Electric Vehicle Charging (EVCS), and Lighting Systems (LS). A total of 73 real-world deployments are analyzed, featuring techniques like Model Predictive Control (MPC), Artificial Neural Networks (ANNs), Reinforcement Learning (RL), Fuzzy Logic Control (FLC), metaheuristics, and hybrid approaches. In order to cover both methodological and practical aspects, and properly identify trends and potential challenges in the field, current review uses a unified framework: On the methodological side, it examines key-attributes such as algorithm design, agent architectures, data requirements, baselines, and performance metrics. From a practical standpoint, the study focuses on building typologies, deployment architectures, zones scalability, climate, location, and experimental duration. In this context, the current effort offers a holistic overview of the scientific landscape, outlining key trends and challenges in real-world machine learning applications for BEMS research. By focusing exclusively on real-world implementations, this study offers an evidence-based understanding of the strengths, limitations, and future potential of ML in building energy control-providing actionable insights for researchers, practitioners, and policymakers working toward smarter, grid-responsive buildings. Findings reveal a maturing field with clear trends: MPC remains the most deployment-ready, ANNs provide efficient forecasting capabilities, RL is gaining traction through safer offline-online learning strategies, FLC offers simplicity and interpretability, and hybrid methods show strong performance in multi-energy setups.