Yazar "Akbulut, Osman" seçeneğine göre listele

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    Enhancing Smart Grid Reliability Through Data-Driven Optimisation and Cyber-Resilient EV Integration
    (Mdpi, 2025) Cavus, Muhammed; Ayan, Huseyin; Sari, Mahmut; Akbulut, Osman; Dissanayake, Dilum; Bell, Margaret
    This study presents a novel cyber-resilient, data-driven optimisation framework for real-time energy management in electric vehicle (EV)-integrated smart grids. The proposed framework integrates a hybrid optimisation engine-combining genetic algorithms and reinforcement learning-with a real-time analytics module to enable adaptive scheduling under uncertainty. It accounts for dynamic electricity pricing, EV mobility patterns, and grid load fluctuations, dynamically reallocating charging demand in response to evolving grid conditions. Unlike existing GA/RL schedulers, this framework uniquely integrates adaptive optimisation with resilient forecasting under incomplete data and lightweight blockchain-inspired cyber-defence, thereby addressing efficiency, accuracy, and security simultaneously. To ensure secure and trustworthy EV-grid communication, a lightweight blockchain-inspired protocol is incorporated, supported by an intrusion detection system (IDS) for cyber-attack mitigation. Empirical evaluation using European smart grid datasets demonstrates a daily peak demand reduction of 9.6% (from 33 kWh to 29.8 kWh), with a 27% decrease in energy delivered at the original peak hour and a redistribution of demand that increases delivery at 19:00 h by nearly 25%. Station utilisation became more balanced, with weekly peak normalised utilisation falling from 1.0 to 0.7. The forecasting module achieved a mean absolute error (MAE) of 0.25 kWh and a mean absolute percentage error (MAPE) below 20% even with up to 25% missing data. Among tested models, CatBoost outperformed LightGBM and XGBoost with an RMSE of 0.853 kWh and R2 of 0.416. The IDS achieved 94.1% accuracy, an AUC of 0.97, and detected attacks within 50-300 ms, maintaining over 74% detection accuracy under 50% novel attack scenarios. The optimisation runtime remained below 0.4 s even at five times the nominal dataset scale. Additionally, the study outlines a conceptual extension to support location-based planning of charging infrastructure. This proposes the alignment of infrastructure roll-out with forecasted demand to enhance spatial deployment efficiency. While not implemented in the current framework, this forward-looking integration highlights opportunities for synchronising infrastructure development with dynamic usage patterns. Collectively, the findings confirm that the proposed approach is technically robust, operationally feasible, and adaptable to the evolving demands of intelligent EV-smart grid systems.
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    How Do People Perceive Bundling? An Experiment
    (Assoc Computing Machinery, 2025) Wallinger, Markus; Akbulut, Osman; Rufai, Kabir Ahmed; Purchase, Helen C.; Archambault, Daniel
    We present an exploratory study on how people perceive visualizations of spatial social networks generated by edge bundling algorithms. Although these algorithms successfully minimize clutter in node-link diagrams, they do so through various methods that can sometimes create false connections between nodes. We conducted a qualitative experiment involving participants with technical expertise but no prior knowledge of edge bundling algorithms. Participants described their perceptions of both bundled and straight-line visualizations in open-ended tasks. Analysis of their annotations and transcripts revealed a general preference for bundled visualizations. However, when it came to false connections, participants tended to follow them in tightly bundled diagrams while also vocalizing that these drawings were more ambiguous. The routing of bundles influenced the perception of clusters and participants assigned more or fewer nodes to the clusters, depending on the routing of bundles. Participants' unfamiliarity with the dataset led them to use analogies to describe the bundled drawings, potentially adding perceived semantic meaning to the data.
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    Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques
    (Mdpi, 2024) Akbulut, Osman; Cavus, Muhammed; Cengiz, Mehmet; Allahham, Adib; Giaouris, Damian; Forshaw, Matthew
    Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.
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    Visualizing ordered bivariate data on node-link diagrams
    (Elsevier, 2023) Akbulut, Osman; Mclaughlin, Lucy; Xin, Tong; Forshaw, Matthew; Holliman, Nicolas S.
    Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor. However, existing visualization methods are not always effective and efficient in representing bivariate graph-based data. This study proposes a novel node-link visual model - visual entropy (Vizent) graph - to effectively represent both primary and secondary values, such as uncertainty, on the edges simultaneously. We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static nodelink diagrams. In the first experiment, we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy. Three static visual encodings that use two visual cues were selected from the literature for comparison: Width-Lightness, Saturation-Transparency, and Numerical values. We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks. The participants achieved higher accuracy of their responses using Vizent and Numerical values; however, both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks. Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy. The performance of the Vizent graph was then compared to the Numerical values visualization. The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented, while no significant difference in accuracy was found. The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.(c) 2023 The Authors. Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University (http://creativecommons.org/licenses/by/4.0/).