Yazar "Akbulut, Leyla" seçeneğine göre listele

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    A Systematic Review of Building Energy Management Systems (BEMSs): Sensors, IoT, and AI Integration
    (Mdpi, 2025) Akbulut, Leyla; Tasdelen, Kubilay; Atilgan, Atilgan; Malinowski, Mateusz; Cosgun, Ahmet; Senol, Ramazan; Akbulut, Adem
    The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI-IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions.
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    Analysis of electrical distribution network voltage configuration with mixed integer linear programming algorithm and genetic algorithm I terms of energy cost
    (Istanbul Univ-Cerrahapasa, 2020) Akbulut, Leyla; Tezcan, Süleyman Sungur; Coşgun, Ahmet
    Since natural and social resources are not evenly distributed over the earth's surface, socioeconomic developments differ in time and space. Although the most important causes of inequality are natural or geographical reasons, the lack of energy supply-demand balance specific to the region causes inequality to increase. Undoubtedly, eliminating the supply-demand imbalance as a result of the increase in energy demand by costing energy cheaply will play a major role in reducing these differences. To meet the energy demand, the existing electricity grid may need to be expanded or partially or completely replaced. The aim of the studies to design a new electricity network or to expand an existing network; to meet the needs of consumers by providing energy distribution with minimum cost and maximum quality. In this study; energy costs generated by re-planning in a network that distributes electricity at different voltage levels to meet the increasing energy needs were analysed. To obtain the optimum network design; a minimization function was established by determining the required transformer powers and their numbers considering the physical and electrical conditions. The generated function was analysed by using a mixed-integer programming algorithm and genetic algorithm in MATLAB.
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    Analysis of electrical distribution network voltage configuration with mixed-integer linear programming algorithm and genetic algorithm regarding energy cost
    (Istanbul University vetdergi@istanbul.edu.tr, 2020) Akbulut, Leyla; Tezcan, Süleyman Sungur; Cosgun, Ahmet
    Because natural and social resources are unevenly distributed over the earth’s surface, socioeconomic developments differ in time and space. Although the most critical causes of inequality are natural or geographical reasons, the lack of energy supply-demand balance-specific to the region increases inequality. Undoubtedly, eliminating the supply-demand imbalance because of the increase in energy demand by costing energy cheaply will significantly reduce these differences. The existing electricity grid must be expanded or partially or completely replaced to meet the energy demand. This study aims to design a new electricity network or expand an existing network to meet consumers’ needs by providing energy distribution with minimum cost and maximum quality. In this study, we analyzed the energy costs generated by re-planning a network that distributes electricity at different voltage levels to meet the increasing energy needs. We established a minimization function by determining the required transformer powers and their numbers considering the physical and electrical conditions to obtain the optimum network design. We analyzed the generated function by using a mixed-integer programming algorithm and a genetic algorithm in MATLAB. © 2020 Istanbul University. All rights reserved.
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    DAĞITIM ŞEBEKESİ GERİLİM KONFİGÜRASYONUNUN KARIŞIK TAMSAYI LİNEER PROGRAMLAMA ALGORİTMASI İLE ENERJİ MALİYETİ YÖNÜNDEN ARAŞTIRILMASI
    (2019) Akbulut, Leyla; Tezcan, Suleyman Sungur; Çoşgun, Ahmet
    Bu makalede, farklı gerilim seviyelerinde çok kademeli dağıtım faaliyetisürdüren gerçek bir elektrik dağıtım şebekesinin artan enerji ihtiyacınıkarşılamak amacıyla yeniden planlanması enerji maliyeti açısından analizedilmiştir. Analiz çalışmasında optimum şebeke tasarımını elde etmek amacıylafiziksel ve elektriksel koşullar göz önünde bulundurularak ihtiyaç duyulantransformatör güçlerini ve sayılarını tespit ederek minimum maliyeti belirleyenbir minimizasyon fonksiyonu oluşturulmuştur. Oluşturulan fonksiyon belirlenenkısıt şartları içerisinde MATLAB yardımıyla karışık tamsayı programlamaalgoritmasından yararlanılarak çözümlenmiştir. Analiz sonuçlarındanyararlanılarak, ilgili şebekede üst seviyeli tek kademeli dağıtıma geçilmesi vegerilim seviyesinin mevcut durumda bırakılarak şebekenin yenidentasarlanması durumları mali açıdan karşılaştırılmıştır.
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    Economic Efficiency of Renewable Energy Investments in Photovoltaic Projects: A Regression Analysis
    (Mdpi, 2025) Akbulut, Adem; Niemiec, Marcin; Tasdelen, Kubilay; Akbulut, Leyla; Komorowska, Monika; Atilgan, Atilgan; Cosgun, Ahmet
    Energy Performance Contracts (EPC) are performance-based financing mechanisms designed to improve energy efficiency and support renewable energy adoption in the public sector. This study examines the economic efficiency of a 1710.72 kWp solar power plant (SPP), implemented under an EPC at Alanya Alaaddin Keykubat University, using a regression-based analysis. The model evaluates the effects of solar radiation, investment cost, and electricity sales price on unit production cost, and its predictions were compared with actual production data. Results show the system exceeded the EPC contract target by 16.2%, producing 2,423,472.28 kWh in its first year and preventing 1168.64 tons of CO2 emissions. The developed multiple linear regression model achieved a predictive error margin of 14.7%, confirming its validity. This study highlights the technical, economic, and environmental benefits of EPC applications in T & uuml;rkiye's public institutions and offers a practical decision-support framework for policymakers. The novelty lies in integrating a regression model with operational data and providing a comparative assessment of planned, predicted, and actual outcomes.
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    Review of Metaheuristic Algorithms for Energy Efficiency, Demand Side Management and Cost Estimation
    (Middle Pomeranian Sci Soc Env Prot, 2025) Akbulut, Leyla; Tasdelen, Kubilay; Cosgun, Ahmet
    This review study provides a comprehensive analysis of the application of metaheuristic algorithms in energy efficiency, demand-side management, and cost estimation. By systematically evaluating over 50 scientific studies published between 2020 and 2024, the paper classifies and analyzes the most frequently used algorithms, their advantages, and key application areas. The findings reveal that metaheuristic algorithms are most commonly applied in energy efficiency optimization (40%), cost reduction (37%), and load planning (23%). From a systems perspective, these algorithms are predominantly implemented in microgrids (27%), smart grids (25%), and power systems (18%). Among them, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO) emerge as the most frequently used due to their high performance in balancing energy demand, minimizing operational costs, and reducing carbon emissions. The analysis also shows that PSO-based models can reduce energy costs by up to 33%, while hybrid algorithms can increase the share of renewable energy use to over 50%. In demand-side management applications, certain algorithms effectively reduce peak loads and improve grid flexibility by dynamically adjusting consumption patterns. These results demonstrate that metaheuristic algorithms offer powerful tools for solving complex energy-related problems. The study contributes to the field by providing a structured, up-to-date literature mapping and highlighting opportunities for future research focused on sustainable and intelligent energy management.
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    Solar-Powered Biomass Revalorization for Pet Food and Compost: A Campus-Scale Eco-Circular System Based on Energy Performance Contracting
    (Mdpi, 2025) Akbulut, Leyla; Cosgun, Ahmet; Aldulaimi, Mohammed Hasan; Khafaji, Salwan Obaid Waheed; Atilgan, Atilgan; Kilic, Mehmet
    Integrating renewable energy with biomass valorization offers a scalable pathway toward circular and climate-resilient campus operations. This study presents a replicable model implemented at Alanya Alaaddin Keykubat University (ALKU, Turkiye), where post-consumer food waste from 30 cafeteria menus is converted into pet food and compost using a 150 L ECOAIR-150 thermal drying and grinding unit powered entirely by a 1.7 MW rooftop photovoltaic (PV) system. The PV infrastructure, established under Turkiye's first public-sector Energy Performance Contract (EPC), ensures zero-electricity-cost operation. On average, 260 kg of organic waste are processed monthly, yielding 180 kg of pet food and 50 kg of compost, with an energy demand of 1.6 kWh h(-1) and a conversion efficiency of 68.4%, resulting in approximately 17.5 t CO2 emissions avoided annually. Economic analysis indicates a monthly revenue of USD 55-65 and a payback period of similar to 36 months. Sensitivity analysis highlights the influence of input quality, seasonal waste composition, PV output variability, and operational continuity during academic breaks. Compared with similar initiatives in the literature, this model uniquely integrates EPC financing, renewable energy generation, and waste-to-product transformation within an academic setting, contributing directly to SDGs 7, 12, and 13.