Yazar "Unsal, Derya Betul" seçeneğine göre listele
Listeleniyor 1 - 5 / 5
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey(Mdpi, 2024) Unsal, Derya Betul; Aksoz, Ahmet; Oyucu, Saadin; Guerrero, Josep M.; Guler, MerveFossil fuels still have emerged as the predominant energy source for power generation on a global scale. In recent years, Turkey has experienced a notable decrease in the production of coal and natural gas energy, juxtaposed with a significant rise in the production of renewable energy sources. The study employed neural networks, ANNs (artificial neural networks), and LSTM (long short-term memory), as well as CNN (convolutional neural network) and hybrid CNN-LSTM designs, to assess Turkey's energy potential. Real-time outcomes were produced by integrating these models with meteorological data. The objective was to design strategies for enhancing performance by comparing various models of outcomes. The data collected for Turkey as a whole are based on average values. Machine learning approaches were employed to mitigate the error rate seen in the acquired outcomes. Comparisons were conducted across light gradient boosting machine (LightGBM), gradient boosting regressor (GBR), and random forest regressor (RF) techniques, which represent machine learning models, alongside deep learning models. Based on the findings of the comparative analyses, it was determined that the machine learning model, LightGBM, exhibited the most favorable performance in enhancing the accuracy of predictions. Conversely, the hybrid model, CNN-LSTM, had the greatest rate of inaccuracy. This study will serve as a guide for renewable energy researchers, especially in developing countries such as Turkey that have not switched to a smart grid system.Öğe Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning(MDPI, 2025) Dogan, Ferdi; Oyucu, Saadin; Unsal, Derya Betul; Aksoz, Ahmet; Vafaeipour, MajidThe real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing of supply and demand, and the optimization of online energy systems. This study examines the use of tree-based ensemble learning models for renewable energy production prediction, focusing on environmental factors such as temperature, pressure, and humidity. The study's primary contribution lies in demonstrating the effectiveness of the bagged trees model in reducing overfitting and achieving higher accuracy compared to other models, while maintaining computational efficiency. The results indicate that less sophisticated models are inadequate for accurately representing complex datasets. The results evaluate the effectiveness of machine learning methods in delivering valuable insights for energy sectors managing environmental conditions and predicting renewable energy sourcesÖğe Impact of Solar Cell Infrastructures on Energy Efficiency in Power Grid Integration(2024) Unsal, Derya BetulPhotovoltaic technology harvest electrical energy by stimulating liberated electrons within the semiconductor layers using solar radiation. Photovoltaic technology produces electrical energy by collecting electrons that are liberated in a semiconductor pn-junction by solar radiation. Photovoltaic solar cells have layered semiconductor structures and this study utilised for this objective. Current researches on energy storage with solar cells, focused to optimise the utilisation of the generated energy with cell efficiency. This study offers a thorough analysis of the energy efficiency of solar cells based on their infrastructures. The study involved obtaining computational visuals and doing efficiency verification. This was done by comparing the impact of different chemical structures on energy production. The MATLAB software was used with fixed parameters and varying efficiency. The results show that the Monocrystalline N-Type IBC model exhibits the maximum efficiency in terms of PV cell structure. The MIBC structure is more efficient than polycrystalline cells and also standard monotypes with high temperatures. This allows the cell to reflect itself and passivise the cell base, resulting in a 5% or more increase in energy production. Standard monotype cell has %16.2 efficiency and Monotype IBC has %20.1 efficiency results achieved with PVsyst and Matlab softwares. The results of the calculations were applied in real time and confirmed by testing the impact of structural differences on efficiency with real climate dataÖğe Medium Voltage and Low Voltage Applications of New Power Line Communication Model for Smart Grids(IEEE, 2016) Unsal, Derya Betul; Koc, Ahmet Hamdi; Yalcinoz, Tankut; Onaran, IbrahimConsidering the customer demand increase on electrical energy, it is obviously seen that the new system requirements such as providing instantaneous data flow, remote monitoring and controlling, would occur. These new system requirements should be adapted to existing electrical infrastructure with minimum cost. In this paper, the system control requirements and communication architectures of electrical grids are discussed. The possibility of the usage of the power line communication (PLC) for the smart grid system is examined. In order to improve communication quality of the smart grid, a new PLC system model is designed in MATLAB. Then Medium (MV) and Low Voltage (LV) applications are simulated using the MATLAB. The applicability of this new PLC system model to smart grid communication systems is discussed.Öğe Sürdürülebilir Akıllı Aydınlatma Sistemi Uygulaması: Sivas Cumhuriyet Üniversitesi(Sivas Cumhuriyet Üniversitesi, 2024) Unsal, Derya Betul; Özyalçın, Gülhan ŞahinTüm dünyada olduğu gibi Türkiye’de de hızla artan nüfus ve sanayileşmeden kaynaklanan enerji gereksinimi, ülkemizin kısıtlı kaynakları ile karşılanamamakta, enerji üretimi ve tüketimi arasındaki açık hızla büyümekte ve enerjide dışa bağımlılık oranımız artmaktadır. Enerji talebindeki hızlı artışın karşılanmasında yenilenebilir enerji kaynaklarından en etkin ve verimli biçimde yararlanılmadır. Bu çalışmada Sivas Cumhuriyet Üniversitesi kampüs alanı içerisinde bulunan cadde ve sokak aydınlatmalarının yenilenebilir enerjiden üretilen elektrik enerjisi ile gece boyu çalışması amaçlanmıştır. Kampüs aydınlatma elektrik planının envanteri çıkarılarak, aydınlatma besleme noktaları, aydınlatma mahallerindeki toplam güç, aydınlatma armatür çeşitliliği ve miktarı gibi veriler toplanmış, elde edilen sonuçlara uygun bir güneş enerjisi santrali modeli oluşturulmuştur.
