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Publication Index: WoSSubject: Deep Learning

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Now showing 1 - 4 of 4
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    Conference Object
    Citation - WoS: 1
    An Undergraduate Curriculum for Deep Learning
    (Ieee, 2018) Tirkes, Guzin; Ekin, Cansu Cigdem; Sengul, Gokhan; Bostan, Atila; Karakaya, Murat
    Deep Learning (DL) is an interesting and rapidly developing field of research which has been currently utilized as a part of industry and in many disciplines to address a wide range of problems, from image classification, computer vision, video games, bioinformatics, and handwriting recognition to machine translation. The starting point of this study is the recognition of a big gap between the sector need of specialists in DL technology and the lack of sufficient education provided by the universities. Higher education institutions are the best environment to provide this expertise to the students. However, currently most universities do not provide specifically designed DL courses to their students. Thus, the main objective of this study is to design a novel curriculum including two courses to facilitate teaching and learning of DL topic. The proposed curriculum will enable students to solve real-world problems by applying DL approaches and gain necessary background to adapt their knowledge to more advanced, industry-specific fields.
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    Conference Object
    Citation - WoS: 1
    Parking Space Occupancy Detection Using Deep Learning Methods
    (Ieee, 2018) Akinci, Fatih Can; Karakaya, Murat
    This paper presents an approach for gathering information about the availabilty of the parking lots using Convoltional Neural Network (CNN) for image processing running on an embedded system. By using an efiicent neural network model, we made it possible to use a very low cost embedded system compared to the ones used in previous works on this topic. This efficient model's performance is compared to one of the models that proved its accuracy in image classification competitions. In these tests, we used datasets that has thousands of different images taken from parking lots in different light and weather conditions.
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    Article
    Citation - WoS: 2
    Citation - Scopus: 4
    Precision Forecasting for Hybrid Energy Systems Using Five Deep Learning Algorithms for Meteorological Parameter Prediction
    (Elsevier Sci Ltd, 2025) Ceylan, Ceren; Yumurtaci, Zehra
    The intermittent nature of renewable energy sources necessitates accurate power production forecasting to ensure system sustainability and balance between energy supply and demand. Although the deep learning-based meteorological forecasting is significantly studied in literature, most of the current literature applies single-algorithm based on each individual energy source and less multi-algorithm based on comparative studies on multiple architectures as applied to integrated hybrid systems. In addition, most of the research uses the same algorithmic solution to all the meteorological parameters without identifying parameter-specific optimization potential, and recent research is verified on actual future time steps instead of historical train-test split. This study presents a comprehensive comparative analysis of five deep learning algorithms, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and CNN-LSTM hybrid, for forecasting critical meteorological parameters (wind speed, ambient temperature, and solar radiation) that determine energy output in a wind and solar-based hybrid energy system (HES). Using five years of Istanbul meteorological data (2018-2022), optimal algorithms were systematically identified for each parameter through rigorous hyperparameter optimization and cross-validation. Key results demonstrate that GRU achieves superior performance in wind speed prediction (RMSE: 0.049 m/s, R2: 0.8634) and solar radiation forecasting (RMSE: 0.146 W/m2, R2: 0.6643), while CNN-LSTM excels in ambient temperature prediction (RMSE: 0.011 degrees C, R2: 0.9976). The integrated approach predicted annual hybrid system energy production with 89 % accuracy, demonstrating 0.48 % deviation from observed values. Most significantly, our framework successfully forecasted sixth year (2023) energy production with 1.55 % error, validating its real-world applicability. This research contributes to the methodological advancement of renewable energy forecasting by systematically identifying optimal algorithmic approaches for different meteorological parameters in hybrid systems, thereby supporting the integration of intermittent renewable sources into sustainable energy infrastructures.
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    Article
    Univariate Deep Learning Models for Short-Term Electricity Load Forecasting from Renewables
    (Ankara Univ, Fac Sci, 2025) Kabran, Fatma Basoglu; Unlu, Kamil Demirberk
    Renewable energy offers a cost-effective, carbon-free solution for energy needs, while protecting the environment. Accurate forecasting of electricity generation from renewable sources is crucial for the efficiency of modern power grids. This study employs a univariate deep learning approach to predict daily renewable energy generation, evaluating Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) as candidate models. Five performance metrics-mean absolute error, root mean squared error, mean absolute percentage error, mean absolute scaled error and the coefficient of determination-are employed to assess the forecasting power of the algorithms. The empirical results show that CNN outperforms other models, achieving an R2 of almost 94%. This research shows that the univariate model based on historical data of electricity load generated from renewables can accurately predict day-ahead electricity load, even without meteorological data.