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Article Citation - WoS: 1Citation - Scopus: 1Wavelet-Enhanced Sequence-To Modeling With Attention Mechanism for Short-Term Wind Power Forecasting(Taylor & Francis inc, 2025) Karaca, Burak; Unlu, Kamil Demirberk; Turkan, SemraElectricity load forecasting is crucial to managing electric systems, especially loads produced from renewable energy sources since the load from renewable energy sources varies when compared with nonrenewable sources. Turkey is producing an increasing amount of electricity from wind energy every day. The aim of this study is to introduce a hybrid deep learning model based on sequence-to-sequence learning (seq-2-seq), attention mechanisms, and wavelet transformation. Long Short-Term Memory (LSTM), Gated Recurrent Unit, and Bidirectional Long Short-Term Memory (BiLSTM) are used as decoders and encoders in the seq-2-seq model. We proposed six different models. All models are univariate type, requiring only the data itself. The model can be used on any wind farms without requiring the meteorological data. We test the proposed model on four different wind farms in Turkey: Soma, Biga, Balikesir, and Mersin. We utilize four different performance metrics to test the model's performance: mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determinations (R2). The best model is seen as Wavelet-Seq2Seq-BiLSTM-LSTM at Biga Wind Farm, which achieved the best performance with a MAE of 0.127, an MSE of 0.001, a MAPE of 0.28, and an R2 of 0.997.Article Citation - WoS: 23Citation - Scopus: 26A Univariate Time Series Methodology Based on Sequence-To Learning for Short To Midterm Wind Power Production(Pergamon-elsevier Science Ltd, 2022) Akbal, Yildirim; Unlu, Kamil DemirberkThe biggest wind farm of Turkey is placed at Manisa which is located in the Aegean Region. Electricity is a nonstorable commodity for that reason, it is very important to have a strong forecast and model of the potential electricity production to plan the electricity loads. In this study, the aim is to model and forecast electricity production of the wind farms located at Manisa by using a univariate model based on sequence-to-sequence learning. The forecasting range of the study is from short term to midterm. The strength of the proposed model is that; it only needs its own lagged value to make forecasts. The empirical evidences show that the model has high coefficient of variation (R-2) in short term and moderate R-2 in the midterm forecast. Although in the midrange forecasts R-2 slightly decreases mean squared error and mean absolute error shows that the model is accurate also in the midterm forecasts. The proposed model is not only strong in hourly electricity production forecasts but with a slight modification also in forecasting the minimum, maximum and average electricity production for a fixed range. This study concludes with two fresh and intriguing future research ideas.Article Citation - WoS: 9Citation - Scopus: 7Identifying the Cycles in Covid-19 Infection: the Case of Turkey(Taylor & Francis Ltd, 2023) Akdi, Yilmaz; Karamanoglu, Yunus Emre; Unlu, Kamil Demirberk; Bas, CemThe new coronavirus disease, called COVID-19, has spread extremely quickly to more than 200 countries since its detection in December 2019 in China. COVID-19 marks the return of a very old and familiar enemy. Throughout human history, disasters such as earthquakes, volcanic eruptions and even wars have not caused more human losses than lethal diseases, which are caused by viruses, bacteria and parasites. The first COVID-19 case was detected in Turkey on 12 March 2020 and researchers have since then attempted to examine periodicity in the number of daily new cases. One of the most curious questions in the pandemic process that affects the whole world is whether there will be a second wave. Such questions can be answered by examining any periodicities in the series of daily cases. Periodic series are frequently seen in many disciplines. An important method based on harmonic regression is the focus of the study. The main aim of this study is to identify the hidden periodic structure of the daily infected cases. Infected case of Turkey is analyzed by using periodogram-based methodology. Our results revealed that there are 4, 5 and 62 days cycles in the daily new cases of Turkey.Article Citation - WoS: 9Citation - Scopus: 9A Hybrid Deep Learning Methodology for Wind Power Forecasting Based on Attention(Taylor & Francis inc, 2024) Akbal, Yildirim; Unlu, Kamil DemirberkWind energy, as a sustainable energy source, poses challenges in terms of storage. Therefore, careful planning is crucial to utilize it efficiently. Deep learning algorithms are gaining popularity for analyzing complex time series data. However, as the "no free lunch" theorem suggests, the trade-off is: they need a lot of data to achieve the benefits. This even brings up a severe challenge for time series analysis, as the availability of historical data is often limited. This study aims to address this issue by proposing a novel shallow deep learning approach for wind power forecasting. The proposed model utilizes a fusion of transformers, convolutional and recurrent neural networks to efficiently handle several time series simultaneously. The empirical evidence demonstrates that the suggested innovative method exhibits exceptional forecasting performance, as indicated by a coefficient of determination (R2) of 0.99. When the forecasting horizon reaches 48, the model's performance declines significantly. However, when dealing with long ranges, utilizing the mean as a metric rather than individual point estimates would yield superior results. Even when forecasting up to 96 hrs in advance, obtaining an R2 value of 0.50 is considered a noteworthy accomplishment in the context of average forecasting.
