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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: 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.
