Wind energy is clean energy that does not pollute the environment. However, the complex and variable operating environment of a wind turbine often makes it difficult to predict the instantaneous active power generated. In this study, a wind turbine active power estimation system based on a short-term memory network (LSTM) using time series analysis is proposed. The data obtained from the wind turbine SCADA system is used as input variables. In the proposed method, a multilayer LSTM architecture is designed to train the model. The first LSTM network consists of 64 units, and the second one consists of 32 units. This is followed by a dense layer consisting of 16 neurons. In the last layer, the architecture is finalized by using a linear activation function for the prediction process. The proposed deep learning (DL)-based LSTM prediction model takes into account environmental factors such as wind speed and wind direction for active power forecasting. The results show that the LSTM-based time series analysis method is capable of effectively capturing time series features among the data. Thus, the proposed architecture can realize high-accuracy active power forecasting.
Power forecasting Wind turbine energy Long short-term memory Regression Machine learning
Birincil Dil | İngilizce |
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Konular | Bilgisayar Yazılımı, Yazılım Mühendisliği (Diğer) |
Bölüm | Araştırma Makalesi |
Yazarlar | |
Yayımlanma Tarihi | 31 Aralık 2023 |
Gönderilme Tarihi | 29 Ekim 2023 |
Kabul Tarihi | 25 Kasım 2023 |
Yayımlandığı Sayı | Yıl 2023 Cilt: 13 Sayı: 2 |
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