TY  - JOUR
T1  - Multilayer LSTM Model for Wind Power Estimation in the Scada System
AU  - Çelebi, Selahattin Barış
AU  - Karaman, Ömer Ali
PY  - 2023
DA  - December
Y2  - 2023
DO  - 10.36222/ejt.1382837
JF  - European Journal of Technique (EJT)
JO  - EJT
PB  - Hibetullah KILIÇ
WT  - DergiPark
SN  - 2536-5010
SP  - 116
EP  - 122
VL  - 13
IS  - 2
LA  - en
AB  - 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.
KW  - Power forecasting
KW  - Wind turbine energy
KW  - Long short-term memory
KW  - Regression
KW  - Machine learning
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UR  - https://doi.org/10.36222/ejt.1382837
L1  - https://dergipark.org.tr/en/download/article-file/3503345
ER  -