Multilayer LSTM Model for Wind Power Estimation in the Scada System

Year 2023, , 116 - 122, 31.12.2023

Selahattin Barış Çelebi , Ömer Ali Karaman

https://doi.org/10.36222/ejt.1382837

Abstract

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.

Keywords

Power forecasting, Wind turbine energy, Long short-term memory, Regression, Machine learning

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