Boosting wind turbine performance with advanced smart power prediction: Employing a hybrid ARMA-LSTM technique

Abstract

Wind energy holds significant importance among renewable energy sources, necessitating precise power forecast systems for the operation of wind turbines. In order to meet the urgent requirement for accurate power forecasting in wind energy production, this study introduces a revolutionary Smart Power Prediction System designed especially for wind turbines. The suggested approach introduces a hybrid model that combines Autoregressive Moving Average (ARMA) and Long Short-Term Memory (LSTM) methods in order to address the limitations of current strategies in capturing both short- and long-term dependencies in wind speed data. This combination improves forecast accuracy while successfully mitigating the drawbacks of conventional methods. To further improve forecasting skills, critical temporal and frequency domain insights are extracted from wind speed data using sophisticated feature extraction techniques, most notably the Discrete Wavelet Transform (DWT). With a remarkable accuracy rate of 99.24%, the integrated ARMA-LSTM-DWT model outperforms current methods by 3.74% after thorough experimentation and validation. The system's implementation in Python highlights its usefulness and potential to greatly improve wind turbine operational efficiency, which will enable improved grid integration and energy management. Finally, by developing a strong and accurate wind energy-specific forecasting system, our work helps to create a more ecologically friendly and sustainable energy environment.

Keywords

• Wind energy
• Power forecast
• Wind turbines
• Smart power prediction system
• ARMA
• LSTM