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

Author:

Abdel-Aty Abdel-HaleemORCID,Nisar Kottakkaran Sooppy,Alharbi Wedad R.,Owyed Saud,Alsharif Mohammed H.

Funder

University of Bisha

Publisher

Elsevier BV

Reference27 articles.

1. A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting;Niu;Appl. Energy,2019

2. “A hybrid deep learning model for short-term PV power forecasting - ScienceDirect.” https://www.sciencedirect.com/science/article/abs/pii/S0306261919319038 (accessed Sep. 12, 2023).

3. “A hybrid deep learning-based neural network for 24-h ahead wind power forecasting - ScienceDirect.” https://www.sciencedirect.com/science/article/abs/pii/S030626191930889X (accessed Sep. 12, 2023).

4. A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions;Magazzino;Renew. Energy,2021

5. A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods, and grey wolf optimizer,”;Altan;Appl. Soft Comput.,2021

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