aspg-31-2004-1791684479 20261011020759 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 2023 3 2 Myvizhi M. Department of Mathematics, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India Ahmed Abdel-Monem Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Sharqiyah, Egypt Accurate forecasting is essential for the long-term success of adding wind energy to the national power system. In this study, we look at forecasting wind turbine using a LSTM deep learning model. To forecast potential outcomes for a time series, it is sufficient to initially obtain pertinent details from past data. While many methods struggle with understanding the long-term dependencies encoded in data sets, LSTM options, an instance of the strategy in deep learning, show potential for efficiently overcoming this challenge. An overview of LSTM's architecture and forward propagation method is provided initially. LSTM network is applied to the wind turbine prediction dataset. This dataset has 9 features and 6575 records. There are four performance matrices used to test the model. The four matrices are mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). MAPE obtained the least error. 2023 48 57 2004 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAACI.030205 https://www.americaspg.com/journal/31/article/2004 https://www.americaspg.com/storage/81692538074.pdf