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Research Article |

Application of LSTM and GRU Neural Networks in Forecasting the Power Output of Wind Power Plant

Author(s): Dan Bui Thi Tuyet1*, Chau Le Thi Minh2, Sa Nguyen Thi Mi3, Duc Nguyen Tu4, and Hieu Phu Thi Ngoc5

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 13, Issue 2

Publisher : FOREX Publication

Published : 30 May 2025

e-ISSN : 2347-470X

Page(s) : 250-256

DOI: https://doi.org/10.37391/IJEER.130208

Abstract

This paper proposes the application of artificial intelligence to forecast the generation capacity of wind power plants by processing data through noise reduction and filtering. It subsequently employs Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for training, testing, and evaluation. Processing the initial data will help minimize noise and reduce the data space. The study focuses on preprocessing methods and selecting the appropriate neural network between LSTM and GRU. The initial data processing will assess the similarity through the Spearman rank correlation coefficient. The data used in the paper is taken from local wind turbines. The processed data will be input into the neural network for evaluation based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Percent Error (PE), and training time. The entire network simulation and evaluation process is performed using MATLAB software. The simulation results show the feasibility and suitability of the GRU network model combined with noise filtering methods, bringing high accuracy and less training time compared to the LSTM network. Specifically, the analysis contrasting the GRU network with the optimized dataset and the LSTM with the unprocessed dataset is more effective than a difference in RMSE of 15.592 and MAPE of 611.047%. Moreover, the time difference between the GRU and LSTM networks with the same dataset has a much earlier time difference from 6 to 28 seconds.

Keywords: Forecasting Generator Power , Wind Power Plants, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Artificial Neural Network (ANN).




Dan Bui Thi Tuyet, Master of Engineering, Faculty of Electrical and Electronic Engineering, HCMC University of Technology and Education, Vietnam; Email: danbtt@hcmute.edu.vn

Chau Le Thi Minh, Doctor of Philosophy, Department of Electric Power Systems, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi; Email: chau.lethiminh@hust.edu.vn

Sa Nguyen Thi Mi, Doctor of Philosophy, Faculty of Electrical and Electronic Engineering, HCMC University of Technology and Education, Vietnam; Email: misa@hcmute.edu.vn

Duc Nguyen Tu, Master of Engineering, Faculty of Electrical and Electronic Engineering, HCMC University of Technology and Education, Vietnam; Email: ducnt@hcmute.edu.vn

Hieu Phu Thi Ngoc, Master of Engineering, Faculty of Electrical and Electronic Engineering, HCMC University of Technology and Education, Vietnam; Email: hieuptn@hcmute.edu.vn

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Dan Bui Thi Tuyet, Chau Le Thi Minh, Sa Nguyen Thi Mi, Duc Nguyen Tu, and Hieu Phu Thi Ngoc (2025), Application of LSTM and GRU Neural Networks in Forecasting the Power Output of Wind Power Plant. IJEER 13(2), 250-256. DOI: 10.37391/IJEER.130208.