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Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

R. Surendran1,*, Youseef Alotaibi2, Ahmad F. Subahi3

1 Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
2 Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
3 Department of Computer Science, University College of Al Jamoum, Umm Al-Qura University, Makkah, 21421, Saudi Arabia

* Corresponding Author: R. Surendran. Email: email

Computer Systems Science and Engineering 2023, 46(3), 3371-3386.


High precision and reliable wind speed forecasting have become a challenge for meteorologists. Convective events, namely, strong winds, thunderstorms, and tornadoes, along with large hail, are natural calamities that disturb daily life. For accurate prediction of wind speed and overcoming its uncertainty of change, several prediction approaches have been presented over the last few decades. As wind speed series have higher volatility and nonlinearity, it is urgent to present cutting-edge artificial intelligence (AI) technology. In this aspect, this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning (IWSP-CSODL) method. The presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter optimizer. In the presented IWSP-CSODL model, the prediction process is performed via a convolutional neural network (CNN) based long short-term memory with autoencoder (CBLSTMAE) model. To optimally modify the hyperparameters related to the CBLSTMAE model, the chicken swarm optimization (CSO) algorithm is utilized and thereby reduces the mean square error (MSE). The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios. The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.


Cite This Article

APA Style
Surendran, R., Alotaibi, Y., Subahi, A.F. (2023). Wind speed prediction using chicken swarm optimization with deep learning model. Computer Systems Science and Engineering, 46(3), 3371-3386.
Vancouver Style
Surendran R, Alotaibi Y, Subahi AF. Wind speed prediction using chicken swarm optimization with deep learning model. Comput Syst Sci Eng. 2023;46(3):3371-3386
IEEE Style
R. Surendran, Y. Alotaibi, and A.F. Subahi "Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model," Comput. Syst. Sci. Eng., vol. 46, no. 3, pp. 3371-3386. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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