@Article{csse.2023.034465,
AUTHOR = {R. Surendran, Youseef Alotaibi, Ahmad F. Subahi},
TITLE = {Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {46},
YEAR = {2023},
NUMBER = {3},
PAGES = {3371--3386},
URL = {http://www.techscience.com/csse/v46n3/52222},
ISSN = {},
ABSTRACT = {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.},
DOI = {10.32604/csse.2023.034465}
}