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Water wave optimization with deep learning driven smart grid stability prediction

Smart Grid (SG) technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures, control systems, and communication technologies. In SGs, user demand data is gathered a...

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Main Authors:	Mustafa Hilal, Anwer, Hassan Abdalla Hashim, Aisha, G. Mohamed, Heba, Alamgeer, Mohammad, K. Nour, Mohamed, Abdelrahman, Anas, Motwakel, Abdelwahed
Format:	Article
Language:	English
Published:	Tech Science Press 2022
Subjects:	TK5101 Telecommunication. Including telegraphy, radio, radar, television
Online Access:	http://irep.iium.edu.my/99925/2/99925_Water%20wave%20optimization%20with%20deep%20learning.pdf http://irep.iium.edu.my/99925/ http://doi.org/10.32604/cmc.2022.031425
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spelling	my.iium.irep.999252022-09-12T00:56:02Z http://irep.iium.edu.my/99925/ Water wave optimization with deep learning driven smart grid stability prediction Mustafa Hilal, Anwer Hassan Abdalla Hashim, Aisha G. Mohamed, Heba Alamgeer, Mohammad K. Nour, Mohamed Abdelrahman, Anas Motwakel, Abdelwahed TK5101 Telecommunication. Including telegraphy, radio, radar, television Smart Grid (SG) technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures, control systems, and communication technologies. In SGs, user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption. Since the entire procedure is valued on the basis of time, it is essential to perform adaptive estimation of the SG’s stability. Recent advancements in Machine Learning (ML) and Deep Learning (DL) models enable the designing of effective stability prediction models in SGs. In this background, the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction (WWOODL-SGSP) model. The aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient manner. To attain this, the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform level. Then, WWO algorithm is applied to choose an optimal subset of features from the pre-processed data. Next, Deep Belief Network (DBN) model is followed to predict the stability level of SGs. Finally, Slime Mold Algorithm (SMA) is exploited to fine tune the hyperparameters involved in DBN model. In order to validate the enhanced performance of the proposed WWOODL-SGSP model, a wide range of experimental analyses Tech Science Press 2022 Article PeerReviewed application/pdf en http://irep.iium.edu.my/99925/2/99925_Water%20wave%20optimization%20with%20deep%20learning.pdf Mustafa Hilal, Anwer and Hassan Abdalla Hashim, Aisha and G. Mohamed, Heba and Alamgeer, Mohammad and K. Nour, Mohamed and Abdelrahman, Anas and Motwakel, Abdelwahed (2022) Water wave optimization with deep learning driven smart grid stability prediction. Computers, Materials & Continua, 73 (3). pp. 6019-6035. ISSN 1546-2226 http://doi.org/10.32604/cmc.2022.031425 10.32604/cmc.2022.031425
institution	Universiti Islam Antarabangsa Malaysia
building	IIUM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	International Islamic University Malaysia
content_source	IIUM Repository (IREP)
url_provider	http://irep.iium.edu.my/
language	English
topic	TK5101 Telecommunication. Including telegraphy, radio, radar, television
spellingShingle	TK5101 Telecommunication. Including telegraphy, radio, radar, television Mustafa Hilal, Anwer Hassan Abdalla Hashim, Aisha G. Mohamed, Heba Alamgeer, Mohammad K. Nour, Mohamed Abdelrahman, Anas Motwakel, Abdelwahed Water wave optimization with deep learning driven smart grid stability prediction
description	Smart Grid (SG) technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures, control systems, and communication technologies. In SGs, user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption. Since the entire procedure is valued on the basis of time, it is essential to perform adaptive estimation of the SG’s stability. Recent advancements in Machine Learning (ML) and Deep Learning (DL) models enable the designing of effective stability prediction models in SGs. In this background, the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction (WWOODL-SGSP) model. The aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient manner. To attain this, the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform level. Then, WWO algorithm is applied to choose an optimal subset of features from the pre-processed data. Next, Deep Belief Network (DBN) model is followed to predict the stability level of SGs. Finally, Slime Mold Algorithm (SMA) is exploited to fine tune the hyperparameters involved in DBN model. In order to validate the enhanced performance of the proposed WWOODL-SGSP model, a wide range of experimental analyses
format	Article
author	Mustafa Hilal, Anwer Hassan Abdalla Hashim, Aisha G. Mohamed, Heba Alamgeer, Mohammad K. Nour, Mohamed Abdelrahman, Anas Motwakel, Abdelwahed
author_facet	Mustafa Hilal, Anwer Hassan Abdalla Hashim, Aisha G. Mohamed, Heba Alamgeer, Mohammad K. Nour, Mohamed Abdelrahman, Anas Motwakel, Abdelwahed
author_sort	Mustafa Hilal, Anwer
title	Water wave optimization with deep learning driven smart grid stability prediction
title_short	Water wave optimization with deep learning driven smart grid stability prediction
title_full	Water wave optimization with deep learning driven smart grid stability prediction
title_fullStr	Water wave optimization with deep learning driven smart grid stability prediction
title_full_unstemmed	Water wave optimization with deep learning driven smart grid stability prediction
title_sort	water wave optimization with deep learning driven smart grid stability prediction
publisher	Tech Science Press
publishDate	2022
url	http://irep.iium.edu.my/99925/2/99925_Water%20wave%20optimization%20with%20deep%20learning.pdf http://irep.iium.edu.my/99925/ http://doi.org/10.32604/cmc.2022.031425
_version_	1744353533863395328
score	13.211869

Water wave optimization with deep learning driven smart grid stability prediction

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