Afrah Al-Bossly^*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6395-6408, 2023, DOI:10.32604/cmc.2023.028433

Abstract Due to the drastic increase in global population as well as economy, electricity demand becomes considerably high. The recently developed smart grid (SG) technology has the ability to minimize power loss at the time of power distribution. Machine learning (ML) and deep learning (DL) models can be effectually developed for the design of SG stability techniques. This article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid Stability (SSODLSA-SGS) prediction model. Primarily, class imbalance data handling process is performed using Synthetic minority oversampling technique (SMOTE) technique. The SSODLSA-SGS model involves two stages of pre-processing… More >

Anwer Mustafa Hilal^1,2,*, Aisha Hassan Abdalla Hashim¹, Heba G. Mohamed³, Mohammad Alamgeer^4,5, Mohamed K. Nour⁶, Anas Abdelrahman⁷, Abdelwahed Motwakel²

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6019-6035, 2022, DOI:10.32604/cmc.2022.031425

Abstract 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… More >

Ashit Kumar Dutta^1,*, Manal Al Faraj¹, Yasser Albagory², Mohammad zeid M Alzamil¹, Abdul Rahaman Wahab Sait³

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1219-1231, 2023, DOI:10.32604/csse.2023.026467

Abstract A cyber physical energy system (CPES) involves a combination of processing, network, and physical processes. The smart grid plays a vital role in the CPES model where information technology (IT) can be related to the physical system. At the same time, the machine learning (ML) models find useful for the smart grids integrated into the CPES for effective decision making. Also, the smart grids using ML and deep learning (DL) models are anticipated to lessen the requirement of placing many power plants for electricity utilization. In this aspect, this study designs optimal multi-head attention based bidirectional long short term memory… More >