Cited by A brief overview of deep generative models and how they can be used to discover new electrode materials

A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems DOI

Mahmoud M. Kiasari,

Mahdi Ghaffari, Hamed H. Aly

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(16), P. 4128 - 4128

Published: Aug. 19, 2024

The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient infrastructure. Their is vital achieving sustainability among all clean sources, including wind, solar, hydropower. This review paper provides thoughtful analysis the current status grid, focusing on integrating various RES, such as wind grid. highlights significant role RES in reducing greenhouse gas emissions traditional fossil fuel reliability, thereby contributing to environmental empowering security. Moreover, key advancements grid technologies, Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), Supervisory Data Acquisition (SCADA) systems, are explored clarify related topics usage technologies enhances efficiency, resilience introduced. also investigates application Machine Learning (ML) techniques management optimization within with techniques. findings emphasize transformative impact advanced alongside need continued innovation supportive policy frameworks achieve future.

Language: Английский

Citations

Recent trends in hierarchical electrode materials in supercapacitor: Synthesis, electrochemical measurements, performance and their charge-storage mechanism DOI

Ganesan Sriram, Gurumurthy Hegde, Karmegam Dhanabalan

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 94, P. 112454 - 112454

Published: June 13, 2024

Language: Английский

Citations

Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management DOI

Ashkan Safari, Mohammadreza Daneshvar, Amjad Anvari‐Moghaddam

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(23), P. 11112 - 11112

Published: Nov. 28, 2024

Artificial intelligence (AI) and machine learning (ML) can assist in the effective development of power system by improving reliability resilience. The rapid advancement AI ML is fundamentally transforming energy management systems (EMSs) across diverse industries, including areas such as prediction, fault detection, electricity markets, buildings, electric vehicles (EVs). Consequently, to form a complete resource for cognitive techniques, this review paper integrates findings from more than 200 scientific papers (45 reviews 155 research studies) addressing utilization EMSs its influence on sector. additionally investigates essential features smart grids, big data, their integration with EMS, emphasizing capacity improve efficiency reliability. Despite these advances, there are still additional challenges that remain, concerns regarding privacy integrating different systems, issues related scalability. finishes analyzing problems providing future perspectives ongoing use EMS.

Language: Английский

Citations

Insights into the specific capacitance of CNT-based supercapacitor electrodes using artificial intelligence DOI

Wael Z. Tawfik, Mohamed Shaban, Athira Raveendran

et al.

RSC Advances, Journal Year: 2025, Volume and Issue: 15(5), P. 3155 - 3167

Published: Jan. 1, 2025

This study uses various ML algorithms, including artificial neural networks, random forest, k -nearest neighbors, and decision tree, based on experimental studies to predict the specific capacitance characteristics of CNT-based SC electrodes.

Language: Английский

Citations

A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials DOI

Chuang Wang, Xingxing Cheng, Kai Luo

et al.

Materials Science and Engineering R Reports, Journal Year: 2025, Volume and Issue: 165, P. 101010 - 101010

Published: May 3, 2025

Language: Английский

Citations

Recent Progress on the Synthesis, Morphological Topography, and Battery Applications of Polypyrrole-Based Nanocomposites DOI

Mohammad Mizanur Rahman Khan, Md. Mahamudul Hasan Rumon

Polymers, Journal Year: 2024, Volume and Issue: 16(23), P. 3277 - 3277

Published: Nov. 25, 2024

Polypyrrole (PPy)-based nanocomposite materials are of great interest to the scientific community owing their usefulness in designing state-of-the-art industrial applications, such as fuel cells, catalysts and sensors, energy devices, especially batteries. However, commercialization these has not yet reached a satisfactory level implementation. More research is required for design synthesis PPy-based composite numerous types battery applications. Due rising demand environmentally friendly, cost-effective, sustainable energy, applications significant solution crisis, utilizing suitable like composites. Among conducting polymers, PPy considered an important class ease synthesis, low cost, friendly nature, so on. In this context, nanocomposites may be very promising due nanostructural properties distinct morphological topography, which vital concerns Such features make them particularly next-generation electrode materials. fabrication appropriate still challenging area research. This review paper describes current progress on synthesizing composites along with topography. We discussed here recent different composites, including PPy/S, PPy/MnOx, MWCNT/PPy, V

Language: Английский

Citations

Research progress in sodium-iron-phosphate-based cathode materials for cost-effective sodium-ion batteries: Crystal structure, preparation, challenges, strategies, and developments DOI

M. Kouthaman,

R.A. Arul Raja,

Dongwoo Shin

et al.

Progress in Materials Science, Journal Year: 2024, Volume and Issue: unknown, P. 101425 - 101425

Published: Dec. 1, 2024

Language: Английский

Citations

AI-based approach for predicting the storage performance of zinc oxide-based supercapacitor electrodes DOI

Mostafa A. Ebied, Mohamed Mostafa A. Azim,

Ahmed Emad-Eldeen

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 94, P. 112292 - 112292

Published: June 11, 2024

Language: Английский

Citations

Sustainable Freshwater/Energy Supply through Geothermal-Centered Layout Tailored with Humidification-Dehumidification Desalination Unit; Optimized by Regression Machine Learning Techniques DOI

Shuguang Li, Yuchi Leng, Rishabh Chaturvedi

et al.

Energy, Journal Year: 2024, Volume and Issue: 303, P. 131919 - 131919

Published: June 3, 2024

Language: Английский

Citations

Machine learning-assisted prediction, screen, and interpretation of porous carbon materials for high-performance supercapacitors DOI

Hongwei Liu, Zhenming Cui,

Zhennan Qiao

et al.

Journal of Materials Informatics, Journal Year: 2024, Volume and Issue: 4(4)

Published: Oct. 24, 2024

Porous carbon materials (PCMs) are preferred as electrode for supercapacitor energy storage applications due to their superior characteristics. However, the optimal performance of these electrodes requires trial and error experimental exploration complexity influencing factors. To address this limitation, we develop a machine learning (ML) combined validation approach predict, screen interpret ideal PCMs supercapacitors. Four ML models used predicting specific capacitance (SC) properties light gradient boosting (LGBM) model exhibits best prediction with an R2 value 0.92. Through comprehensive interpretability analysis ML, important variables SC identified impact range is determined. By analyzing deviation key values during verification, accurate predictions made, facilitating precise material screening. Additionally, accuracy applicability evaluated. This research pioneered eigenvalue fall-point screening based on combination experiments accurately materials, providing compelling strategy advancing technology.

Language: Английский

Citations