A comprehensive review of AI-enhanced smart grid integration for hydrogen energy: Advances, challenges, and future prospects

M SaberiKamarposhti, H Kamyab, S Krishnan… - International Journal of …, 2024 - Elsevier
The convergence of hydrogen energy with artificial intelligence (AI) in smart infrastructure
has significant potential to revolutionise the worldwide energy sector. This article thoroughly …

[HTML][HTML] The Russia-Ukraine conflict: Its implications for the global food supply chains

S Jagtap, H Trollman, F Trollman, G Garcia-Garcia… - Foods, 2022 - mdpi.com
Food is one of the most traded goods, and the conflict in Ukraine, one of the European
breadbaskets, has triggered a significant additional disruption in the global food supply …

A high-accuracy phishing website detection method based on machine learning

M Bahaghighat, M Ghasemi, F Ozen - Journal of Information Security and …, 2023 - Elsevier
The rapid development of e-commerce, e-banking, and social networks has made phishing
attack detection one of the most critical technologies in all cyber security systems. To …

[HTML][HTML] Designing and prototy** the architecture of a digital twin for wind turbine

M Mahmoud, C Semeraro, MA Abdelkareem… - International Journal of …, 2024 - Elsevier
This paper outlines the key components necessary to develop a digital twin (DT) for a wind
turbine, aiming to provide a detailed methodology and guidelines for building this system …

Tracking and predicting technological knowledge interactions between artificial intelligence and wind power: Multimethod patent analysis

J Wang, L Cheng, L Feng, KY Lin, L Zhang… - Advanced Engineering …, 2023 - Elsevier
To track the dynamics of AI and wind power technology knowledge interaction and predict
future interaction directions, this study proposes a multiview and multilayer patent analysis …

Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning …

V Rathakrishnan, S Bt. Beddu, AN Ahmed - Scientific Reports, 2022 - nature.com
Predicting the compressive strength of concrete is a complicated process due to the
heterogeneous mixture of concrete and high variable materials. Researchers have predicted …

Machine learning autoencoder‐based parameters prediction for solar power generation systems in smart grid

A Zafar, Y Che, M Faheem, M Abubakar, S Ali… - IET Smart …, 2024 - Wiley Online Library
During the fourth energy revolution, artificial intelligence implementation is necessary in all
fields of technology to meet the increasing energy demands and address the diminishing …

[HTML][HTML] A survey on recent applications of artificial intelligence and optimization for smart grids in smart manufacturing

CC Hsu, BH Jiang, CC Lin - Energies, 2023 - mdpi.com
To enable highly automated manufacturing and net-zero carbon emissions, manufacturers
have invested heavily in smart manufacturing. Sustainable and smart manufacturing …

[HTML][HTML] Use of state-of-art machine learning technologies for forecasting offshore wind speed, wave and misalignment to improve wind turbine performance

M Sacie, M Santos, R López, R Pandit - Journal of Marine Science and …, 2022 - mdpi.com
One of the most promising solutions that stands out to mitigate climate change is floating
offshore wind turbines (FOWTs). Although they are very efficient in producing clean energy …

RAGN-L: a stacked ensemble learning technique for classification of fire-resistant columns

AÖ Çiftçioğlu - Expert Systems with Applications, 2024 - Elsevier
One of the main challenges in using reinforced concrete materials in structures is to
comprehend their fire resistance. The assessment of fire resistance can be performed in a …