Featuring Wave and Tidal Energy Conversion With Artificial Intelligence and Machine Learning DOI
Laeeq Razzak Janjua

Practice, progress, and proficiency in sustainability, Journal Year: 2024, Volume and Issue: unknown, P. 59 - 82

Published: Nov. 1, 2024

Artificial intelligence (AI) and machine learning (ML) are becoming indispensable tools for increasing the efficiency sustainability of this renewable energy source ocean industry has made significant strides in recent years. The initial stages research development when AI ML first started to emerge wave tidal space. development, management, upkeep maritime systems have all changed as a result these innovations. An massive, unexplored resource that potential make an important contribution world's mix is energy. In order maximize efficacy conversion, chapter focuses on incorporation artificial technologies. It looks at technologies' capability support clean solutions build sustainable environment particularly context urban living.

Language: Английский

Optimizing renewable energy systems through artificial intelligence: Review and future prospects DOI Creative Commons
Kingsley Ukoba, Kehinde O. Olatunji,

Eyitayo Adeoye

et al.

Energy & Environment, Journal Year: 2024, Volume and Issue: 35(7), P. 3833 - 3879

Published: May 22, 2024

The global transition toward sustainable energy sources has prompted a surge in the integration of renewable systems (RES) into existing power grids. To improve efficiency, reliability, and economic viability these systems, synergistic application artificial intelligence (AI) methods emerged as promising avenue. This study presents comprehensive review current state research at intersection AI, highlighting key methodologies, challenges, achievements. It covers spectrum AI utilizations optimizing different facets RES, including resource assessment, forecasting, system monitoring, control strategies, grid integration. Machine learning algorithms, neural networks, optimization techniques are explored for their role complex data sets, enhancing predictive capabilities, dynamically adapting RES. Furthermore, discusses challenges faced implementation such variability, model interpretability, real-time adaptability. potential benefits overcoming include increased yield, reduced operational costs, improved stability. concludes with an exploration prospects emerging trends field. Anticipated advancements explainable reinforcement learning, edge computing, discussed context impact on Additionally, paper envisions AI-driven solutions smart grids, decentralized development autonomous management systems. investigation provides important insights landscape applications

Language: Английский

Citations

45
The Impact of Integrating Variable Renewable Energy Sources into Grid-Connected Power Systems: Challenges, Mitigation Strategies, and Prospects DOI Creative Commons

Emmanuel Ejuh,

Kang Roland Abeng,

Chu Donatus Iweh

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(3), P. 689 - 689

Published: Feb. 2, 2025

Although the impact of integrating solar and wind sources into power system has been studied in past, chaos caused by energy generation not yet had broader mitigation solutions notwithstanding their rapid deployment. Many research efforts using prediction models have developed real-time monitoring variability machine learning predictive algorithms contrast to conventional methods studying variability. This study focused on causes types variability, challenges, strategies used minimize grids worldwide. A summary top ten cases countries that successfully managed electrical presented. Review shows most success embraced advanced storage, grid upgrading, flexible mix as key technological economic strategies. seven-point conceptual framework involving all stakeholders for managing networks increasing variable renewable (VRE)-grid integration proposed. Long-duration virtual plants (VPPs), smart infrastructure, cross-border interconnection, power-to-X, flexibility are takeaways achieving a reliable, resilient, stable grid. review provides useful up-to-date information researchers industries investing energy-intensive

Language: Английский

Citations

2
Edge AI: A Taxonomy, Systematic Review and Future Directions DOI
Sukhpal Singh Gill, Muhammed Golec,

Jianmin Hu

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 28(1)

Published: Oct. 18, 2024

Language: Английский

Citations

15
Human-Machine Nexus for Digital Rebound Fostering Futuristic Energy-Efficiency DOI
Bhupinder Singh, Christian Kaunert

Practice, progress, and proficiency in sustainability, Journal Year: 2024, Volume and Issue: unknown, P. 222 - 244

Published: June 28, 2024

Human-machine interaction plays a pivotal role in realizing energy-efficient and sustainable urban mobility. There is vital contribution of HMI facilitating more environmentally responsible transportation solutions. Through the seamless between users, smart infrastructure, autonomous vehicles, HMI-driven approaches promise to optimize traffic flows, reduce energy consumption, minimize emissions. In rapidly urbanizing world, evolution smart-sustainable mobility pressing concern, necessitating judicious integration cutting-edge technology with ecological sustainability. This chapter explores multifaceted nexus human-machine interaction, technology, sustainability, mobility, specific focus on footprint within context systems.

Language: Английский

Citations

11
Digital Twins in 3D Printing Processes Using Artificial Intelligence DOI Open Access
Izabela Rojek, Tomasz Marciniak, Dariusz Mikołajewski

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(17), P. 3550 - 3550

Published: Sept. 6, 2024

Digital twins (DTs) provide accurate, data-driven, real-time modeling to create a digital representation of the physical world. The integration new technologies, such as virtual/mixed reality, artificial intelligence, and DTs, enables research into ways achieve better sustainability, greater efficiency, improved safety in Industry 4.0/5.0 technologies. This paper discusses concepts, limitations, future trends, potential directions infrastructure underlying intelligence for large-scale semi-automated DT building environments. Grouping these technologies along lines allows consideration their individual risk factors use available data, resulting an approach generate holistic virtual representations facilitate predictive analyses industrial practices. Artificial intelligence-based DTs are becoming tool monitoring, simulating, optimizing systems, widespread implementation mastery this technology will lead significant improvements performance, reliability, profitability. Despite advances, aforementioned still requires research, improvement, investment. article’s contribution is concept that, if adopted instead traditional approach, can become standard practice rather than advanced operation accelerate development.

Language: Английский

Citations

4
The intelligent brain and the energy heart: Synergistic evolution of artificial intelligence and energy storage technology in China DOI
Yan Chen,

Jiayi Lyu,

Umair Akram

et al.

Acta Psychologica, Journal Year: 2025, Volume and Issue: 253, P. 104711 - 104711

Published: Jan. 23, 2025

Language: Английский

Citations

0
A Review of Emerging Techniques for Power Quality Improvement in Renewable Energy Integration DOI Creative Commons
P. Rajesh Kumar, Shravani Chapala, M. Rajitha

et al.

E3S Web of Conferences, Journal Year: 2025, Volume and Issue: 616, P. 03029 - 03029

Published: Jan. 1, 2025

The growing integration of renewable energy sources (RES) into power grids presents significant challenges to maintaining quality (PQ) due the inherent variability and intermittency these resources. This paper provides a comprehensive review emerging techniques aimed at improving in systems with high levels integration. It examines state-of-the-art methods, including advanced control strategies, innovative compensation devices, latest developments electronic interfaces. Special emphasis is placed on role modern technologies, such as artificial intelligence (AI) machine learning (ML), enhancing adaptability robustness PQ solutions. effectiveness limitations various approaches, use Flexible AC Transmission Systems (FACTS), Unified Power Quality Conditioners (UPQC), dynamic voltage restorers (DVR), are critically analysed. Additionally, this explores smart grid concepts deployment storage complementary measures mitigate issues. Future research directions outlined, highlighting need for further advancements real-time monitoring, adaptive algorithms, hybrid that combine multiple optimal performance. serves valuable resource researchers, engineers, policymakers seeking understand address associated future systems.

Language: Английский

Citations

0
Data-driven modelling method and application based on energy multi-layer network structure of energy hub DOI Creative Commons
Qingsen Cai,

Luochang Wu,

Chunyang Gao

et al.

Automatika, Journal Year: 2025, Volume and Issue: 66(2), P. 335 - 352

Published: March 24, 2025

Language: Английский

Citations

0
Synergizing Artificial Intelligence with Sustainable Design for Smart Built Environments DOI
Luana Parisi, Victoria Maame Afriyie Kumah, Elvis Konadu Adjei

et al.

Lecture notes in civil engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1493 - 1504

Published: Jan. 1, 2025

Language: Английский

Citations

0
Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California DOI Open Access
Victor Oliveira Santos, Felipe Pinto Marinho, Paulo Alexandre Costa Rocha

et al.

Published: July 2, 2024

Quantum machine learning applications have become viable with the recent advancements in quantum computing. Merging ML power of computing holds great potential for data-driven decision-making, as well development more powerful models capable handling complex datasets faster processing time. This area offers improving accuracy real-time forecasting renewable energy production. However, literature on this topic is sparse. Addressing knowledge gap, study aims to design, implement, and evaluate performance a neural network forecast model solar irradiance up 3-hours ahead. The proposed was compared Support Vector Regression, Group Method Data Handling, Extreme Gradient Boost classical models. Using best configuration found, framework could provide competitive results when its competitors, considering intervals 5- 120-minutes ahead, where it fourth best-performing paradigm. For ahead predictions, QNN able overcome clas-sical counterparts, but XGBoost. fact can be an indication that may identify retrieve relevant spatiotemporal information from input dataset such manner not attainable by current approaches.

Language: Английский

Citations

3