Leveraging machine learning in porous media DOI Creative Commons
Mostafa Delpisheh, Benyamin Ebrahimpour,

Abolfazl Fattahi

et al.

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: 12(32), P. 20717 - 20782

Published: Jan. 1, 2024

Evaluating the advantages and limitations of applying machine learning for prediction optimization in porous media, with applications energy, environment, subsurface studies.

Language: Английский

HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting DOI
Ahmed A. Ewees, Mohammed A. A. Al‐qaness, Laith Abualigah

et al.

Energy Conversion and Management, Journal Year: 2022, Volume and Issue: 268, P. 116022 - 116022

Published: July 27, 2022

Language: Английский

Citations

119
State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques DOI
Raniyah Wazirali, Elnaz Yaghoubi,

Mohammed Shadi S. Abujazar

et al.

Electric Power Systems Research, Journal Year: 2023, Volume and Issue: 225, P. 109792 - 109792

Published: Sept. 8, 2023

Language: Английский

Citations

113
Recent progress in the design of advanced MXene/metal oxides-hybrid materials for energy storage devices DOI
Muhammad Sufyan Javed, Abdul Mateen, Iftikhar Hussain

et al.

Energy storage materials, Journal Year: 2022, Volume and Issue: 53, P. 827 - 872

Published: Oct. 6, 2022

Language: Английский

Citations

110
Optimized integration of solar energy and liquefied natural gas regasification for sustainable urban development: Dynamic modeling, data-driven optimization, and case study DOI
Chengying Yang, Tirumala Uday Kumar Nutakki, Mohammed A. Alghassab

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 447, P. 141405 - 141405

Published: Feb. 26, 2024

Language: Английский

Citations

67
Optimizing Biomass-to-Biofuel Conversion DOI
Zakir Hussain,

M. Babe,

S. Saravanan

et al.

Advances in finance, accounting, and economics book series, Journal Year: 2023, Volume and Issue: unknown, P. 191 - 214

Published: Oct. 3, 2023

This chapter explores the integration of IoT and AI technologies to optimize biomass-to-biofuel conversion processes. algorithms can be used process parameters such as temperature, pressure, enzyme dosage, leading increased biofuel yields, reduced energy consumption, improved quality control. Sustainability assessment is also highlighted, with playing a crucial role in monitoring analyzing sustainability metrics. Companies Pacific Ethanol, Renmatix, IOCL, GranBio have achieved significant improvements yield, efficiency, control, by leveraging technologies. These advancements inspire potential applications strategies different biomass feedstock scenarios, enabling organizations drive transition towards cleaner more sustainable sources while improving operational efficiency competitiveness.

Language: Английский

Citations

63
AI-driven solutions in renewable energy: A review of data science applications in solar and wind energy optimization DOI Creative Commons

Nzubechukwu Chukwudum Ohalete,

Adebayo Olusegun Aderibigbe,

Emmanuel Chigozie Ani

et al.

World Journal of Advanced Research and Reviews, Journal Year: 2023, Volume and Issue: 20(3), P. 401 - 417

Published: Dec. 7, 2023

This comprehensive review explores the transformative role of artificial intelligence (AI) and data science in renewable energy sector, with a particular focus on solar wind optimization. The study systematically examines intersection AI energy, highlighting emergence AI-driven solutions their impact enhancing efficiency, reliability, sustainability systems. begins an overview its growing importance global mix, emphasizing critical this sector. It then delves into methodological approach, outlining research strategy criteria for selecting relevant studies energy. includes detailed analysis collection synthesis techniques used to identify key innovations trends core comprises extensive literature survey applications covers fundamental principles state-of-the-art techniques, emerging such as novel algorithms integration grids. evaluates technological, economic, environmental impacts addressing challenges proposing solutions. Furthermore, discusses standards regulatory frameworks implications stakeholders. concludes summary AI's future prospects, recommendations industry leaders policymakers. provides thorough understanding current state potential offering valuable insights researchers, professionals, policymakers engaged field sustainable

Language: Английский

Citations

48
Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions DOI

Zaid Allal,

Hassan Noura, Ola Salman

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 354, P. 120392 - 120392

Published: Feb. 21, 2024

Language: Английский

Citations

33
Replenish Artificial Intelligence in Renewable Energy for Sustainable Development DOI
Bhupinder Singh, Pushan Kumar Dutta, Christian Kaunert

et al.

Advances in finance, accounting, and economics book series, Journal Year: 2024, Volume and Issue: unknown, P. 198 - 227

Published: April 5, 2024

The whole world is struggling with the multifaceted challenges of climate change, environmental degradation, and for search sustainable development, United Nations Sustainable Development Goals (SDGs) have emerged as a guiding beacon. Among these, SDG 7 (Affordable Clean Energy) 13 (Climate Action) hold particular significance, encapsulating critical facets humanity's collective pursuit more future. Nation states signatories to an array international treaties agreements committed comprehensive set responsibilities aimed at advancing goals 13. legal landscape also champions cooperation fundamental tenet. This chapter introduces legal-financial advisory framework address evolving AI-driven renewable energy projects.

Language: Английский

Citations

25
The energy security risk assessment of inefficient wind and solar resources under carbon neutrality in China DOI
Jingbo Sun, Yang Wang,

Yuan He

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 360, P. 122889 - 122889

Published: Feb. 21, 2024

Language: Английский

Citations

19
A comprehensive review of Building Energy Management Systems (BEMS) for Improved Efficiency DOI Creative Commons

Wags Numoipiri Digitemie,

Ifeanyi Onyedika Ekemezie

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(3), P. 829 - 841

Published: March 11, 2024

Building Energy Management Systems (BEMS) play a crucial role in enhancing energy efficiency and sustainability buildings. This abstract provides comprehensive review of BEMS, focusing on its components, benefits, challenges, future trends. BEMS is centralized system that monitors controls building services, such as heating, ventilation, air conditioning, lighting, other systems, to improve occupant comfort. The key components include sensors, controllers, communication networks, user interfaces. These work together collect data performance, analyze consumption patterns, optimize operation. One the primary benefits ability reduce costs. By monitoring controlling systems based occupancy schedules environmental conditions, can significantly waste. Additionally, comfort productivity by maintaining optimal indoor conditions. Despite faces several including high upfront costs, complexity installation, integration issues with existing systems. However, advancements technology, Internet Things (IoT) cloud computing, are addressing these challenges making more accessible cost-effective. Looking ahead, promising, continued technology driving adoption into smart artificial intelligence machine learning algorithms expected further energy-saving capabilities enhance performance. In conclusion, for improving leveraging capabilities, help consumption, lower create healthier comfortable environments.

Language: Английский

Citations

19