Input keywords, title, abstract, author, affiliation etc..
Journal Article An open access journal
Journal Article

Transforming Energy Sector with Intelligent Solutions

by Sarah Rodriguez
1
Tallinn University of Technology
*
Author to whom correspondence should be addressed.
JEIT  2023 5(1):185; https://doi.org/10.xxxx/xxxxxx
Received: 8 February 2023 / Accepted: 31 March 2023 / Published Online: 31 March 2023

Abstract

This paper explores the transformation of the energy sector through intelligent solutions enabled by AI-driven engineering. Through case studies and research insights, it investigates how artificial intelligence is revolutionizing traditional energy production, distribution, and consumption practices, including renewable energy integration, grid optimization, and energy efficiency management. The study highlights the application of AI techniques such as predictive modeling, smart metering, and demand response in enhancing reliability, sustainability, and affordability in energy systems. Additionally, it discusses the integration of AI with smart grids, energy storage systems, and electric vehicle infrastructure to enable decentralized energy generation, grid resilience, and electrified transportation. The paper also addresses challenges such as regulatory barriers, cybersecurity risks, and societal acceptance in the adoption of AI-driven engineering solutions in the energy sector. It emphasizes the importance of stakeholder engagement, policy support, and technology innovation in leveraging AI's potential to accelerate the transition to a clean, resilient, and equitable energy future.

 


Copyright: © 2023 by Rodriguez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Share and Cite

ACS Style
Rodriguez, S. Transforming Energy Sector with Intelligent Solutions. Journal of Engineering Innovations & Technology, 2023, 5, 185. doi:10.xxxx/xxxxxx
AMA Style
Rodriguez S.. Transforming Energy Sector with Intelligent Solutions. Journal of Engineering Innovations & Technology; 2023, 5(1):185. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Rodriguez, Sarah 2023. "Transforming Energy Sector with Intelligent Solutions" Journal of Engineering Innovations & Technology 5, no.1:185. doi:10.xxxx/xxxxxx

Article Metrics

Article Access Statistics

References

  1. Aazam, M., & Huh, E. N. (2016). Fog computing-based energy-efficient IoT framework for healthcare services in smart cities. Future Generation Computer Systems, 56, 849-861.
  2. Bessa, R. J., Matos, M. A., & Santos, S. (2018). A review on the integration of distributed energy resources in the smart grid. Renewable and Sustainable Energy Reviews, 82, 702-717.
  3. Carvallo, A. A., & Rohrig, K. (2017). Smart grid: Fundamentals of design and analysis. John Wiley & Sons.
  4. Gao, X., Zeng, C., Li, Y., & Zhao, D. (2019). A review of machine learning for the prediction of wind turbine loads and responses. Renewable Energy, 130, 1118-1133.
  5. Pahwa, A., Anand, G., & Mohsenian-Rad, H. (2018). A survey of multi-agent system applications in smart grids. IEEE Access, 6, 19167-19189.
  6. Singh, S., & Agrawal, R. (2019). Artificial intelligence techniques for renewable energy forecasting: A review. Renewable and Sustainable Energy Reviews, 104, 154-170.
  7. Wang, S., & Zhang, Y. (2018). An artificial intelligence-enabled energy management system for smart grid. IEEE Transactions on Industrial Informatics, 14(9), 4233-4240.