Trends, Effects, and Future Outlook for the Integration of Artificial Intelligence Technologies in the Energy Sector: The Role of Open Innovation

Article Sidebar

Page Numbers: 143-158
Download PDF
Published: Apr 12, 2025
Digital Object Identifier: 10.58578/mjaei.v2i2.5391
Save this to:
Save to Mendeley Download RIS

Article Metrics:
Viewed: 207 times
Downloaded: 154 times
Article can trace at:

Author Fee:
Free Publication Fees for Foreign Researchers (USD 0.00)
Check for article on SINTA:

LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers

Connected Papers:
Connected Papers

Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.

Authors:
Elijah Omoniyi Fatokun1
Copyright :

Authors retain copyright and grant the journal right of first publication.


Main Article Content

Elijah Omoniyi Fatokun
Ajayi Crowther University, Nigeria

Abstract

This paper explores the integration of Artificial Intelligence (AI) in the energy sector, focusing on its impact on operational efficiency, cost reduction, and environmental sustainability. It examines how open innovation fosters collaboration among energy companies, startups, and research institutions to accelerate AI adoption. Despite challenges such as data privacy, regulatory barriers, and infrastructure needs, AI technologies are poised to enhance energy optimization and support global goals like net-zero emissions and increased renewable energy penetration. The paper also looks ahead to the future of AI in energy, highlighting the potential of quantum computing, reinforcement learning, and advanced neural networks in driving further innovation.

Keywords:
Share Article:

Citation Metrics:

 Scopus



Downloads

Download data is not yet available.

Scopus Citation Data

Data source Crossref
1
citations
Check Secondary Documents in Scopus
Open this article in Scopus, then check the Secondary documents tab. Use Manual Citation Fallback only for counts you have verified manually.
Open in Scopus
Similar Scopus Articles
Scopus
  1. Berenjian K. (2027)
    Impact of Mild Traumatic Brain Injury (mTBI) on CYP2D6 Activity and the Restorative Effects of Melatonin and Vitamin C Supplementation
    Iranian Journal of Pharmaceutical Research, 26(1)
  2. Miyazono S. (2027)
    Improved Efficiency and Lesion Detection in Small Bowel Capsule Endoscopy Using the Open-Source Artificial Intelligence Model SEE-AI
    Den Open, 7(1)
  3. Bhagyasree M.R. (2027)
    CONSUMER HEALTH ENTOMOLOGY-A FORTIORI INDUSTRIAL SCIENCE OF EMERGING PUBLIC HEALTH SIGNIFICANCE, WITH EMPHASIS ON ITS COMMERCIAL, BEHAVIOURAL AND SOCIAL CONSIDERATIONS
    Indian Journal of Entomology, 89(1), 122-127

Article Details

How to Cite
Fatokun, E. O. (2025). Trends, Effects, and Future Outlook for the Integration of Artificial Intelligence Technologies in the Energy Sector: The Role of Open Innovation. Mikailalsys Journal of Advanced Engineering International, 2(2), 143-158. https://doi.org/10.58578/mjaei.v2i2.5391

Creative Commons License
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

References

Amin, S. M., & Lee, K. (2021). Artificial intelligence and its applications in the energy sector: A review. Energy Reports, 7(2), 94-110. https://doi.org/10.1016/j.egyr.2021.03.013
Baker, L., Martin, P., & Singh, R. (2020). Artificial intelligence applications in renewable energy. Renewable and Sustainable Energy Reviews, 122, 109-119. https://doi.org/10.1016/j.rser.2020.109719
Baker, L., Martin, P., & Singh, R. (2020). Artificial intelligence applications in renewable energy. Renewable and Sustainable Energy Reviews, 122, 109-119. https://doi.org/10.1016/j.rser.2020.109719
Chesbrough, H., & Bogers, M. (2020). Explicating open innovation: Clarifying an emerging paradigm for understanding innovation. Research Policy, 49(8), 104019. https://doi.org/10.1016/j.respol.2020.104019
Deloitte. (2023). AI and the future of energy: Opportunities and challenges. Deloitte Insights. https://www2.deloitte.com/global/en/insights.html
Gonzalez, A., Garcia, M., & Swales, N. (2022). Open innovation in the energy sector: The role of partnerships and collaborative technologies. Energy Strategy Reviews, 37, 100644. https://doi.org/10.1016/j.esr.2022.100644
Jordan, M., Chung, S., & Patanakul, P. (2022). Quantum computing for energy optimization: Opportunities and challenges. Energy Reports, 8, 118-132. https://doi.org/10.1016/j.egyr.2021.11.009
Li, Y., Wang, X., & Zhang, X. (2018). The role of AI in optimizing renewable energy systems. Journal of Cleaner Production, 198, 13-24. https://doi.org/10.1016/j.jclepro.2018.07.198
Liu, Y., Zhang, Y., & Sun, X. (2020). Artificial intelligence in energy storage management: A review. Energy Reports, 6, 322-336. https://doi.org/10.1016/j.egyr.2020.05.005
Liu, Y., Zhang, Y., & Sun, X. (2021). Artificial intelligence in energy storage management: A review. Energy Reports, 6, 322-336. https://doi.org/10.1016/j.egyr.2020.05.005
Mastrangelo, A., Guerra, V., & Marletta, L. (2020). AI for optimization in renewable energy systems: A comprehensive review. Energy, 207, 118104. https://doi.org/10.1016/j.energy.2020.118104
Shao, M., Yang, X., & Yang, H. (2021). Fault detection and diagnosis of solar energy systems using AI-based techniques. Renewable and Sustainable Energy Reviews, 135, 110083. https://doi.org/10.1016/j.rser.2020.110083
Shen, W., Li, X., & Yang, Y. (2023). Deep learning for energy system optimization: A review of recent developments. Renewable and Sustainable Energy Reviews, 135, 110084.
Tian, H., Zhang, Z., & Chen, J. (2022). AI-enhanced demand response in smart grids: A review of recent developments. Electric Power Systems Research, 193, 107069. https://doi.org/10.1016/j.epsr.2021.107069
Wang, W., Li, S., & Zhang, Y. (2020). Optimizing battery life in energy storage systems using machine learning algorithms. Journal of Energy Storage, 27, 101019. https://doi.org/10.1016/j.est.2020.101019
Zhang, W., Sun, Y., & Wang, L. (2020). Machine learning-based fault detection for smart grid systems. Electric Power Components and Systems, 48(9), 1023-1033. https://doi.org/10.1080/15325008.2020.1798695
Zhang, X., Zhang, L., & Zhang, J. (2021). AI-based optimization techniques for the modern power grid: Challenges and solutions. IEEE Access, 9, 5362-5375. https://doi.org/10.1109/ACCESS.2021.3051834
Zhou, X., Li, Y., & Wang, Z. (2021). Predictive maintenance for wind turbines using AI models: A review. Renewable Energy, 171, 484-495. https://doi.org/10.1016/j.renene.2021.01.023
Zhou, X., Li, Y., & Wang, Z. (2021). Predictive maintenance for wind turbines using AI models: A review. Renewable Energy, 171, 484-495.
Zhou, X., Li, Y., & Wang, Z. (2021). Predictive maintenance for wind turbines using AI models: A review. Renewable Energy, 171, 484-495. https://doi.org/10.1016/j.renene.2021.01.023

Explore Our Journals
Find the most suitable journal for your research. If this journal does not fully align with the scope of your manuscript, we invite you to explore our wider portfolio of journals covering diverse fields of study. Please select one of the journals below to identify the most appropriate publication platform for your work.