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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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Volume 02, Issue 05

Published on: May 2026

A REVIEW OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN ELECTRICAL ENGINEERING

Ashish Kumar Tiwari

Dr. Mamta Tholia Khileri Rakhi Kamra

Department of Electrical Engineering Maharaja Surajmal Institute of Technology

Article Status

Plagiarism Passed Peer Reviewed Open Access

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Abstract

Artificial intelligence has revolutionized the disci-pline of electrical engineering by facilitating intelligent decision-making, automation, and superior system performance. This review article provides an in-depth examination of key artificial intelligence methodologies—including machine learning, deep learning, and neural networks—and their implementations across diverse subfields such as power system optimization, renewable energy integration, fault detection, smart grids, energy manage-ment, and electric vehicles.

This review elucidates the enhanced efficiency, reliability, and predictive prowess of AI-driven models relative to conventional rule-based methodologies. It further examines principal chal-lenges, encompassing data reliance, substantial computational demands, and intricate deployment complexities. Moreover, the article delineates prospective advancements wherein AI facilitates the evolution of autonomous, self-healing, and sustainable energy infrastructures. Collectively, the incorporation of AI in electrical engineering is anticipated to exert a pivotal influence on the development of next-generation intelligent power systems.

How to Cite this Paper

Tiwari, A. K. (2026). A Review of Artificial Intelligence Applications in Electrical Engineering. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.563

Tiwari, Ashish. "A Review of Artificial Intelligence Applications in Electrical Engineering." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.563.

Tiwari, Ashish. "A Review of Artificial Intelligence Applications in Electrical Engineering." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.563.

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References


  1. L’Heureux, K. Grolinger, and M. A. M. Capretz, “Transformer-Based Model for Electrical Load Forecasting,” Energies, vol. 15, no. 14, 2022.

  2. Zhao, C. Xia, L. Chi, Z. Chen, and X. Wang, “Short-Term Load Forecasting Based on the Transformer Model,” Information, vol. 12, no. 12, 2021.

  3. Zhang et al., “An Improved Informer Model for Short-Term Load Forecasting Considering Periodic Characteristics,” Frontiers in Energy Research, vol. 10, 2022.

  4. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.

  5. Wang, Q. Chen, T. Hong, and C. Kang, “Review of Smart Meter Data Analytics: Applications and Challenges,” IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 3125–3148, 2019.


  6. Hong and S. Fan, “Probabilistic Electric Load Forecasting: A Tutorial Review,” International Journal of Forecasting, vol. 32, no. 3, pp. 914–938, 2016.

  7. Dudek, “Neural Networks for Pattern-Based Short-Term Load Fore-casting,” Neurocomputing, vol. 205, pp. 64–74, 2016.

  8. Lago, F. De Ridder, and B. De Schutter, “Forecasting Spot Electricity Prices: Deep Learning Approaches,” Applied Energy, vol. 221, pp. 386–405, 2018.

  9. Vaswani et al., “Attention Is All You Need,” Advances in Neural Information Processing Systems, vol. 30, 2017.

  10. K. Nti, M. Teimeh, and O. Nyarko-Boateng, “Electricity Load Forecasting: A Review,” Journal of Electrical Systems and Information Technology, vol. 7, no. 13, 2020.

Ethical Compliance & Review Process

  • All submissions are screened under plagiarism detection.
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  • Peer Review Type: Double-Blind Peer Review
  • Published on: May 18 2026
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