IJCOPE Journal

UGC Logo DOI / ISO Logo

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.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

AI-BASED INTELLIGENT ENERGY OPTIMIZATION IN SMART MICROGRIDS USING GENERATIVE ADVERSARIAL NETWORKS

B LikhithaPriya

K Naresh

Department of MCA, Annamacharya Institute of Technology and Sciences, Tirupati, Andhra Pradesh, India

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Abstract

Energy management in smart microgrids has become more dynamic and sophisticated due to the growing integration of renewable energy sources. Conventional optimization methods frequently fall short in managing uncertain energy generation and varying demand. This research introduces Generative Adversarial Networks (GANs), an artificial intelligence-based method for optimizing energy consumption in smart microgrids. The suggested system makes use of a generator and discriminator model to identify trends in energy usage and generate outputs with optimal energy distribution.

How to Cite this Paper

LikhithaPriya, B. (2026). AI-Based Intelligent Energy Optimization in Smart Microgrids using Generative Adversarial Networks. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.070

LikhithaPriya, B. "AI-Based Intelligent Energy Optimization in Smart Microgrids using Generative Adversarial Networks." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.070.

LikhithaPriya, B. "AI-Based Intelligent Energy Optimization in Smart Microgrids using Generative Adversarial Networks." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.070.

Search & Index

References

[1] Y. Chen, Y. Wang, D. Kirschen, and B. Zhang, "Model-free renewable scenario generation using generative adversarial networks," IEEE Transactions on Power Systems, vol. 33, no. 3, pp. 3265–3275, 2018.
[2] Y. Hu, Y. Li, L. Song, H. P. Lee, and N. Lu, "MultiLoad-GAN: A GAN-based synthetic load generation method considering spatial-temporal correlations," arXiv preprint arXiv:2210.01167, 2022.
[3] Y. Li, C. Zhao, and C. Liu, "Model-informed generative adversarial network for optimal power flow under uncertainty," arXiv preprint arXiv:2206.01864, 2022.
[4] Y. Yang, P. Liu, H. Ma, Z. Tao, Z. Tang, and Y. Zhou, "A GAN-and-Transformer-assisted scheduling approach for hydrogen-based multi-energy microgrid," Processes, vol. 13, no. 9, 2025.
[5] S. R. Kasimalla, K. Park, J. Hong, and Y. J. Kim, "Improving microgrid protection systems using GAN-generated

Ethical Compliance & Review Process

  • All submissions are screened under plagiarism detection.
  • Review follows editorial policy.
  • Authors retain copyright.
  • Peer Review Type: Double-Blind Peer Review
  • Published on: Apr 06 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Scroll to Top