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Journal of Advanced Multidisciplinary Research

The Journal of Advanced Multidisciplinary Research (JAMR) is a peer-reviewed, international hybrid journal dedicated to advancing high-quality research across emerging technological, scientific, and societal disciplines. By bridging theory and practice, the journal offers a platform for global researchers, academics, and industry professionals to publish high-impact contributions.

3+ Articles
1 Volumes
1 Issues
Journal of Advanced Multidisciplinary Research Cover

Journal of Advanced Multidisciplinary Research

The Journal of Advanced Multidisciplinary Research (JAMR) is a peer-reviewed, international hybrid journal dedicated to advancing high-quality research across emerging technological, scientific, and societal disciplines. By bridging theory and practice, the journal offers a platform for global researchers, academics, and industry professionals to publish high-impact contributions.

Home › Articles › Volume 1, Issue 1 › AI-Driven Optimization Models for Hybrid Renewable E...
Research Article

AI-Driven Optimization Models for Hybrid Renewable Energy Grid Management


A Comparative Analysis of Machine Learning Approaches in Microgrid Control

Volume 1, Issue 1, Pages 116-130

Author

Eleanor VancePh.D.✲
Affiliation:
Department of Computer Science, Tech Institute of Science, Boston, Massachusetts, United States
Corresponding Author Email[✲]:

Corresponding Author

Eleanor Vance

Abstract

As power grids transition toward sustainable sources, integrating intermittent renewable energy systems poses significant stability and efficiency challenges. This paper presents an AI-driven framework designed to optimize energy distribution across hybrid renewable microgrids. Utilizing a combination of deep reinforcement learning and predictive forecasting models, our approach anticipates dynamic demand fluctuations while maintaining grid balance. Experimental results demonstrate a 14.2% reduction in overall transmission losses and a 22% improvement in peak-load response efficiency compared to traditional management strategies. These findings underscore the potential of scalable machine learning algorithms in modernizing clean energy infrastructure.

Author Keywords

Artificial Intelligence, Renewable Energy, Smart Grids, Machine Learning, Energy Efficiency.

DOI

10.1000/jamr.2026.01.999

Paid Access

Subscription Access Article. All rights reserved.

Funding

This research was funded by the National Sustainable Science Foundation (Grant No. NSSF-2025-8842) and supported by the Center for Clean Energy Innovation at Tech Institute of Science.

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Journal
Journal Journal of Advanced Multidisciplinary Research
Volume Volume 1
Issue Issue 1
Year 2026

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