Title: Machine learning-driven innovations for energy efficiency engineering systems empower greener technologies

Authors: R. Regin; K. Selvamani; S. Kanimozhi; Pallavi Ahire; Swakantik Mishra; Sukhwinder Sharma; Sushma Rani

Addresses: Department of Computer Science and Engineering, SRM Instıtute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, CEG Campus, Anna University, Chennai, Tamil Nadu, India ' Department of Artificial Intelligence and Data Science, Panimalar Engg College, Chennai City Campus, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, Pimpri Chinchwad University, Mohitewadi, Maharashtra, India ' Department of Electrical and Electronics Engineering, Centurion University of Technology and Management, R. Sitapur, Odisha, India ' Department of Data Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India ' Department of Computer Science and Engineering (AI&ML), MVGR College of Engineering (A), Vizianagaram, Andhra Pradesh, India

Abstract: The research investigates the role of high-energy electronics as a key player in the strength efficiency and sustainability sector. In addition, we look at recent developments in power electronics, including advanced semiconductor materials and novel topologies with machine learning-enhanced control strategies to bring technological innovations towards climate-smart technology. Our process combines a complete literature research and architectural analysis to illuminate innovative power electronics through machine learning and data-driven optimisation. Where the consequences of this study not only show substantial enhancements in power performance and sustainability, but also strengthen the case for embedding advanced energy electronics across myriad programs perfectly aligned with eco-green tech. The discussion extends to how our results may influence the integration of renewable electricity, industrial strategies, and environmental sustainability through transformational system learning-driven innovations. This paper outlines a scenario where green technology meets machine learning to usher in a new era of energy efficiency for a greener planet, highlighting power electronics' immense potential and future direction. Current constraints are noted as side comments.

Keywords: sustainable power systems; machine learning optimisation; advanced power electronics; renewable energy integration; energy efficiency solutions; green technology innovations; smart grid technologies; eco-friendly semiconductor materials.

DOI: 10.1504/IJESMS.2025.148247

International Journal of Engineering Systems Modelling and Simulation, 2025 Vol.16 No.5, pp.255 - 269

Received: 05 Feb 2024
Accepted: 14 Aug 2024
Published online: 01 Sep 2025 *

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