Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Hosakote Shankara, Kavitha
Unknown Affiliation
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
An efficient load-balancing in machine learning-based DC-DC conversion using renewable energy resources Shankara, Kavitha Hosakote; Hosakote Shankara, Kavitha; Srikantaswamy, Mallikarjunaswamy; Nagaraju, Sharmila
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp307-316

Abstract

This paper introduces the machine learning-based DC-DC conversion algorithm (ML-DC2A), a pioneering machine learning (ML) approach designed to enhance load-balancing in DC-DC conversion systems powered by renewable energy sources. Traditional control strategies, such as pulse-width modulation (PWM), maximum power point tracking (MPPT), and basic voltage and current controls, are foundational yet often fall short in adapting to the rapid fluctuation’s characteristic of renewable energy supply. The ML-DC2A optimizes crucial performance indicators including conversion efficiency, reliability, adaptability to energy supply variability, and response time to changing loads. By leveraging predictive analytics and adaptive algorithms, it dynamically manages the conversion process, offering superior performance over traditional techniques. A notable drawback of conventional methods is their inability to anticipate and adjust to real-time changes in energy availability and demand, leading to inefficiencies and potential system instability. The proposed ML-DC2A addresses these challenges by incorporating a sophisticated ML framework that predicts future energy scenarios and adaptively adjusts system parameters to maintain optimal performance. Initial results highlight the transformative potential of integrating ML into renewable energy conversion systems, promising significantly enhanced efficiency and system resilience, thus marking a significant step forward in sustainable energy management.
Co-Authors Nagaraju, Sharmila Shankara, Kavitha Hosakote Srikantaswamy, Mallikarjunaswamy