Adaptive Maximum Power Point Tracking Using Machine Learning for Photovoltaic Systems
ID:203 View Protection:ATTENDEE Updated Time:2025-12-24 14:17:53 Hits:382 Online

Start Time:2025-12-30 15:00(Asia/Amman)

Duration:15min

Session:S9 Track 5: Emerging Trends of AI/ML » S9-2Track 5: Emerging Trends of AI/ML

No files

Abstract
Machine Learning (ML) technology for solar photovoltaic (PV) systems has emerged as a good option for increasing energy conversion efficiency under varying environmental conditions. This paper presents an adaptive Maximum Power Point Tracking (MPPT) approach using ML techniques to optimize real-time energy harvesting in PV systems. Traditional MPPT techniques such as Perturb and Observe (P&O) and Incremental Conductance are less efficient under rapidly varying irradiance and temperature. But the proposed ML-based MPPT scheme learns dynamically from environment and system data and forecast and optimize the operating point with a better accuracy. Various supervised learning models are compared on simulated data to identify the most accurate model with respect to accuracy, convergence speed, and computational cost. Experimental validation by MATLAB/Simulink confirms the better performance of the adaptive ML-based MPPT approach compared to conventional approaches. This paper illustrates the potential of intelligent control in improving the robustness and efficiency of PV systems in real-world applications
Keywords
Machine Learning, Maximum Power Point Tracking, adaptive control, Photovoltaic (PV) Systems, Real-Time Optimization, Renewable Energy, solar energy, environmental variability, Energy Efficiency, Smart Grid Integration
Speaker
Rakesh Kumar
GLA University GLA University

Submission Author
Rakesh Kumar GLA University
Kanchan Yadav GLA University Mathura
Submit Comment
Verify Code Change Another
All Comments
Important Date
  • Conference Date

    Dec 29

    2025

    to

    Dec 31

    2025

  • Dec 20 2025

    Draft paper submission deadline

  • Dec 31 2025

    Contribution Submission Deadline

  • Dec 31 2025

    Registration deadline

Sponsored By
United Societies of Science
Organized By
Zarqa University
Previous Conferences