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Adaptive Maximum Power Point Tracking Using Machine Learning for Photovoltaic Systems
ID：203 View protection：Participant Only Updated time：2025-12-24 14:17:53 Views：112 Online

Start Time：2025-12-30 15:00

Duration：15min

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

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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

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Important Dates

  • Conference date

    12-29

    2025

    -

    12-31

    2025

  • 12-30 2025

    Presentation submission deadline

  • 02-10 2026

    Draft paper submission deadline

  • 02-10 2026

    Registration deadline

Sponsored By

United Societies of Science

Organized By

扎尔卡大学

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