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Abstract:
Background: Solar energy systems are expanding rapidly, which increases the need for efficient power extraction and accurate power forecasting. Conventional maximum power point tracking methods show reduced performance under varying meteorological conditions, which leads to power losses. Machine learning offers data driven models that adapt to changing environmental patterns and improve system performance.
Aim and Scope: The study aims to enhance solar power harvesting and forecasting through machine learning techniques. Multiple predictive models are evaluated to identify reliable approaches for photovoltaic system applications.
Methodology: Solar and meteorological datasets were preprocessed through data cleaning, removal of missing values, and extraction of time based features to support time series modeling. Linear regression, random forest, and artificial neural network models were trained and evaluated through mean absolute error, root mean square error, coefficient of determination, and graphical performance analysis to achieve accurate solar power prediction and effective maximum power point tracking.
Results: The proposed framework improves solar power collection and contributes to grid stability. Machine learning based models demonstrate fast and accurate maximum power point tracking with consistent power output and improved efficiency.
Conclusion: The integration of intelligent control and machine learning techniques enhances the efficiency and reliability of solar energy systems. The proposed approach supports increased power generation, improved grid stability, and stronger sustainability of renewable energy utilization
Keywords: ANFIS, MPPT, Photovoltaic systems, Renewable Energy, Switched quasi Z
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Copyright (c) 2025 Anwar Ali Sathio, Tahreem Hignora, Raja Vavekanand, Sameer Ali
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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