Adaptive Machine Learning Control Systems for Residential and Commercial PV Energy Harvesting

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Chinese explanation / 中文解读

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Original abstract

Photovoltaic (PV) energy harvesting has become a cornerstone of the global shift toward renewable energy, but its efficiency remains limited by environmental variability, load fluctuations, and system degradation. Traditional Maximum Power Point Tracking (MPPT) algorithms, such as Perturb and Observe or Incremental Conductance, perform reasonably well under stable conditions but struggle with rapid irradiance changes, partial shading, and nonlinear system dynamics. This has pushed researchers toward adaptive machine learning (ML). They are control systems that can learn from real-time data and dynamically adjust control strategies. This paper explores how adaptive Machine learning frameworks are being applied to optimize Photovoltaic energy harvesting in both residential and commercial settings. We examine the conceptual basis of adaptive control, review current models, including neural networks, reinforcement learning, and hybrid approaches, and discuss the practical implementation challenges they pose. The review shows that machine learning-based controllers outperform conventional methods in energy yield by 5-15% under variable conditions, while improving fault detection and predictive maintenance. However, issues around computational cost, data requirements, and system interpretability remain. The paper concludes that adaptive Machine Learning is not a replacement for conventional control but a complementary layer that enhances robustness and efficiency as PV systems become more decentralized and complex.

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