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Adaptive Machine Learning Control Systems for Residential and Commercial PV Energy Harvesting
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
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.
Keywords
Photovoltaic Systems, Machine Learning, Adaptive Control, MPPT, Energy Harvesting, Smart Grid, Renewable Energy
References
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[3] Guntupalli, R., Sudhakaran, M., & Raj, P. A. (2022). Modeling & implementation of DRLA based partially shaded solar system integration with 3-ϕ conventional grid using constant current controller. Heliyon, 8(6), e09669. https://doi.org/10.1016/j.heliyon.2022.e09669
[4] Mustapha, Melhaoui & Rhiat, Mohammed & Oukili, Mohamed & Atmane, Ilias & Hirech, Kamal & Bossoufi, Badre & Almalki, Mishari & Alghamdi, Thamer A. H. & Alenezi, Mohammed. (2025). Hybrid fuzzy logic approach for enhanced MPPT control in PV systems. Scientific Reports. 15. 10.1038/s41598-025-03154-w.
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How to cite this paper
@article{1719717,
author = {Aniekan Oliseh Eno-Ibanga, Dr. Samir Abood},
title = {Adaptive Machine Learning Control Systems for Residential and Commercial PV Energy Harvesting},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {2},
pages = {1577-1580},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719717.pdf},
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.},
keywords = {Photovoltaic Systems, Machine Learning, Adaptive Control, MPPT, Energy Harvesting, Smart Grid, Renewable Energy},
month = {August},
doi = {https://doi.org/10.64388/IREV9I2-1719717}
}