Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
The integration of artificial intelligence (AI) with renewable energy technologies is transforming sustainable power generation by improving control, efficiency, and adaptive decision-making. Traditional solar tracking and wind turbine systems, however, rely on static control algorithms that struggle to adapt to real-time environmental changes like cloud cover, shading, variable sunlight, and fluctuating wind patterns, leading to inefficiencies in energy harvesting. This study aims to design AI-enhanced control mechanisms for solar and wind systems to optimize energy yield and minimize maintenance costs. The methodology integrates machine learning (ML), deep reinforcement learning (DRL), and computer vision into solar and wind equipment control systems. For solar systems, real-time data from IoT sensors dynamically adjusts panel orientation, and predictive analytics forecast solar irradiation trends for proactive positioning. Whereas fuzzy logic controllers adjust performance reinforcement learning optimizes blade pitch and yaw in wind turbines according to wind conditions. A comparative analysis reveals that AI-driven systems have lower operating costs and a 25% increase in energy output. The results show how AI-powered wind turbine and solar tracking systems can adapt to changes in the environment increasing dependability and energy efficiency. This study establishes the groundwork for future developments in edge computing blockchain and hybrid AI models for decentralized energy distribution.
Keywords
Artificial Intelligence, Renewable Energy, Solar Tracking, Wind Turbines, Predictive Maintenance, Machine Learning, Deep Reinforcement Learning, IoT Integration.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Joe Arun C S J, Kishore Kunal, Suma R, Vasu Namala, Terrance Frederick Fernandez and Vairavel Madeshwaren;
Writing- Original Draft Preparation: Joe Arun C S J, Kishore Kunal, Suma R, Vasu Namala, Terrance Frederick Fernandez and Vairavel Madeshwaren;
Visualization: Joe Arun C S J, Kishore Kunal and Suma R;
Investigation: Vasu Namala, Terrance Frederick Fernandez and Vairavel Madeshwaren;
Supervision: Joe Arun C S J, Kishore Kunal and Suma R;
Validation: Vasu Namala, Terrance Frederick Fernandez and Vairavel Madeshwaren;
Writing- Reviewing and Editing: Joe Arun C S J, Kishore Kunal, Suma R, Vasu Namala, Terrance Frederick Fernandez and Vairavel Madeshwaren; All authors reviewed the results and approved the final version of the manuscript.
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Joe Arun C S J
Department of Business Analytics, Loyola Institute of Business Administration, Chennai, Tamil Nadu, India.
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Cite this article
Joe Arun C S J, Kishore Kunal, Suma R, Vasu Namala, Terrance Frederick Fernandez and Vairavel Madeshwaren, “AI Driven Solar Tracking and Smart Wind Turbines - Enhancing Renewable Energy Efficiency Through Adaptive Machine Learning Algorithms”, Journal of Machine and Computing, vol.5, no.4, pp. 2316-2327, October 2025, doi: 10.53759/7669/jmc202505180.
