Intelligent Solar Panel Control: A Comparative Study of Machine Learning for Optimal Energy Yield and Overheating Mitigation

The efficiency of solar panels depends not just on how much sunlight they receive, but also on how well they are protected from overheating. Traditional solar trackers often follow the sun mechanically, but they ignore important real-world factors like heat stress, rain, or night-time safety. To address this, we built a smart control system that uses machine learning to balance two goals at once: capturing as much sunlight as possible and preventing thermal damage to the panels. We created a high-quality synthetic dataset that mimics realistic conditions by combining real irradiance and temperature data, and then generated "ideal" tilt angles using a custom control logic. This logic included sun-following in bright daylight, protective tilting under high temperatures, steep adjustments during low light or rain, and safe positions at night. We also added sensor noise to reflect real-world challenges. Using this dataset, we trained and compared four different approaches: a reinforcement learning agent (DDPG), a deep neural network (MLP), a Random Cut Forest model, and an eXtreme Gradient Boosting (XGBoost) algorithm. The results were clear: while all models learned useful control policies, XGBoost consistently delivered the most accurate and robust predictions. The DDPG agent showed promise for more complex scenarios, learning directly from feedback without explicit labels. Our findings highlight the power of data-driven intelligence in solar energy systems. By moving beyond rigid rules, these models demonstrate how solar panels can actively "think" about their environment, making them more efficient, reliable, and resilient in real-world conditions. We also developed an interactive visualization to show how the best-performing model can make real-time adjustments, bringing this research closer to practical use.