Abstract
Maximizing output from renewable solar panels requires higher efficiency. Conventionally, such optimization techniques-MPPT (Maximum Power Point Tracking) along with heuristic algorithms-suffer significantly from slow adaptability and track sub optimality under dynamic environments. This article proposes a numerical modeling framework from hybrid AI models, combining physics-informed neural networks and RL for real-time optimization of orientation in solar panels. The methodology uses numerical modeling for precise energy transformation analysis, and deep learning-based optimization dynamically adjusts the angles of panels to maximize power output. A self-learning adaptive neural network is developed to improve tracking accuracy based on real-time irradiance and temperature variations. Moreover, an Edge AI architecture is introduced to make low-latency decisions with reduced dependency on cloud computation, thus improving the efficiency of the system. Besides, an advanced hybrid model based on CNN-LSTM is applied to solar energy forecasting for predictive control of the maximum energy yield. Experimental validation was performed using UTL 335W and 330W PV modules, where real-time data acquisition was followed by AI-driven optimization. Results show an increase in energy yield by 10-15% compared to traditional MPPT systems, while computations are performed 40-50% faster using AI-based numerical modeling. The proposed approach achieves 25% lower forecasting error (RMSE/MAE) and 30% reduced power consumption through Edge AI implementation. This study sets up a new paradigm for AI-integrated solar optimization, which ensures real-time adaptability and enhanced performance in practical deployment. The findings advance the intelligent solar tracking and set a new benchmark for AI-driven renewable energy management.