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Numerical modeling and neural network optimization for advanced solar panel efficiency.

Udit Mamodiya1, Indra Kishor2, Mohammed Amin Almaiah3

  • 1Faculty of Engineering and Technology, Poornima University, Jaipur, Rajasthan, India.

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Summary
This summary is machine-generated.

This study introduces a hybrid AI framework combining physics-informed neural networks and reinforcement learning for real-time solar panel optimization. The AI system enhances energy yield by 10-15% and improves tracking speed compared to traditional methods.

Keywords:
CNN-LSTMEdge AINeural network optimizationNumerical modelingPhysics-informed neural networks (PINNs)Reinforcement learningSolar forecastingSolar panel efficiency

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Engineering
  • Numerical Modeling and Simulation

Background:

  • Conventional Maximum Power Point Tracking (MPPT) and heuristic algorithms struggle with slow adaptability and sub-optimal performance in dynamic solar environments.
  • Maximizing energy output from solar panels necessitates higher efficiency and improved real-time optimization techniques.

Purpose of the Study:

  • To propose a novel numerical modeling framework using hybrid AI for real-time solar panel orientation optimization.
  • To enhance solar energy yield and system efficiency through adaptive AI-driven control.
  • To reduce computational latency and cloud dependency via an Edge AI architecture.

Main Methods:

  • Integration of physics-informed neural networks (PINNs) with reinforcement learning (RL) for dynamic angle adjustment.
  • Development of a self-learning adaptive neural network for improved tracking accuracy based on real-time environmental data.
  • Implementation of an Edge AI architecture for low-latency decision-making.
  • Application of a CNN-LSTM hybrid model for solar energy forecasting and predictive control.

Main Results:

  • Achieved a 10-15% increase in energy yield compared to traditional MPPT systems.
  • Demonstrated 40-50% faster computation speeds using AI-based numerical modeling.
  • Reduced forecasting error (RMSE/MAE) by 25% and power consumption by 30% via Edge AI.
  • Experimental validation using UTL 335W and 330W PV modules confirmed AI-driven optimization effectiveness.

Conclusions:

  • The proposed hybrid AI framework offers a new paradigm for intelligent solar optimization, ensuring real-time adaptability and enhanced performance.
  • The study sets a new benchmark for AI-driven renewable energy management and intelligent solar tracking.
  • The integration of numerical modeling, deep learning, and Edge AI significantly improves the efficiency and responsiveness of solar energy systems.