Solar Energy PINN Prediction

This research-driven project introduces a Physics-Informed Neural Network (PINN) framework for optimizing solar energy prediction in Indonesia. By integrating fundamental solar radiation laws and thermal efficiency equations directly into the neural network's loss function, the system overcomes the intermittency challenges of renewable energy and data scarcity. The model achieves an R² of 0.834, significantly outperforming traditional data-only models while maintaining strict adherence to physical conservation laws.