Adaptive Control for Solar Energy Optimization Leveraging Recurrent Neural Networks

In recent days, research and development focusing on capturing techniques of renewable energy resources from nature, especially the quest for maximum solar energy optimization process is predominantly becoming popular with dynamic optimization techniques. This proposed work proves the novel method for optimizing solar panel energy output by implementing solar panel modules with Recurrent Neural Networks (RNNs) to forecast and adapt to the changes in environmental circumstances. Recent research focuses on mechanical trackers and fixed angle adjustment of solar panels due to the continuous changes in solar radiation and climatic variations which restrict efficiency reduction and less than ideal energy capture. The proposed work uses recurrent neural networks to forecast the real-time variables affecting solar panel performance because of their ability to analyze time-dependent data. The proposed work examines the past environmental weather data, solar irradiance pattern, and panel temperature levels, to predict and dynamically adjust the solar panel orientation and operating parameters to optimize the energy capture from solar. This solar panel orientation adjustment provides economical, maintenance free and excellent solutions to mechanical tracking systems for solar farms and dispersed solar installations[1]. The recurrent neural network gradually raises its prediction accuracy by continuous learning of previous data which proves the notable increase in energy yield and 10% to 15% increase in energy capture under changing environmental conditions compared to traditional techniques, The simulation results show that the implementation of RNN with solar panel optimization technique improves the solar energy efficiency & promoting more dependable and sustainable renewable energy sources which can be applicable for both large-scale solar power plants and small-scale residential systems.