Optimizing Green Energy Systems with Deep Learning Techniques for Enhancements in Efficiency and Sustainability

The aim is to optimize the efficiency and sustainability of green energy systems with advanced deep learning techniques. As steady growth in renewable sources like solar and wind energy takes place, accurate predictions of energy production and consumption are necessary for the optimal performance and stability of a grid. The holistic dataset, which gathers solar radiation, wind speed, temperature, humidity, and electricity consumption patterns from a 100 MW solar panel installation, is collected. Two hybrid deep learning models called Convolutional Neural Network with Long ShortTerm Memory (CNN-LSTM) and Convolutional Neural Network with Gated Recurrent Units (CNN-GRU) are used in this research. To analyze the performance, accuracy, precision, recall, F1-score, and error rates (MAE, MSE, RMSE) were computed. It seems clear that a high prediction accuracy of 97.76% has been presented by the architecture CNN-LSTM as compared to the CNN-GRU by its accuracy of 94.56%. The CNN-LSTM even finds reflection in better error rates and higher precision, recall, and F1 scores. In practice, the CNNLSTM model can be used for dynamic load forecasting, grid management, and optimizing renewable energy integration, thus ensuring more efficient, cost-efficient, and stable energy use in the grid.