Intelligent Green Energy Solutions: Optimizing Renewable Energy for Edge Cloud Computing

Client applications that need fast processing while using energy efficiently make it essential to add renewable energy to edge cloud systems. Our study recommends an advanced renewable energy integration solution that employs an LSTM network to improve the process. LSTM technology analyzes energy data sequences quickly to deliver correct predictions for renewable power outputs and user power needs. The study employs a Kaggle dataset to preprocess information and extract significant traits before building the model. The LSTM model produces energy trend forecasts that help us take quick actions to handle our energy supplies properly. Our suggested method achieved strong results with 92% accuracy along with precision and recall rates above 0.9 which combined to an F1-score of 0.92. Our model proves better than standard machine learning algorithms like XGBoost, Support Vector Machine, and Random Forest in predictions. The model enhances energy forecasting quality which enables better sustainable energy use with lesser strain on edge devices. Effective resource management and decision-making through this method helps edge networks reduce energy waste and better include renewable power sources. Our energy management model creates a suitable framework to use artificial intelligence for planning energy usage in eco-friendly and smart data processing networks.