AI-Powered Network Energy Optimization Machine Learning Approaches to Reducing Network Power Consumption

Communication and IoT networks require efficient energy optimization to extend the node lifetime, enhance the throughput, and minimize the operating cost. In contrast to the previous research which samples individual practices, this paper suggests an integrated AI-based approach consolidating several smart modules into one pipeline. The structure starts with a Federated Monitoring Framework of secure low overhead data collection across distributed nodes. Hybrid Entropy Genetic Algorithm is used to perform preprocessing of features, which reduces input dimensions and improves efficiency. A hybrid LSTM-GRU + Temporal Convolutional Network (TCN) model allows predicting network energy consumption with low RMSE and a short latency in real-time. Particle Swarm Optimization-Deep Reinforcement Learning (PSO-DRL) can dynamically manage routing and partial replacement of component swapping to further enhance efficiency, and Reinforced Federated Learning (RFL) ensure ongoing model updates at the edge in a privacy-preserving way. The simulation results of the large-scale network cases indicate that proposed framework is infinitely better in comparison with the baseline models in terms of consuming less energy, high ratios of delivering packets, as well as higher throughput. The findings demonstrate the potentials of the proposed pipeline in providing sustainable, intelligent and adaptive network operations.