Neural Network-Driven Optimization for Cost Minimization in Telecom Energy Systems with Nonlinear Converter Efficiencies

This paper presents an enhanced method for energy management in telecommunication networks, prioritizing accurate modeling of converter efficiencies with neural networks. Diverging from traditional models, our approach utilizes manufacturers' data on converter efficiencies as a function of delivered power to train a neural network. This advanced modeling is integrated into the optimization framework, improving the predictive capabilities of energy management without directly affecting cost. Trained on a diverse set of scenarios, the model adeptly handles the nonlinear efficiency behaviors of converters. The results validate the critical role of detailed efficiency modeling in telecommunication energy systems and demonstrate the role of neural networks in refining energy management. Additionally, the study underscores the significance of precise energy conversion modeling for the integration of renewables, marking a step forward in sustainable telecom operations.