Time series forecasting holds a crucial role in any industrial or institutional context since the knowledge and the analysis of historical data allow organizations to make informed decisions and optimize processes. Accurate predictions are the foundation for valuable business strategy since they could result in additional revenues or cost savings. Therefore, the integration of Machine Learning modules into forecasting tasks and the continuous exploration and development of novel and domain-specific models represent areas of extensive and ongoing investigation. This work explores some of these tools as part of a project of energy saving realized by Spindox Spa and committed by an important telecommunication company.