Forecasting Power Consumption in the DSL Access Network System based on Network Activities by using Machine Learning

Abstract

The widespread telecommunication networks and Internet services like Digital Subscriber Line (DSL) access has contributed to the growing demand of electricity. With environmental consciousness arising from non-renewable energy sources, coupled with rising demand, industries need to implement green strategies. This research focuses on predicting power consumption in DSL access networks using machine learning approaches. With an emphasis on Very High Bit Rate Digital Subscriber Line 2 (VDSL2) technology, the study investigates DSL modem’s power usage under an ideal and a bridge tap faulty conditions of copper cable along distances ranging from 100 to 1000 meters. The different levels of network activities are generated by IxChariot software and Pearson’s correlation techniques are used to analyse the relationship between power consumption and network activities. Combining the Train-Valid-Test and Random Forest algorithms, a predictive Machine Learning model is developed to forecast the power consumption based on relevant variables. The results show that power consumption tends to increase as the network activities are heavier and the proposed model presents a low minimum absolute error as a good model to forecast the power. It may aid Internet service providers to predict the optimum power limit in the network systems and pursue further investigation on how to reduce electricity use in the telecommunication field.