This project aims to predict energy consumption in 5G base stations using Supervised Learning Regression techniques. The goal is to model and estimate the energy consumed by different 5G base stations based on various features such as load, transmitting power, and energy-saving. Machine learning (ML) has emerged as the transformative force that enables communications service providers (CSPs) to shift from reactive problem-solving to predictive, data-driven operations that anticipate issues before they impact customers. BTSs are geographically scattered across the networks service area and thousands of fault indicating alarms are generated by a typical BTS on a daily basis. By leveraging AI-driven insights, telecom providers can move from reactive fixes to. The World Economic Forum's AI Transformation of Industries initiative seeks to catalyse responsible industry transformation by exploring the strategic implications, opportunities and challenges of promoting artificial intelligence (AI)-driven innovation across business and operating models.
[PDF Version]