Enhancing Energy Efficiency in 5G Radio Access Networks by Digital Twin and Machine Learning

5G communication system is considered a fundamental part of the Industry 4.0 vision. The escalating energy consumption of 5G Radio Access Networks poses significant challenges. This paper presents a novel approach to improve energy efficiency in 5G Radio Access Networks through a Digital Twin-assisted orchestration framework. Leveraging the Open Radio Access Network, Asset Administration Shell for semantic modeling and Machine Learning techniques, the proposed system enables real-time monitoring, intelligent decision-making, and automated control for dynamic energy saving. Key Performance Indicators and Key Value Indicators are defined and mapped to guide optimization strategies. Experimental evaluation shows the capability of the proposed system to predict energy consumption, consolidate traffic, and manage radio units to reduce power usage without degrading network performance or user Quality of Service.