Page 302 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

offloading actions that reduce power usage without compromising service quality. Integration
with platforms like LEXI (Open source driven) ensures that digital twins are dynamically updated
with real-world device data, while connectivity via Kafka and MQTT supports real-time ingestion
and orchestration.

Ultimately, this system allows telcos to move beyond static power-saving profiles toward a
fully adaptive, context-aware energy management approach. The platform not only improves
operational sustainability and aligns with global net-zero goals but also creates tangible cost
savings by reducing unnecessary power draw especially during off-peak conditions or under
redundant infrastructure states.

Objectives and Aims:
This use case aims to reduce unnecessary energy usage in telecom operations while maintaining
or even improving Quality of Service (QoS). By leveraging AI-based predictions and simulations,
the network can become more adaptive and responsive to changing conditions, enabling
automated decision-making that balances performance with energy efficiency. The solution
also supports predictive maintenance and network self-adaptation, contributing to long-term
sustainability objectives and significant operational cost reductions.

Flow Diagram (Functional View)

Sub-Scenarios:

This solution leverages AI to optimize energy usage through intelligent network management.
User admission is dynamically controlled based on real-time load, energy constraints, and
QoS requirements, while configurations like antenna tilt and bandwidth are automatically
adjusted using predicted usage patterns. The system deactivates idle resources during low-
traffic periods to save power and employs traffic load forecasting based on historical data,
events, and time-of-day trends. Additionally, energy-aware scheduling shifts non-critical tasks
to off-peak hours or times when cleaner energy is available, ensuring efficient and sustainable
network operations.

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