Energy Saving

We design and implement, together with our customers, solutions and architectures based on Digital Twins, which allow, by exploiting Machine Learning Prediction models and Artificial Intelligence algorithms, to control and analyze a network and the services it provides in order to improve its performance dynamically, ensuring the optimization of various parameters: Energy Saving, CO2 emission and Trade-off between Energy Saving and Network Performance.

Energy Saving

Monitoring and Dynamic Optimization of Network Performance
Thanks to predictive Machine Learning models and Artificial Intelligence algorithms, the system is able to analyze the behavior of the network in real time and anticipate critical issues. This allows for proactive intervention to optimize performance, latency and capacity, dynamically adapting to changes in traffic or anomalous conditions. The network thus becomes *self-adaptive*, improving operational efficiency and ensuring stable service quality.

Reduction of Energy Consumption and Environmental Impact
The Digital Twin continuously simulates network operating scenarios, allowing for the identification and implementation of more energy-efficient configurations. This leads to a significant reduction in consumption, while maintaining service levels unchanged. The targeted use of resources also translates into lower CO₂ emissions, contributing to ESG (Environmental, Social and Governance) objectives and environmental sustainability.

Management of the Trade-off between Efficiency and Performance
Reducing energy consumption is not always compatible with maintaining network performance. The system is able to evaluate in real time the trade-off between energy savings and quality of service, suggesting or applying optimal configurations based on operational priorities. This intelligent balancing ensures informed and contextualized decisions, adaptable to different needs (e.g. peak hours vs. night hours).

Applications in specific sectors

Telco

In the telecommunications sector, Digital Twins allow to create a virtual and real-time replica of the entire network, from core infrastructures to access components. This digital twin allows you to constantly monitor the status of the network, anticipate malfunctions, simulate the impact of new configurations or services, and dynamically optimize the allocation of resources. Thanks to predictive algorithms, it is possible to prevent congestion, improve the user experience and drastically reduce energy consumption. Furthermore, the ability to intelligently balance performance and sustainability makes the Digital Twin a strategic tool for addressing the challenges of 5G network modernization and beyond.

Industries

In the industrial world, the adoption of Digital Twins allows for advanced management of plants and production lines. Each machine, sensor and process can be digitally replicated to enable continuous, predictive and adaptive control of operations. Artificial intelligence models allow you to predict failures, optimize maintenance (from reactive to predictive) and improve the energy efficiency of the entire plant. Furthermore, the digital twin allows to simulate alternative production scenarios, identifying the best compromise between performance, costs and environmental impact. This approach enables a true digital transformation, oriented towards flexibility, resilience and sustainability.