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Nokia AVA - Telco network energy efficiency

How AI reduces the CO2 emissions and energy costs of telco networks

AI-as-a-Service can reduce the carbon footprint of service providers by 30 percent. Now.

AI-based energy management automation has proven itself to be the fast track to shrinking the carbon footprint of telco networks. It can reduce energy costs and carbon footprint by 30% with no negative impact on performance or end customer experience.

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Why telco network CO2 emissions have become an urgent business problem

Communication Service Providers (CSPs) are pressured to choose the best strategies that will enable them to shrink the carbon footprint of their networks and achieve their sustainability commitments. Pressure doesn’t come only from the Paris Accord, it also comes from the cost side. Energy consumption already accounts for about half of all telco network operations costs, making it a very large cost block that requires attention at the corporate level.

Intelligent automation of energy efficieny

And the problem is expanding, driven by exponential traffic growth and the rising number of sites. For these reasons, telcos need to act urgently to slash their network energy bills and boost sustainability.

CSPs have considerable scope to cut energy consumption. Data transfer accounts for only around 15 percent of the energy used by a mobile network. That means the rest is simply wasted because equipment or whole sites sit idle during periods of low traffic and auxiliary systems — especially cooling systems — are often misconfigured.

Where does the energy go?

where does the energy go

How Artificial Intelligence reduces the carbon footprint of telco networks

AI can optimize power savings for every base station in every sector, every day. For example, conventional energy-saving methods operate based on pre-defined static shutdown windows and are not able to handle complex savings scenarios. Nokia AVA for Energy Efficiency, a data-driven AI based energy solution for telcos, predicts low traffic periods and shuts down resources such as frequency carriers or even whole sites in case of overlapping coverage. We have seen that AI-based solutions achieve two to five times more savings than non-AI systems that perform temporary shutdowns based on fixed schedules.

Dynamic shutdowns only account for network elements. At most radio sites, 50% of energy consumption goes to auxiliary components such as fans, cooling systems, lighting and other power supplies. To make sure those also perform as efficiently as they should, AI-powered energy consumption management has to cover both active radio and passive equipment — benchmarking energy trends to spot performance anomalies in historically “invisible” passive equipment that could be draining energy and might need to be reconfigured or replaced.

How to make AI-based energy management for networks happen

Because of its software nature, AI-based energy efficiency solutions can be deployed in just a few weeks without major upfront investment — especially thanks to outcome-based Software-as-a-Service (SaaS) business models, which enable you to pay only for the energy savings outcomes you actually achieve. Implementing the AI system over a public cloud can make it even faster by easing the processing and analysis of the large volume and velocity of network data.

Real-world experience shows that AI-driven automation can be implemented in a matter of weeks, making it the most immediate opportunity for large cost savings. We have seen power savings in real networks from seven percent to 30 percent. Since Nokia AVA for Energy Efficiency is multi-vendor these savings apply to not just the equipment of a single RAN vendor but the whole network.

As a total-site software-based telco energy solution, an AI system can be set up quickly to minimize all kinds of energy waste.

Nokia AVA for Energy Efficiency capabilities for telco networks

Harness AI for more sustainable energy use

Benefits of AI-based energy management for mobile networks

Energy-efficiency

Up to 30% energy savings and less CO2 emission for telco radio networks

Optimize

Energy cost savings are achieved within weeks due to quick setup times

Efficiency

2-5x more power savings than non-AI systems that perform temporary shutdowns based on fixed schedules

Energy-efficiency

Up to 70% less energy consumption
for cooling

Antenna

Minimizes all kinds of energy waste
across active radio and auxiliary equipment

Operate

No impact on network performance while dynamically shutting down network resources

Data-centre

No large-scale deployments
and hardware changes needed

Did you know?

  • Companies with low GHG emissions and high sustainability are rated more than 15% higher than those with above-average CO2 emissions

  • Power consumption accounts for 50% of network operations costs of CSPs globally

  • Radio access network (RAN) accounts for some 80% of mobile network energy consumption and carbon footprint

  • 50% of energy consumption goes to auxiliary components such as cooling systems

How China Mobile is using Nokia AI for energy-efficient 5G

China Mobile needed a solution that would cut energy consumption and control costs without compromising the customer experience

Download the case study

Your network is wasting energy. AI can change that in a flash.

For any CSPs out there who have been waiting for the right solution to come along and help them meet their sustainability goals with energy savings: the wait is over.

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