Nokia AVA – AI energy efficiency for telco
How AI reduces the CO2 emissions and network 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.
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.
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 consumption management 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.
Hear firsthand how other CSPs are using AI to achieve their energy-efficiency targets.
How to make AI 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 - AI Energy Efficiency capabilities for telco networks
AI models and machine learning predict network traffic and adjust shutdown times dynamically to extend savings windows compared to static schedules, avoiding any degradation of network performance. Supervised learning permanently adjusts predictions based on the latest load and network performance feedback. The AI solution enables differentiated energy saving plans for different areas such as rural or urban. The result is a coherent energy control that dynamically adapts energy consumption to traffic levels while maintaining a premium user experience. 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.
Dynamically adjusts the azimuth and elevation angle of antennas to reduce energy consumption further at given capacity and coverage requirements. Due to full automation there is no need for personnel to go to the station to adjust the antenna.
Hardware can continue to use power even when it’s not in use unless it’s physically shut off. Hard power saving control makes sure equipment that isn’t needed is fully shut down, which can further increase energy savings by up to 50%. With the accurate predictions of the AI engine the unplugged equipment is powered up right in time when it is again needed.
Telco AI analytics benchmarks energy trends and spots anomalies in the performance of historically “invisible” passive equipment such as batteries or air conditioners that could be draining energy and need to be reconfigured or replaced. Drawing from sources including radio networks, connected devices, weather data, asset databases, energy bills and alarms, Nokia AVA for Energy Efficiency uses advanced analytics to identify patterns and trends and provide benchmarks. This data feeds into a dashboard that displays anomalies such as faulty equipment, leakage or theft.
Intelligent Fresh Air Ventilator exchanging hot and cold air inside and outside the compute room and intelligent air conditioning can massively reduce the operating time of cooling systems throughout the day. This can lead to a 70% reduction of cooling costs which the major energy cost driver of radio sites.
AI models simulate the results of proposed changes to calculate the impact on network energy efficiency and CO2 emissions in advance. The system can make recommendations to correct, upgrade or modernize, and offers advanced simulation capabilities, so you can see how much energy you’ll save by implementing proposed changes.
As software based overlay energy management solution Nokia AVA for Energy minimizes the power consumption of RAN equipment across all layers for all major vendors. There is no dependency on vendor specific hardware.
Benefits of AI energy management for mobile networks
Up to 30% energy savings and less CO2 emission for telco radio networks
Energy cost savings are achieved within weeks due to quick setup times
2-5x more power savings than non-AI systems that perform temporary shutdowns based on fixed schedules
Up to 70% less energy consumption
Minimizes all kinds of energy waste
across active radio and auxiliary equipment
No impact on network performance while dynamically shutting down network resources
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
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.