AI can reduce CO2 emissions and network energy costs, with no negative impact on performance or end customer experience. As energy consumption already accounts for about half of all telco network operations cost, 78% of telcos are counting on AI energy solutions to cut energy use.
With the pressure coming from the Paris Accord, and from the cost side as well, you must choose the best strategies to shrink the carbon footprint of your networks and achieve your sustainability commitments. AI-based energy management automation can be the fast track to solving both issues.
How Artificial Intelligence reduces the carbon footprint of telco networks
Did you know 78% of telcos are counting on AI energy solutions to cut energy use? From the originally produced energy in power plants only 90% “arrive” at the network, so there is already a loss of 10% during energy transmission. From this remaining energy about 80% is consumed by the radio access, the rest by transport, core and OSS. 30% of that network energy (35% of the original energy) is consumed by auxiliary passive components such as air conditioning and power systems, so that only 70% (65% of the original energy) is consumed by the network elements itself.
Site solutions for energy are extremely important. Power hungry fans and power supplies consume another 20%, only the rest arrives at the chipsets as such and can be used for transmitting traffic. From that remaining energy only 30% is really used in a productive revenue generating way since on average most resources are running idle. Therefore, AI based solutions performing dynamic shutdowns of unused resources are key. In effect 85% of the original energy “disappears” and is not used productively.
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
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.
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.
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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