Controlling energy use: the role of AI-based solutions

Building and running energy efficient networks is becoming a strategic imperative for all telecoms CSPs. 

Commitment to reducing energy consumption is a common issue in many businesses, driven by increasing energy prices as well as by regulatory demands to control and be more transparent about energy management and carbon emissions. However, 5G deployment, which risks increasing CSPs’ energy use, is intensifying the need to improve energy efficiency in the telecoms industry. Even though 5G technologies were designed to reduce energy consumption by 90% compared to 4G on a like-for-like basis, the greater density of 5G means that, without intervention, adding 5G to existing networks will increase CSPs’ energy use.

CSPs have plenty of opportunities to reduce energy consumption in the mobile network without affecting user experience. CSPs have a vast choice of energy-saving measures, each with different implementation time-scales and potential savings, that go beyond just the natural deployment of more-modern networks. Measures to reduce energy consumption can address all elements of the network and can be grouped into four categories: network modernisation, intelligent power-saving features, efficient use of assets and alternative ways of sourcing energy.

CSPs need initially to prioritise the solutions that can be implemented in a short time-scale and that can deliver results quickly. AI-based software solutions offer CSPs the opportunity to implement energy-saving measures as soon as possible. Most importantly, the unique advantages that AI can deliver to energy management efficiency can be enhanced by the technical features of 5G connectivity.

AI can expand the potential of traditional energy-saving features. For example, AI can be applied to static switch off/on networking equipment to enable it to adjust for dynamic changes in customer behaviour and usage. It can also help to tap into new energy-saving opportunities across the network. AI can be used to predict traffic patterns and fluctuations, forecast network utilisation and weather impacts, provide maintenance and fault management data, and suggest the most actionable approach to energy management in even the most complex of network scenarios. Energy savings will be dynamically adjusted to align with network performance, ensuring that CSPs’ goals and KPIs are met but customer experience is not compromised.

However, it is extremely important that CSPs select a business and delivery model for AI-based energy-saving solutions that is best-suited to address the urgency of CSPs’ current cost challenges. The software-as-a-service (SaaS)-based business model, as an alternative delivery mechanism to on-premises and hosted cloud deployments, can greatly reduce time to value. CSPs can quickly have access to SaaS solutions because they are kept as standardised as possible, often including a set of blue-print services, which allows for minimum customisation and reduced time spent on installation and configuration. Similarly, continuous and automatic updates and upgrades can be efficiently managed and delivered by the software vendors. As a result, CSPs benefit from faster engagement with vendors, meaning shorter time to value.

We have provided an overview of an ideal AI-based energy saving solution that effectively aligns energy consumption reductions with network performance requirements. This solution will allow CSPs to realise quantifiable energy and cost-saving benefits across the entire mobile network; and can be achieved in the short term with limited upfront investment.