Karsten Winther, EMEA President at Vertiv, explains why the role of AI in cognitive infrastructure starts with smart energy.
Artificial Intelligence (AI) is revolutionising data centre operations, delivering improved efficiency and enhanced service offerings. And, as companies grapple with increasing data centre workloads, AI-enabled solutions provide a way to alleviate the burden on IT management teams, lower operating costs and improve overall efficiency.
However, the transformative power of AI extends beyond these benefits, particularly in the realm of cognitive infrastructure. By leveraging AI, data centre operators can implement predictive maintenance strategies that go beyond traditional approaches, helping deliver optimal performance, minimising risks and reducing downtime. Within this context, the role of renewable energy sources and energy storage in smart energy systems becomes evident, providing opportunities to stabilise power grids and optimise energy consumption.
Against this backdrop, this article explores the critical role of AI in cognitive infrastructure, beginning with smart energy and its implications for energy consumption, operational efficiency, carbon reduction strategies and beyond.
Smart energy and the role of renewable sources
Like most sectors, the energy industry is experiencing rapid change and innovation. Renewable sources such as solar and wind power are leading the way in revolutionising the market, meeting the growing demand for more sustainable electricity generation and consumption. Alongside this transformation, alternative energy sources and energy storage play pivotal roles in shaping the evolving landscape. These options not only provide environmentally-friendly solutions but also contribute to grid stability and enable efficient resource use.
In certain countries, the availability of real-time energy prices empowers market players to adopt innovative strategies. One example is optimising power consumption by charging electric vehicles overnight and using them during the day. This approach reduces energy usage and contributes to grid stabilisation in an era of intermittent energy sources.
Real-time energy prices further facilitate optimised power consumption, promoting grid stability and energy ‘prosuming’, which empowers consumers to generate renewable energy locally. This can be seamlessly integrated with battery energy storage systems to enable a consistent energy supply during both peak and low generation periods.
To address the evolving energy landscape, the integration of AI in smart energy systems is particularly interesting. AI algorithms are able to analyse real-time data, enhancing grid management, demand response and efficient resource use. By continuously monitoring energy demand and supply, AI can enhance the distribution of renewable energy, maximising its effective use. Moreover, AI algorithms can predict changes in energy patterns, enabling proactive measures to maintain grid stability and reliability.
AI as a key enabler for data centre continuity
AI can be a game changer in ensuring operational continuity and reducing carbon footprints in data centres. By processing and analysing real-time data, AI enables several digital services that further empower traditional preventive and reactive services. These include monitoring services, advanced incident management and condition-based maintenance. AI-powered energy conservation tools optimise equipment calibration, resulting in operational savings and reduced energy costs.
Predictive maintenance strategies take data centre efficiency to new heights as operators can accurately time asset use while minimising risks and reducing downtime. Anticipating physical behaviours and predicting equipment lifespan empower operators to keep critical infrastructure functioning seamlessly, ensuring uninterrupted power and internet connectivity.
AI-driven predictive maintenance and lifecycle monitoring also guarantee infrastructure availability while minimising downtime. Remote monitoring services, enhanced by AI and machine learning, provide real-time visibility and diagnostics, enabling proactive network performance assessment. The integration of AI and smart energy solutions not only optimises data centre operations.
The importance of smart energy analytics in cognitive infrastructure
Energy analytics play a crucial role in today’s data centre projects, offering deep insights and actionable information derived from big data. By harnessing the power of software and data, even traditional areas like power and cooling can be improved through dynamic controls and intelligent decision-making.
Cognitive infrastructure, enabled by AI, allows systems to learn and predict the most effective ways to optimise IT performance over time. This includes managing cold air, humidity levels, speed and pressure, surpassing the capabilities of human monitoring and adjustment. The application of AI extends to predictive maintenance, empowering operators to anticipate physical behaviours and predict end-of-life for various equipment components.
Such insights enable operators to minimise downtime and help deliver the continuous operation of critical infrastructure, such as keeping the lights on and the internet running.
A bright future driven by rapid innovation
Looking ahead, the development of AI algorithms specifically designed for cognitive infrastructure will further enhance the capabilities of smart energy systems. These algorithms will enable data centres and other industries to make intelligent decisions in real-time, optimising energy usage, predicting equipment failures and proactively managing power consumption. By leveraging AI’s ability to analyse vast amounts of data and learn from patterns and trends, data centres can continuously improve their energy efficiency and reduce their environmental impact.
Furthermore, the collaboration between AI and renewable energy sources holds great potential for a more sustainable future. AI can help optimise the integration of intermittent renewable sources like solar and wind power into the energy grid. By analysing weather patterns, energy demand and supply, AI algorithms can determine the most efficient and effective ways to use renewable energy resources. This integration will not only reduce reliance on fossil fuels but also contribute to the stability and resilience of a more sustainable power grid.
The role of AI in cognitive infrastructure, starting with smart energy, is critical for achieving more sustainable and efficient data centre operations. From optimising energy consumption to digital services empowering lifecycle maintenance and renewable energy integration, AI empowers data centre operators to make informed decisions, reduce costs and minimise their environmental footprint.
As technology continues to evolve, AI will undoubtedly play an even more significant role in shaping the future of cognitive infrastructure and smart energy systems. By embracing these advancements, a digital infrastructure landscape can be created that is more environmentally friendly, resilient and capable of meeting the growing demands of the digital age.
