Reactive to predictive: A smarter way to manage energy

At Teraco, our commitment to energy efficiency has long been underpinned by continuous improvement and smart design. Today, the journey has evolved, and we are now harnessing the power of predictive analytics and AI-driven control systems to proactively manage energy usage and reduce environmental impact across all our data centres.

Reactive to predictive: a smarter way to manage energy
We’ve moved beyond traditional monitoring to a system where AI models predict, recommend, and control key operational settings. By doing so, our operations teams can now query how much energy should be expended on cooling and receive an intelligent, data-backed answer. This allows us to benchmark actual performance against predicted baselines, identify anomalies, and optimise energy usage in near real-time.

Key results and metrics
We achieved a 2% improvement in Power Usage Effectiveness (PUE), realising more than 11 million kWh energy savings across our data centres over the past year, which is a notable change driven by better airflow management and data-driven insights.

Predictive modelling has enabled rapid intervention when power consumption spikes. Our system can identify anomalies in the operating efficiency of cooling equipment, which can be resolved immediately, restoring optimal performance.

We achieved a PUE reduction from 1.4 to 1.3 in one of our facilities by implementing automated recommendations for adjustments in free air cooling settings – a seasonal change that has now been sustained for over 12 months.

Operational enhancements powered by data
Airflow optimisation – Our hot aisle and cold aisle configurations are balanced more effectively thanks to real-time sensor feedback and data integration.

Proactive maintenance – Our platform enables monthly data reviews and real-time dashboard alerts, allowing the team to conduct proactive filter cleaning at more regular intervals, as opposed to waiting for scheduled quarterly maintenance. This results in instantly visible energy efficiency gains.

Dynamic cooling decisions – Seasonal shifts in weather patterns trigger predictive model responses, prioritising free air cooling units to reduce HVAC load while maintaining ISO 50001 energy management standards and ensuring compliance with strict client SLAs for temperature and humidity parameters.
Data-driven decision making

Our predictive control system is built to understand objectives and safety thresholds, from client SLAs to system efficiency targets. Models are refreshed daily, with quarter-hourly updates and hourly predictive model runs. This ensures that deviations are not just flagged but immediately investigated.
A prime example: When daily cooling energy use exceeded the predictive model’s allowance, the system prompted an operational check. Within hours, inefficiencies were corrected, highlighting how data transparency enables faster and smarter decisions.

Tools and talent
Building an HVAC operational excellence team at Teraco has led to meaningful and positive change. Combined with AI-powered control philosophies and a centralised data platform, we’ve created a feedback loop where human expertise and machine learning reinforce one another.

Looking ahead
With predictive analytics embedded into our infrastructure, we are better equipped to plan our sustainability roadmap for the years ahead. We have embedded processes for continuous improvement, not just in theory, but in measurable outcomes that our teams can monitor and improve in real-time.

At Teraco, we provide our clients with hyperscale data centre facilities built to hyperscale standards – with the intelligence and adaptability to match. Through predictive analytics, continuous data refinement, and automated controls, we are not only reducing energy usage but redefining operational excellence in the data centre space.t