Case Study: Data Center Reduces Costs 28% with AI

The Challenge

When this Toronto-based colocation data center approached us in early 2025, they were spending $450,000 per year on electricity to power and cool their 500-rack facility. Their energy costs had increased 35% over the previous two years due to:

• Rising electricity rates in Ontario
• Expanding customer demand requiring more servers
• Inconsistent cooling efficiency across different times of day
• Peak demand charges from simultaneous server deployments

The facility manager knew they were overspending but didn't have visibility into where the waste was occurring or how to fix it without impacting uptime-a non-negotiable requirement for their enterprise customers.

The Baseline Analysis

We started with a two-week data collection period, integrating with:

• Building Management System (BMS) for HVAC controls
• PDU (Power Distribution Unit) meters for rack-level consumption
• Utility interval data showing 15-minute power consumption
• Weather data and external temperature sensors

The analysis revealed three major inefficiencies:

Issue 1: Cooling Over-Provisioning

The facility's cooling system was running at maximum capacity 24/7, regardless of actual heat load. During low-utilization periods (nights and weekends), they were cooling empty aisles to the same temperature as fully-loaded server racks.

Cost impact: $180,000/year in wasted cooling energy

Issue 2: Peak Demand Spikes

New server deployments happened during business hours when the facility was already near peak load. This caused demand spikes that set the monthly demand charge-even though the servers could have been powered on at 2 AM with zero operational impact.

Cost impact: $72,000/year in avoidable demand charges

Issue 3: Missed Time-of-Use Optimization

Ontario's time-of-use electricity rates vary significantly throughout the day. The facility wasn't taking advantage of off-peak rates for flexible workloads like backup systems testing, battery conditioning, or pre-cooling during cheap overnight hours.

Cost impact: $35,000/year in missed savings opportunities

The Solution

Phase 1: Predictive Cooling Optimization (Weeks 1-4)

We deployed AI models that predict heat load 24 hours ahead based on:

• Historical server utilization patterns
• Scheduled maintenance and deployments
• External temperature forecasts
• Day-of-week and seasonal trends

The system automatically adjusts cooling setpoints and airflow to match predicted needs rather than running at maximum capacity. During the first month, this reduced cooling energy consumption by 32% with zero impact on server temperatures or uptime.

Phase 2: Peak Demand Management (Weeks 5-8)

We implemented automated scheduling for new server deployments and high-power maintenance activities:

• Server power-on sequences scheduled for off-peak hours
• Battery testing moved to overnight periods
• Pre-cooling initiated before predicted high-load periods
• Real-time alerts when approaching monthly peak threshold

The facility's peak demand dropped from 780 kW to 612 kW-a 21.5% reduction that directly translates to lower demand charges every month.

Phase 3: Time-of-Use Arbitrage (Weeks 9-12)

With cooling and demand optimized, we focused on shifting flexible loads to cheaper hours:

• Pre-cooling the facility during off-peak hours (11 PM - 7 AM at $0.065/kWh)
• Running backup generator tests during mid-peak instead of on-peak
• Scheduling software updates and system scans for off-peak periods
• Optimizing UPS battery charge cycles to align with cheap power

The Results

Breakdown of Savings

Additional Benefits:

• Zero downtime: All optimizations occurred automatically with no service interruptions
• Extended equipment life: Running cooling at appropriate levels reduces wear on HVAC systems
• Carbon reduction: 340 tons of CO2 avoided annually by reducing consumption and shifting to cleaner grid hours
• Staff time savings: Automated scheduling eliminated 15 hours/month of manual coordination

ROI Analysis

What Made This Work

Three factors were critical to success:

1. Good Data Foundation

The facility had existing BMS and PDU metering, which made integration straightforward. Sites without this infrastructure may need additional sensors, but the ROI still justifies the investment.

2. Gradual Automation Rollout

We didn't flip a switch and hand over control. The system ran in monitoring mode for two weeks, then gradually took on more automation as the team gained confidence. This change management approach prevented resistance and ensured buy-in.

3. Continuous Learning

AI models were retrained monthly as the facility's usage patterns evolved. When the data center added 50 new racks in month 4, the system adapted within days to optimize the new load profile.

Lessons for Other Data Centers

This case study demonstrates that even well-managed facilities have significant optimization opportunities:

• 20-30% energy cost reduction is achievable without capex investment
• ROI typically occurs within 6-12 months
• Automation eliminates the need for constant manual monitoring
• Uptime and reliability can improve alongside cost savings

The key is moving from reactive energy management (responding to yesterday's usage) to predictive optimization (preparing for tomorrow's needs). Data centers are particularly well-suited for this approach due to their 24/7 operations, measurable loads, and tolerance for automated control.

Next Steps

If your data center is spending $200K+ annually on electricity, you likely have similar optimization opportunities. The first step is a baseline analysis to quantify where you're losing money and what savings are achievable.

Most vendors (including Enalysis) offer free assessments that use your utility bills and basic facility data to estimate potential savings. This gives you a data-driven business case before committing to implementation.