Data Centers
AI-POWERED AUTONOMOUS DATA CENTRES: ENERGY MANAGEMENT & OPTIMIZATION
AI & ML Enabled Real-Time Monitoring, Forecasting and Closed-Loop Optimization. Up to 12–15% reduction in total energy consumption with ROI in less than six months.
Global data centres consume 1–2% of total electricity worldwide, with energy distributed approximately as: IT Load (~50–60%), Cooling including HVAC, Chillers, CRAC, and Cooling Towers (~20–30%), and Electrical Losses, Lighting & Others (~10–20%).
As AI workloads increase rack density and thermal intensity, energy complexity multiplies.
Proven Results
MEASURABLE IMPACT
Reduction in total energy consumption across Chillers, Cooling Towers, CRAC, Pumps
Typical 10–15 MW Data Centre (₹7/kWh, 8760 hours)
Return on investment in less than six months
Annual CO₂ reduction supporting net-zero initiatives
Why It Matters
VALUE PROPOSITION
Prevent Overcooling
Maintain SLA compliance while eliminating energy waste from overcooling.
Improve Asset Uptime & Life
Reduce operational risk and improve decision speed through real-time intelligence.
Improved ESG Performance
Supports net-zero initiatives and improved environmental, social, and governance metrics.
From Monitoring to Autonomous
This platform transforms data centres from reactive, to predictive, and finally to autonomous optimized operations.
Industry Challenges
KEY CHALLENGES
Escalating Energy Consumption
High-density AI and GPU workloads. Increasing total facility power (Ptot). Peak demand charges and tariff volatility. Rising grid dependency. Energy becoming the dominant operational cost
Energy inefficiency directly reduces profitability per MW deployed.
Cooling System Inefficiencies
Overcooling to maintain SLA compliance. Unbalanced cooling tower loads. Inefficient chiller part-load operations. Pumps operating with bypass open. Dynamic thermal disturbances from IT load and ambient changes
Excessive energy waste, reduced equipment life, and operational instability.
Lack of Predictive Intelligence
No real-time forecasting of load, weather, or energy prices. Reactive maintenance practices. Limited visibility of asset health. Manual reporting and delayed decisions
Energy and asset risks remain unmanaged until failure or cost escalation occurs.
Fragmented Systems & Poor Visibility
Disconnected BMS, PLCs, SCADA, DCIM. No coordinated control across multiple assets. No unified industrial historian. Siloed operational data
Monitoring exists, but optimization does not.
Regulatory & Sustainability Pressure
ISO 50001 compliance requirements. CO₂ reporting mandates. Net-zero commitments. Carbon intensity reduction goals
Energy management must move from reporting to active optimization.
Renewable & Energy Market Complexity
Solar and CHP integration. Day-ahead and intra-day energy scheduling. Price volatility exposure. Peak shaving and load shifting opportunities
Without intelligent forecasting and optimization, energy procurement risks increase.
Platform Coverage
CHALLENGE × SOLUTION
Every operational challenge in your plant maps to one or more modules in the AhilyaSoft platform.
| Challenge | APC / RTO | EMS | MES / PPMS | LIMS | APM | AI / AASP |
|---|---|---|---|---|---|---|
01Energy Efficiency (PUE Reduction) | ||||||
02Cooling Optimization | ||||||
03Power Reliability | ||||||
04Downtime Risk | ||||||
05Capacity Optimization | ||||||
06Asset Failures | ||||||
07Sustainability Targets |
scroll →
| Challenge | APC / RTO | EMS | MES / PPMS | LIMS | APM | AI / AASP |
|---|---|---|---|---|---|---|
01Energy Efficiency (PUE Reduction) | ||||||
02Cooling Optimization | ||||||
03Power Reliability | ||||||
04Downtime Risk | ||||||
05Capacity Optimization | ||||||
06Asset Failures | ||||||
07Sustainability Targets |
AI & ML Control Targets
APC KEY OBJECTIVES BY UNIT
At the core of our optimization lies multivariable predictive control enhanced with AI, applied across every unit of the complex.
Chiller Plant
Optimize chiller sequencing and loading
Minimize kW/ton across all chillers
Maintain supply water temperature setpoint
Prevent surge and optimize condenser approach
Cooling Tower
Optimize fan speed and staging
Minimize approach temperature
Balance energy vs water consumption
Coordinate with chiller plant operation
CRAC / CRAH Units
Optimize supply air temperature
Prevent hot spots and overcooling
Coordinate airflow with IT load
Minimize fan and compressor energy
Pumping Systems
Optimize pump speed and staging
Maintain differential pressure setpoints
Minimize pumping energy
Prevent cavitation and equipment stress
Deep Capability
COOLING OPTIMIZATION CAPABILITIES
Parallel equipment optimization using efficiency curves
Optimal start/stop sequencing
Load allocation across chillers
Cooling tower optimization
Pump flow minimization
Network temperature optimization
Data hall model-based total cooling capacity optimization
How We Build It
INDUSTRIAL AI MODELING FRAMEWORK
Horizontally scalable across single or multi-site data centre portfolios.
Problem Definition & Site Survey
Data Collection & Variable Identification
Exploratory Statistical Analysis
AI/ML Model Development
Training, Testing & Tuning
Real-Time Deployment & Validation
Continuous Learning & Adaptation
Global Market
THE DATA CENTRE ENERGY LANDSCAPE
Data centres consume approximately 1–2% of global electricity. With AI workloads surging, cooling and power optimization represent the largest untapped efficiency gains.
A 0.1 PUE improvement = 5–8% energy cost savings per facility.
Our AI-driven cooling optimization delivers 12–15% energy reduction with ROI in under six months.