Data Centers
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
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
Maintain SLA compliance while eliminating energy waste from overcooling.
Reduce operational risk and improve decision speed through real-time intelligence.
Supports net-zero initiatives and improved environmental, social, and governance metrics.
This platform transforms data centres from reactive, to predictive, and finally to autonomous optimized operations.
Industry Challenges
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.
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.
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.
Disconnected BMS, PLCs, SCADA, DCIM No coordinated control across multiple assets No unified industrial historian Siloed operational data
Monitoring exists, but optimization does not.
ISO 50001 compliance requirements CO₂ reporting mandates Net-zero commitments Carbon intensity reduction goals
Energy management must move from reporting to active optimization.
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
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
At the core of our optimization lies multivariable predictive control enhanced with AI, applied across every unit of the complex.
Optimize chiller sequencing and loading
Minimize kW/ton across all chillers
Maintain supply water temperature setpoint
Prevent surge and optimize condenser approach
Optimize fan speed and staging
Minimize approach temperature
Balance energy vs water consumption
Coordinate with chiller plant operation
Optimize supply air temperature
Prevent hot spots and overcooling
Coordinate airflow with IT load
Minimize fan and compressor energy
Optimize pump speed and staging
Maintain differential pressure setpoints
Minimize pumping energy
Prevent cavitation and equipment stress
Deep Capability
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
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
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.
