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Peak Load Management
Turn peak demand into operational flexibility
Prevent demand spikes and capacity violations with automated peak control
Shift and coordinate loads within operational, temperature, and redundancy limits
Turn peak response from reactive firefighting into repeatable system intelligence
For Technical Facility Managers, Energy Managers, Utility Managers, and operations teams responsible for electrical capacity, utility systems, and energy-intensive operations.
Trusted by leading data centers, manufacturers, and energy innovators.
The Peak Constraint
Your biggest energy cost may only happen a few hours a month
Most sites still manage peak events manually, reacting too late or operating too conservatively to avoid risk. The result is unused flexibility, unnecessary demand charges, and infrastructure that operates below its actual potential. As power demand becomes more dynamic, peak-driven headroom increasingly limits growth, efficiency, and operational resilience. Traditional EMS platforms monitor demand. etalytics orchestrates flexibility.
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Avoidable demand charges and contractually defined peak load penalties drive up energy costs.
Static controls do not respond to changing load and price conditions.
Electrical and thermal assets operate in silos instead of as one system
Peak mitigation competes with uptime and redundancy requirements
Operators react after peaks emerge instead of preventing them
Existing flexibility from generators, storage, EV charging, and flexible loads remains underused
Infrastructure expansion is often driven by peak assumptions, not actual capability
Operational Peak Intelligence
Automated peak control across your entire energy system
etalytics transforms peak management from manual intervention into continuous operational control.
Using live telemetry, configurable control logic, and real-time system coordination, the platform detects peak risk before thresholds are reached and orchestrates available flexibility across the site. Instead of optimizing individual assets in isolation, etalytics determines how the full energy system should behave to reduce peaks, preserve stability, and improve capacity utilization.
Typical optimization modules and use cases:
Peak prediction and early response:
Detect peak risk before thresholds are reached, creating more time to intervene and fewer emergency decisions
System-wide energy coordination
Coordinate CHP, boilers, storage, cooling, and flexible loads as one system instead of separate assets
Predictive peak shifting
Move loads proactively based on demand forecasts, operating priorities, and site constraints
Intelligent storage dispatch
Use battery and thermal storage to buffer short-term spikes without disrupting operations
Multi-energy optimization
Coordinate HVAC and thermal systems with electricalinfrastructure, batteries, CHP, PV, and microgrids where relevant
Controlled load flexibility
Prioritize and shift EV charging and non-critical loads to preserve capacity for business-critical operations
Renewable integration
Include onsite generation and flexible demand in peak control to reduce grid peaks and increase local energy use
Simple Process
How it works
etalytics follows a structured three-step deployment model.
Platform integration
We connect to existing infrastructure such as SCADA, BMS, PLCs, historians, submeters, utility interfaces, weather data, and relevant tariff or market signals. The standard approach is to use existing data, sensors, meters, and control infrastructure first instead of adding new hardware.
Digital twin setup
We structure data by system, asset, and energy flow, then model the relevant physical and operational relationships. This creates transparency, identifies inefficiencies, validates optimization potential, and can provide virtual measurements such as estimated volume flows when direct measurements are not available.
AI control deployment
Based on the validated system understanding, etalytics deploy optimization logic in open-loop recommendation mode or closed-loop adaptive control. Control actions operate within defined boundaries and include transparency, manual override options, and fallback strategies for mission-critical operations.
Measurable Impact
Operational Improvements That Matte
Lower energy costs
Reduce total energy input and cost across the optimized scope.
Measured by normalized kWh or MWh consumption, energy cost in EUR or USD, and savings compared with an agreed baseline.
Lower CO2 emissions
Reduce emissions by operating assets more efficiently and shifting operations where lower-carbon energy is available.
Measured by CO2e reduction over a defined period.
Less manual effort
Reduce manual setpoint changes, overrides, and reactive troubleshooting.
Measured by manual intervention rate, override events, and operator time spent on recurring control adjustments.
Lower equipment runtime and wear
Avoid unnecessary operation and prioritize efficient modes such as free cooling, optimized part-load operation, and coordinated asset use.
Measured by runtime hours, start-stop cycles, and utilization of active versus passive or more efficient modes.
Higher stability and supply quality
Maintain temperatures, pressures, humidity, airflow, or other operating parameters within defined boundaries.
Measured by deviation from target ranges and percentage of time within operating limits.
More intelligent use of flexibility.
Use thermal inertia, storage, on-site generation, and price signals where relevant.
Measured by shifted load, avoided peak demand, use of favorable tariffs, or demand response participation.
Validated business case
Quantify savings potential, technical fit, risk, and implementation effort before scaling.
Measured by expected savings versus solution cost and a clear rollout decision.
Data centers
Optimize cooling plants, free cooling, hydraulic distribution, airflow-related dependencies, and supply temperatures while protecting mission-critical uptime and stability.
Pharmaceuticals and clean environments
Improve HVAC and utility efficiency while maintaining stable environmental conditions, compliance requirements, and operational boundaries.
Chemicals and industrial production
Coordinate cooling, heating, ventilation, thermal utilities, and electrical infrastructure under fluctuating production loads and changing energy prices.
Manufacturing and automotive
Reduce energy waste in process cooling, ventilation, heating, and site-level energy systems with variable production schedules and operating modes.
Large commercial and high-load buildings
Improve performance in complex HVAC environments where demand, occupancy, weather, and operating schedules change continuously.
Why etalytics
Because efficiency software should do more than show dashboards.
etaONE® turns your operational data into a live digital twin of your energy system and uses AI to continuously identify the best operating strategy for your site. The result is lower energy cost, earlier detection of performance drift, and better operational decisions with less manual work – without replacing your existing infrastructure.
System-level optimization, not siloed fixes
etalytics optimizes across cooling, heating, ventilation, electrical infrastructure, storage, CHP, and microgrids.
Digital twin foundation
Physical and data-driven models make complex system behavior transparent and provide the basis for reliable optimization.
AI operators can trust
Recommendations and control actions are explainable, bounded, and validated against real operating behavior.
Built for critical infrastructure
The solution supports fallback strategies, manual override options, and operation within defined safety and reliability boundaries.
Existing-infrastructure-first approach
Projects usually start with available sensors, meters, data sources, and control points, with additional hardware recommended only where core measurements are missing, or model accuracy would materially improve.
Business-case driven delivery
etalytics focuses on optimization scopes where expected savings and operational value exceed solution cost and create a validated business case.
Fast path to value
A focused first implementation can often be completed in roughly three months once access, data, and customer-side decisions are available, depending on project scope and customer readiness.
Proven impact
Trusted by leading operators worldwide
Equinix, Merck, and others achieve up to 50% cooling energy savings with etalytics. Explore our case studies.
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Trusted by operators across data centers and industry
FAQ
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We empower industries to make complex energy systems and operations more intelligent, efficient, and resilient — with AI and digital twins that operators can trust.
