Peak Load Management

Turn peak demand into operational flexibility

Prevent spikes and capacity violations — with automated peak control

Shift and coordinate loads — within operational, temperature, and redundancy limits

Make peak response repeatable — turn firefighting into system intelligence

For Data center cooling, Manufacturing HVAC, District energy systems , Thermal storage

Trusted by leading data centers, manufacturers, and energy innovators.

The Peak Constraint

Your biggest energy cost may only happen a few hours a month

etalytics reduces demand peaks automatically by coordinating flexible loads, storage, energy sources, and charging infrastructure within real operating limits so peak response becomes repeatable system intelligence instead of reactive firefighting.

Lower energy costs — reduce total energy input and cost across the optimized scope. Measured by normalized kWh or MWh consumption, energy cost, and savings against an established baseline.
Lower CO₂ emissions — operate assets more efficiently and shift operations toward lower-carbon energy where 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 adjustments.
Lower equipment runtime and wear — avoid unnecessary operation and prioritize efficient modes such as free cooling and optimized part-load. Measured by runtime hours, start-stop cycles, and use of efficient versus default modes.
Operational Peak Intelligence

System-Level AI Control Built on Digital Twins

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.

Peak prediction and early response

Earlier detection creates time for targeted action instead of last-minute intervention.

System-wide energy coordination

Electrical and thermal assets such as CHP, boilers, storage, and flexible loads are coordinated as one energy system.-

Predictive peak shifting

emand is shifted proactively based on predicted peaks, operational priorities, and technical constraints.

Intelligent storage dispatch

Battery or thermal storage is used during critical demand periods to absorb or supply energy when peak risk rises.

Multi-energy optimization

Renewable generation and flexible demand are integrated into the control strategy based on availability, timing, and operational priorities.

Controlled load flexibility

The system dynamically coordinates available energy sources such as electricity, gas, CHP, and boiler operation based on prices, demand, and system limits.

Renewable integration

EV charging and other flexible electrical loads are automatically limited, shifted, or prioritized during critical periods.

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

Automated peak control across your entire energy system.

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.

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Use Cases and Industries

Where Automated Peak Load Management Delivers Value

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.

Where to start?

See what etalytics can unlock at your site.

Start with a feasibility assessment. Weanalyze your existing data, estimate your savings and flexibility potential,and define a focused first use case — no system changes, no obligation.

Trusted by operators across data centers and industry

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FAQ

Questions? We’ve got you covered.

What data do you need to start?

Typical inputs include electrical power, temperatures, pressures, volume flows, equipment states, runtimes, setpoints, control signals, weather data, and tariff or market data where relevant. If key signals such as volume flows are missing, etalytics can often estimate virtual measurements from available data and physics-based relationships.

Do we need additional hardware?

Not necessarily. Many projects can start with existing meters, sensors, and control-system data. Additional hardware is only relevant where important measurement points are missing.

Which teams need to be involved?

Successful projects typically involve operations, energy management, facility or utility teams, automation or BMS stakeholders, and IT or cybersecurity teams. This ensures operational ownership, technical system access, secure integration, and clear governance.

How do you address security and GDPR?

The setup depends on your architecture, hosting model, and internal requirements. In most monitoring use cases, the focus is on technical system data rather than personal data, but access control, processing scope, and governance still need to be clearly defined.

Can the system control mission-critical infrastructure safely?

Yes. Deployment can start in open-loop mode with recommendations before moving to closed-loop control. Closed-loop control operates within predefined limits, preserves manual override, and includes fallback strategies so reliability and operational safety remain protected

How quickly can we expect time-to-value?

Time-to-value depends on customer readiness, data access, system complexity, and decision speed. A focused standard implementation can often be completed in roughly three months once the required data access, technical interfaces, and project decisions are available.

How do we get started?

Start with a feasibility assessment. It clarifies technical fit, quantifies savings potential, identifies risks and constraints, and defines a realistic first use case and rollout path.