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.

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

Step 1: Connect Data Sources
Connect typical data sources such as submeters, PLCs, BMS, SCADA, historians, and utility feeds. Existing infrastructure is used wherever possible to create a consolidated data base.
Step 2: Structure by System and Asset
Organize incoming signals by asset, system, area, or utility flow so data becomes operationally meaningful. This turns disconnected point data into usable Energy Transparency.
Step 3: Monitor Live Performance
Monitor energy flows and asset behavior in real time through structured dashboards. Teams get a clear operational view for daily decisions and faster issue detection.
Step 4: Review and Improve
Use the live monitoring layer as the basis for recurring reviews, reporting, and operational follow-up. This helps teams move from reactive checking to consistent performance oversight.
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.

Dashboard mockup
Use Cases and Industries

Where Adaptive Energy Control 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.

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.

Built for real-world systems

We model your actual chillers, pumps, heat exchangers, and cooling assets – not a generic template.

Physics-based, data-trained

Our digital twin combines engineering logic with live operating data for reliable, site-specific optimization.

Actionable, not theoretical

We do not just report inefficiencies. We identify where performance drifts, what it means, and what to do next.

Safe by design

You decide the level of autonomy. From recommendation mode to closed-loop control, operators stay in charge.

Fast to implement

We connect to your existing BMS using standard protocols – no rip-and-replace required.

Proven in mission-critical environments

Trusted by leading operators in data centers and industry, with measurable impact on efficiency and operations.

Get Started

Ready to optimize your facility's energy use?

Request a feasibility study to evaluate real-time monitoring dashboards for your site. We assess connectivity, data readiness, and which KPIs and dashboards deliver the fastest impact on energy costs and operational efficiency.

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.