Energy Autonomy

Optimize sourcing, generation, and storage based on market signals

Reduce energy costs by aligning sourcing, generation, and storage with electricity prices, gas prices, tariffs, and demand peaks

Replace fixed schedules with adaptive dispatch across CHP, boilers, batteries, storage, and flexible loads

Improve flexibility value while respecting asset limits, operational dependencies, and technical constraints

Request Feasibility Study

Book Demo

Built for industrial energy systems with coupled electricity and heat assets, volatile energy markets, and real plant constraints.

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

Value Loss

Fixed schedules waste flexibility and raise energy costs

Industrial energy systems are increasingly exposed to volatile electricity prices, changing gas costs, dynamic tariffs, grid constraints, and demand peaks. Yet many sites still operate sourcing, onsite generation, storage, and flexible loads with static schedules or isolated control logic. This means flexibility often remains unused.

Storage may charge or discharge at the wrong time; CHP or other electricity generators, heat pumps and boiler decisions may not reflect current cost conditions, and flexible loads may create avoidable peaks instead of being scheduled intelligently.

‍

Storage and generation run on fixed schedules instead of adapting to price and demand signals

Electricity and gas procurement decisions are disconnected from onsite dispatch

Price peaks, demand peaks, and tariff windows are not translated into operational decisions fast enough

CHP, boilers, heat pumps, batteries, renewable generation, and flexible loads are optimized separately

Manual planning makes it difficult to react to volatility, asset availability, and changing site demand

Adaptive Dispatch

Coordinate sourcing, generation, storage, and flexible loads dynamically

etalytics enables Energy Autonomy by combining market signals, asset behavior, site demand, and operational constraints into one adaptive scheduling logic. Instead of optimizing individual assets in isolation, the system coordinates electricity, heat, gas, storage, and flexible demand across the site. The solution can start with a focused use case such as CHP and boiler dispatch, battery scheduling, or peak-load reduction, and then scale toward broader site-level energy flexibility once the business case is validated.

Bivalent energy source control

Select whether demand should be covered through onsite generation, storage, or external sourcing based on prices, asset status, and site priorities.

Spot, forward, and tariff alignment

Align procurement and dispatch with electricity prices, gas prices, dynamic tariffs, grid charges, and relevant market signals

CHP, heat pump and boiler coordination:

Schedule CHP and boiler operations based on electricity prices, gas prices, heat demand, asset efficiency, and system constraints.

Storage-based load shifting

Charge and discharge storage dynamically to avoid high-cost periods and reduce peak demand

Flexible renewable integration

Include renewable generation in dispatch decisions alongside storage, conventional assets, and flexible loads

Controlled charging infrastructure

Schedule charging loads in line with available capacity, tariff structures, and site demand

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 Matter 

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.

Request Feasibility Study

Book Demo

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.

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