Book Demo
Book Demo
Energy Autonomy
Improve Industrial Energy Procurement with Real Site Data
Align procurement decisions with actual site demand patterns
Reduce exposure to avoidable price, volume, and peak-related risk
Use operational flexibility more strategically in energy buying
Built for industrial teams that need procurement decisions grounded in real operational energy behavior.
Trusted by leading data centers, manufacturers, and energy innovators.
Cost Exposure
Industrial energy procurement needs operational context
Many industrial energy procurement decisions are still based on static assumptions, fragmented planning inputs, or incomplete demand visibility. But industrial sites do not behave statically. Production schedules shift, technical loads change, and flexibility is often not reflected in procurement logic. As a result, energy buying decisions become harder to size, harder to time, and harder to optimize.
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Procurement is often based on outdated or simplified demand assumptions
Load profiles change, but energy buying strategies are not updated fast enough
Procurement, operations, and technical teams work from different data views
Peak demand, volatility, and site constraints are difficult to anticipate reliably
Missed alignment between market timing and plant behavior increases cost risk
What You Get
A data-driven foundation for industrial energy procurement
etalytics helps industrial teams connect procurement decisions with real operational energy behavior. Energy Autonomy combines historical and live site data, forecasting logic, and flexibility analysis to create a stronger basis for industrial energy procurement.
Instead of treating demand as fixed, the solution helps teams understand how energy demand behaves, how it may change, and where operational flexibility can improve procurement outcomes.
Typical optimization modules and use cases:
Demand pattern analysis
Structure historical and live energy data by time, asset, utility, and operating condition to reveal actual consumption behavior
Price and exposure visibility
Evaluate procurement-relevant demand scenarios against changing market conditions and risk windows
Flexibility-aware procurement planning
Identify where operational flexibility can influence when and how energy is procured
Shared planning across teams
Bring operational, technical, and energy data into one shared view for planning and decision support
Scenario-based decision support
Compare demand, market, and operating scenarios before procurement decisions are made
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 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.
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.
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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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
FAQ
Questions? We’ve got you covered.
Start with a free feasibility study
Ready to improve your facility's energy efficiency ?
Discover where your biggest savings potential lies. Our experts analyze your energy system, identify opportunities, and show you the path to lower costs, emissions and resilient operations – with AI you can trust.
We empower industries to make complex energy systems and operations more intelligent, efficient, and resilient — with AI and digital twins that operators can trust.
