Book Demo
Book Demo
Optimize Complex Energy Systems
See your energy demand before it costs you
Driver-based demand forecasting
Early peak-risk detection
Weather- and schedule-aware modeling
For Data center cooling, Manufacturing HVAC, District energy systems , Thermal storage
Trusted by leading data centers, manufacturers, and energy innovators.
Detect anomalies before they become critical.
Not knowing what comes next can be expensive
etaONE predicts future demand peaks and enables action before they lead to higher grid charges, procurement costs, or capacity contraints.
Forecast models learn from your historical consumption and the operational and external inputs that drive it, so your team anticipates expected load instead of reacting to what already happened.
Forecasted load curves highlight the intervals where demand is likely to exceed expected thresholds, giving you time to flatten, shift, or prepare before a peak affects cost or capacity.
Weather, production, and occupancy schedules are factored in as load drivers, so forecasts reflect the real activity behind your energy use rather than a generic curve.
Actual consumption is continuously compared against forecast to surface deviations early, while forward demand visibility gives energy teams a structured basis for short- and mid-term procurement and planning decisions.
What you get
System-Level AI Control Built on Digital Twins
The solution can be introduced step by step, starting with a focused use case and scaling across additional systems, assets, or sites oncethe business case is validated.
Demand Forecast Models
Forecast models learn from historical consumptionpatterns and relevant external or operational inputs. Teamscan anticipate expected load instead of reacting to past consumption.
Weather-Aware Forecasting
Weather data is considered as a driver for energyconsumption where it influences system load. Facilities canbetter prepare for demand changes caused by temperature or weather conditions.
Weather-Aware Forecasting
Weather data is considered asa driver for energy consumption where it influences system load. Facilities canbetter prepare for demand changes caused by temperature or weather conditions.
Schedule-Based Demand Planning
Production, occupancy, or operating schedules can beused to explain and forecast expected load changes. Energy planningbecomes connected to actual operational activity.
Forecast vs. Actual Comparison
Actual consumption is compared against forecasteddemand to reveal deviations. Teams canidentify unusual patterns earlier and investigate root causes.
Procurement Support
Forecasted demand provides a more structured basisfor short- and mid-term energy planning. Energy teams can make procurement and planning decisions with better demandvisibility.
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.
For engineering and company leaders
Outcomes your team and your business can measure.
Replace planning that can't keep up
Model weather, production, and occupancy together instead of comparing them by hand in spreadsheets.
Flatten and shift load intelligently
Use forward demand visibility to act on peaks before they happen, using thermal inertia, storage, and flexible operation.
Make complex demand manageable
Turn many interacting load drivers into a clear, forward-looking signal of what to expect and when.
Separate signal from noise
Distinguish a normal load swing from a preventable peak before it hits.
Always stay in control
Forecasts and recommendationsoperate within your defined boundaries; the AI surfaces and advises, your teamdecides.
Cut peak-driven energy costs
Reduce exposure to demand charges and unfavorable intervals by seeing peaks before they occur.
Strengthen procurement decisions
Base short- and mid-term energybuying on structured forecasts instead of outdated assumptions.
Improve planning without capital expenditure
More value from existinginfrastructure, with no new hardware and no rip-and-replace.
Scale from one site to the whole portfolio
Prove value at a singlefacility, then roll the same forecasting layer across the fleet
Turn flexibility into a financial lever
Shift load, avoid peaks, andcapture favorable tariffs where market signals allow.
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.
Large commercial and high-load buildings
Improve performance in complex HVAC environments where demand, occupancy, weather, and operating schedules change continuously.
Manufacturing and automotive
Reduce energy waste in process cooling, ventilation, heating, and site-level energy systems with variable production schedules and operating modes.
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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Data Center
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Equinix
Real results in live, mission-critical infrastructure.
"With the help of etalytics' expertise, we are implementing AI-based operational optimization of the cooling systems at the Frankfurt site [...]. We are thus supporting a highly innovative approach that can serve as a blueprint for an entire industry."
Energy Foresight
See what etaONE® forecasts for your facility.
Start with a free feasibility study. We analyze your existing data, estimate your peak-reduction and planning potential, and show you the path — no system changes, no obligation..
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.
