Energy Transparency

Benchmark Energy Performance With Operational Context

Standardize energy-performance indicators, compare like with like, and track when a site or asset moves away from its expected range. etaONE® connects every KPI to its boundary, calculation logic, operating context, and source data so technical and management teams can work from the same evidence.

Compare consistently: Use shared KPI definitions across sites and asset groups.

Account for operating conditions: Include relevant production, weather, load, utilization, or operating modes.

Investigate performance gaps: Move from portfolio-level comparisons to the underlying system and source signals.

For energy managers, utility teams, facility engineers, and portfolio leaders who need reliable and explainable energy-performance comparisons.

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

Inconsistent Comparisons

Good decisions require fair comparisons

Many organizations already calculate energy-performance indicators. The challenge is that sites and teams often use different system boundaries, formulas, time periods, units, and normalization methods. This creates rankings and reports that look precise but do not always support a fair technical comparison.

Without a consistent methodology, benchmarking can direct attention toward the wrong site or asset. Genuine performance gaps remain hidden, while normal operational differences may be treated as inefficiencies.

KPI names are shared, but calculation rules differ by site.
Baselines remain in use after major operating changes without review.
Assets are placed in the same peer group even when their technical conditions differ.
Monthly averages hide gradual performance deterioration.
Analysts spend more time reconciling definitions than investigating causes.
Comparable Performance

Build trusted energy benchmarks

etaONE® helps teams define how energy performance should be calculated, compared, and reviewed. KPIs are connected to their system boundaries, source data, operating conditions, and calculation logic.

Standardize KPI definitions

Define common formulas, units, aggregation periods, system boundaries, and data-quality rules.

Teams can confirm that an indicator such as kWh per unit, kWh per runtime hour, or cooling efficiency is calculated consistently across the comparison.

Establish baselines

Compare current energy performance against approved baselines, targets, or expected operating ranges.  

This gives teams a documented point of comparison instead of relying on informal assumptions.

Normalize relevant operating conditions

Include variables such as production volume, ambient temperature, system load, utilization, runtime, or operating mode where they materially affect consumption.

This helps distinguish justified variation from genuine underperformance.

Create meaningful peer groups

Group comparable sites, systems, or asset types using defined inclusion criteria.

Comparable entities can be ranked together,while fundamentally different systems remain separate.

Track performance drift

Monitor whether an asset or site moves awayfrom its baseline, peer range, or expected operating band.

Persistent deviations become easier to identify before they are hidden inside quarterly or annual averages.

Drill down into the cause

Link KPI changes to the contributing timeseries, operating states, and asset context.

Technical teams can investigate why performance changed without rebuilding the calculation manually.

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.
Operational Impact

Turn energy KPIs into better operational decisions

Compare performance fairly

Use consistent boundaries, formulas, units, and peer-group criteria to create fair comparisons across sites, systems, and assets.

Detect performance drift earlier

Identify when efficiency gradually moves away from an approved baseline, target, or expected operating range.

Prioritize the biggest opportunities

Prioritize persistent and operationally relevant performance differences instead of treating every short-term fluctuation as a problem.

Explain why performance changed

Move from a portfolio ranking or KPI deviation to the asset data, operating conditions, and source signals behind it.

Standardize portfolio reviews

Give site and corporate teams a shared set of indicators for recurring energy-performance discussions.

Drive continuous improvement

Use traceable benchmarks to select improvement measures, review their effect, and identify where further technical analysis is needed.

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Where It Applies

Benchmarking for complex industrial energy systems

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.

Manufacturing and automotive

Reduce energy waste in process cooling, ventilation, heating, and site-level energy systems with variable production schedules and operating modes.

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.

Start Here

Find out whether your sites and assets are truly comparable

Benchmarking is the next step after energy transparency. Once teams trust their data and compare performance consistently, they can identify inefficiencies, anticipate degradation, and prioritize optimization efforts.

Trusted by operators across data centers and industry

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FAQ

Questions? We’ve got you covered.

What is energy performance benchmarking?

Energy performance benchmarking compares the energy use or efficiency of a site, system, or asset with a defined reference. The reference may be a historical baseline, a target, an expected performance range, or a group of comparable peers.

What is an energy performance indicator?

An energy performance indicator, or EnPI, is a measure used to evaluate energy performance. Examples include kWh per unit produced, electricity per runtime hour, cooling-system efficiency, or energy consumption per square meter. The indicator must be linked to a clearly defined boundary and calculation method.

What is the difference between a KPI and a baseline?

A KPI describes performance. A baseline provides the reference against which performance is compared. For example, kWh per production unit may be the KPI, while the average performance during an approved historical period may be the baseline.

Why is normalization important?

Energy use changes with factors such as weather, production volume, equipment load, occupancy, runtime, and operating mode. Normalization helps account for these variables so teams can distinguish normal variation from a meaningful efficiency gap.

What data is needed?

Typical inputs include energy consumption, meter data, asset and system structures, operating hours, runtime, load, production volume, weather data, and operating modes. The required inputs depend on the KPI and comparison being created.

How are performance deviations prioritized?

Deviations can be assessed based on their magnitude, persistence, recurrence, data quality, and operational relevance. This helps teams focus on sustained and material performance gaps instead of reacting to every short-term fluctuation.

Does benchmarking automatically improve performance?

No. Benchmarking identifies where performance differs and where investigation is required. Improvement depends on the operational, maintenance, engineering, or optimization actions that follow.