Energy Baseline & EnPI
The number every energy claim in the plant is measured against.
- Clause 6.4 Energy performance indicators
- Clause 6.5 Energy baseline(s)
- Clause 9.1 Monitoring, measurement, analysis and evaluation
Clause 6.5 requires an energy baseline against which performance is judged, and clause 6.4 requires EnPIs that make performance visible. This module fits linear and multivariable regression models between energy consumption and relevant variables such as production volume, degree-days and occupancy, reporting R², standard error and residual diagnostics for every model. Baselines can be built and maintained per SEU, per shift pattern or per product mix, and re-baselining after a major static-factor change runs through a controlled workflow that preserves the prior baseline for comparison. EnPIs calculated against these baselines roll up from machine to line to plant to corporate level, each carrying its own target, variance and statistical confidence interval.
Multivariable regression modelling
Fit linear and multivariable regression baselines against relevant variables such as production, weather and occupancy, with automatic model refitting as new data accumulates.
R² and residual diagnostics
Report R², standard error, t-statistics and residual plots for every fitted model, so model quality can be judged before it is approved.
Automated EnPI calculation
Calculate normalised energy performance indicators automatically whenever new relevant-variable data arrives, separating genuine efficiency change from production or weather swings.
Governed re-baselining workflow
Trigger, justify and approve baseline revisions after major static-factor changes through an approval workflow that keeps the prior baseline for comparison.
Multi-level EnPI scorecards
Roll EnPIs up from machine to line to plant to corporate level, each with its own baseline, target and variance for direct comparison.
Confidence-interval reporting
Attach statistical confidence bounds to every baseline prediction, distinguishing a genuine EnPI deviation from ordinary model uncertainty before an alert fires.
Relationship between energy consumption and the primary relevant variable used to calculate the baseline
| Production Output (tons) | Energy Consumption (MWh) |
|---|---|
| 820 | 41,200 |
| 795 | 39,900 |
| 860 | 43,100 |
| 780 | 39,400 |
| 910 | 45,200 |
| 705 | 36,800 |
| 940 | 46,100 |
| 830 | 41,800 |
| 760 | 38,600 |
| 885 | 43,900 |
| 815 | 40,700 |
| 875 | 42,950 |
| R² = 0.94 | 0.99 |
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The figures and charts shown on this website are illustrative sample data used to demonstrate the software's capabilities and do not reflect the actual performance of any organization.
A baseline regression an auditor can actually reproduce, with published R² and residuals.
Re-baselining follows a controlled, documented workflow instead of an ad-hoc spreadsheet edit.
EnPIs stay comparable across seasons because normalisation is automatic, not manual.
Executives see one EnPI per site instead of a dozen conflicting spreadsheet formulas.
Every performance claim carries a statistical confidence level, not just a single number.
Baseline regression model with R² and residual report
Normalised EnPI time series per SEU and per site
Re-baselining approval record
EnPI scorecards by organisational level
Baseline documentation package (clause 6.5 evidence)
The complete module set
Online Monitoring & Metering
Watch live consumption against target lines instead of reviewing it once a month.
Management & Analytics Dashboards
Role-based dashboards that turn shared data into decisions for every audience.
Advanced Report Builder
Build, schedule and distribute Excel and PDF reports without copy-pasting anything.
Talk to an engineer about your specific modules
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