Energy Baselines & Performance Metrics That Show What Is Really Changing
Engineering-led baseline models and KPIs for industrial sites whose metrics report energy use but cannot explain it, so you can tell real efficiency gains from changes in production or weather.
- Moves you from what are we consuming to what is driving it, and how it is changing
- Builds a baseline as a model of energy versus its real drivers, not a single number
- EnPIs normalised so external factors do not look like performance
- Lets you measure expected versus actual, and prove genuine improvement
- Part of SHV Energy
- ISO 50001
What This Service Is
Energy Baselines and Performance Metrics sit directly on top of the work done in a Metering and Data Assessment. Once a site has confidence in its data, the next question is different: how do we define, measure and track performance in a way that actually reflects what is happening on the site?
This service turns raw energy data into a structured, reliable framework that explains performance rather than just reporting it, and lets the site track improvement over time. It moves the conversation from what are we consuming to what is driving our energy use, and how is that changing.
Governing standardISO 50001 (EnB & EnPIs)
The Challenge It Solves
At this stage most sites already have KPIs: energy per unit of production, cost per shift, overall energy intensity. Reports are generated regularly and there is visibility at a high level. The issue is that these metrics are frequently not aligned to the real drivers of energy use.
Performance may appear to improve or deteriorate, but the site cannot clearly explain why, because a change in a KPI may be driven by production volume or weather rather than a genuine efficiency change. That makes it hard to tell real progress from normal variation. In many cases a baseline has never been properly defined: consumption is known, but there is no model linking it to the variables that drive it. Without that, there is no reliable way to say whether energy use is higher or lower than expected, whether a change is justified by operating conditions, or whether an improvement initiative actually delivered, which matters most when a site is under pressure to demonstrate savings, report carbon reductions or justify capital.
- KPIs that report consumption but are not aligned to the real drivers
- Performance that appears to change for reasons outside the site's control
- No properly defined baseline linking energy use to its drivers
- No reliable way to prove an improvement initiative actually delivered
How EM3 Delivers It
Identify what drives energy use
Rather than assuming a simple relationship such as energy per unit of production, we analyse historical data to understand how different variables, production levels, operating hours and shift patterns, ambient conditions such as temperature, and process-specific loads, actually influence energy use.
Build the baseline model
We build a model of how energy consumption behaves under different conditions, grounded in an engineering understanding of how the systems operate. The baseline is not a single number; it is a relationship between energy consumption and its drivers, representing how the site is expected to perform.
Construct the performance metrics
We select the right variables to normalise energy use, ensure the KPIs respond correctly to changes in those variables, and remove distortions caused by factors outside your control, so the metrics behave logically.
Validate against reality
We check that the metrics tell the truth: when performance genuinely improves, they show it clearly; when consumption rises because of higher output or external conditions, that is visible and explainable rather than appearing as deterioration.
Integrate into ongoing tracking
We integrate the structure into how the site tracks performance going forward, defining how data should be used, how KPIs should be reported and how changes should be interpreted, so the framework keeps working after we leave.
What You Receive
A validated energy baseline model
A model that defines how much energy the site is expected to use under different operating conditions, based on actual historical behaviour, so you can compare current against expected performance.
Tailored Energy Performance Indicators
Not generic ratios: KPIs tailored to your site and linked to the variables that actually drive consumption, so they provide insight into performance rather than just reporting it.
A performance-tracking framework
A way to assess whether energy use is higher or lower than expected, how performance changes over time, and whether improvements are sustained.
Distortion-free metrics
Metrics normalised for the drivers outside your control, so production volume and weather no longer disguise or exaggerate performance.
Measurement and verification support
Once the baseline is established, the impact of any change, operational or capital, can be quantified against it, supporting measurement and verification of projects.
Analytical performance management
The shift from descriptive reporting to analytical performance management: a system the site can use to understand and manage energy use over time.
Proven Outcome
Across many sites, a common pattern is that energy performance is tracked using simple ratios that do not account for the key drivers. That leads to performance appearing to improve because production fell rather than because efficiency rose, energy increases being read as negative even when they are justified by higher output, and project impacts that cannot be separated from normal variation.
By establishing a proper baseline and aligning the KPIs to real drivers, sites can clearly identify when performance has genuinely improved, isolate the impact of a specific change, and track energy use in a way that reflects operational reality. In practice this often changes the internal conversation: instead of debating whether performance has improved, teams can point to a clear comparison between expected and actual consumption.
Why EM3
Data plus engineering context
Rather than defining baselines purely through statistics, we link them back to how the site actually operates, which avoids models that are mathematically correct but physically meaningless.
Part of a wider system
Performance metrics are not standalone outputs. They are built to connect data collection, system behaviour and operational decision-making, so the outputs are usable in practice, not just theoretically sound.
Clarity, not more KPIs
The objective is not to increase the number of KPIs or reports, but to ensure the metrics in use genuinely reflect what is happening on site and can be used with confidence.
Models that stay valid
The work is built to link data to system behaviour and to produce models that remain valid over time, so the framework keeps telling you the truth as the site evolves.
How We Engage
Frequently Asked Questions
How is this different from a metering and data assessment?
A metering and data assessment makes your data trustworthy. This service builds on that: it defines a baseline and KPIs that explain performance and let you track improvement, moving you from what are we consuming to what is driving it and how it is changing.
What exactly is an energy baseline?
It is not a single number. It is a model of the relationship between your energy use and the variables that drive it, such as production, operating hours and weather, representing how the site is expected to perform under given conditions.
Why are our current KPIs not enough?
Most KPIs are not aligned to the real drivers, so performance can appear to improve or worsen for reasons outside your control, like production volume or weather. That makes it hard to tell genuine progress from normal variation.
Will this tell us whether an improvement actually worked?
Yes. With a validated baseline you can compare expected against actual consumption and isolate the impact of a specific change from normal variation, which also supports measurement and verification of projects.
Do you just give us more reports?
No. The aim is clarity, not more KPIs. We build metrics that genuinely reflect what is happening on site so they can be used with confidence, not just added to a dashboard.
How long does it take?
It is a focused analytical engagement, typically a few weeks, depending on data availability and site complexity, and usually shorter than a full audit.
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