Use Case · Costs
The annual financial statement shows a total. What causes it and which part of it is avoidable is not included. Here is how you get from the total to a measure that pays off.
Initial situation
Everyone in the building knows that energy costs have risen. Which system, which shift, or which night is responsible for this is unknown — because the bill condenses everything into a single number. Without this context, any energy-saving measure remains a guess.
Step by step
Step 1
Meters provide quarter-hour values, not monthly values. Where individual consumers are not recorded, additional sensor technology isolates them — depending on the case, without interfering with the machine control system.
Step 2
Three patterns regularly cost the most: consumers starting up simultaneously, which drives up the demand charge; a baseline that does not drop at night; and individual systems that are permanently above their target. All three only become visible over time.
Step 3
For each anomaly, the expected annual savings, effort, and payback period are presented side by side. This makes it easy to decide what to tackle first — and what isn't worth the effort.
Step 4
After implementation, the new route lies alongside the old one. What was actually saved is thus determined and simultaneously flows into the greenhouse gas balance.
The values in the product views are sample data.
Prerequisites
Quarter-hourly values instead of monthly totals. For larger connections, this is often already available.
Without them, it is impossible to say which consumption is to be expected and which is not.
When the main meter is not sufficient to narrow down the cause.
Result
Not just anywhere in the company, but a specific system with a time window.
Expected Euro per year, effort, and payback period — before the decision.
The course of events after implementation proves whether the calculation has paid off.
From the field
At Holzmanufaktur Decker, relevant consumption and operating data were made visible using smart meters and a radar sensor. This helped identify potential savings of over €3,500 per year — and made Scope 1 and Scope 2 data continuously available.
Potential and actual benefits depend on the location, data situation, and implemented measures.
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