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We prove savings using your own data, not just a model. The method is simple, transparent and consistent with recognised measurement and verification practice (IPMVP):
1. Build the "before" picture
We take at least 12 months of historical energy data for your refrigeration equipment (for example, refrigeration packs and cooling systems) plus the matching outside temperature and store trading data. This shows how your refrigeration behaves today across different seasons and trading patterns.
2. Learn how refrigeration energy reacts to weather and load
From that "before" data, we build a statistical relationship between outside temperature, store operation and refrigeration energy use. In other words: "On a day like this, with this temperature and this footfall, this is how much energy your refrigeration normally uses without optimisation."
3. Predict what would have happened without DLH/ARC
After DLH/ARC is switched on, we keep collecting the same energy, temperature and operational data. For each day, we use the "before" model to calculate what your refrigeration energy would have been if you had carried on as before, under the same weather and operating conditions.
4. Compare business-as-usual with reality
For each period we compare:
1. The energy use predicted by the "before" model (what you would have used with the old controls)
2. The actual energy you used with DLH in place
The gap between these two numbers is the measured saving, which we show in kWh, cost, percentage and CO₂ reduction.
5. Drill down by site and system
Where metering allows, DLH works from local meters or controller data for each major system – for example, individual refrigeration packs, cold rooms, chiller circuits, AHUs, data hall cooling loops or specific hotel/hospital plant rooms – rather than only the main incoming supply. This lets you see which stores, data centres, factories, hospitality sites and hospitals, and which specific assets, are delivering savings, not just a single blended estate figure.
6. Make results comparable across the estate
Because performance is measured consistently across sites, results can be compared fairly between small convenience stores and large supermarkets, different data centres, production facilities, hotels and resorts, or multiple hospital sites. You can view percentage reduction and return on investment on a like‑for‑like basis across your entire estate.