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Methodology

How we calculate a saving

Two mechanisms, running together. The first one is the arithmetic: we value every saving at what the equipment actually draws, not what it’s rated for. They are deliberately conservative; we would rather under-claim a saving than defend one we can’t evidence.

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How the saving is measured, in two phases

First we prove it on your own cabins. Then we keep measuring it for the rest of the project, a different way.

Phase 1The proof week

Same cabins, same meter, 14 of 15 consecutive days.
  • Monitoring runs until your consumption settles.
  • Seven days before the control goes on is your baseline. Seven days after is the test.
  • Both read straight off the same meter. Nothing scaled, nothing modelled.
  • You see the result at the Savings Review and decide with a measured number in front of you.

Phase 2The rest of the project

Every hour, every circuit, against measured data from Gaia sites.
  • You are not on week on, week off once it is running. The control stays on.
  • Each hour of each circuit is compared with what the same kind of circuit uses at the same outside temperature, in or out of working hours, from measured data across Gaia sites.
  • The difference is the saving. That is the figure the portal reports.
  • No thresholds or settings are published, because that is the product.

Phase 1 is free. Phase 2 is what you pay for.

The two mechanisms

Gaia does not set a baseline once and then defend it. Two things run together, and each answers a different question a QS would ask.

1. We value savings at what the kit actually draws, not what it’s rated for

This is the arithmetic, and it is the most important thing on this page.

For every load we control, we hold its measured average consumption in the conditions it was running in. When one of our rules switches that load off, the saving we credit is:

the time it was off × the measured average for that load in those conditions

Never its rated power multiplied by hours. That distinction is the whole point. A heater or an air-conditioning unit cycles; its rated power is the draw at full call, and its actual average draw across a week is materially lower.

Conditions matter, so we compare like with like. The same heater behaves differently at a different outside temperature, and inside working hours or outside them (weekdays, 6am to 6pm, by the clock). We match on all three of those, the kind of circuit, the outside temperature and working hours, before we value anything, and the last one carries as much weight as the temperature does, because a 9 °C Monday morning behaves nothing like a 9 °C Sunday evening.

And that average is taken across every hour in the comparison, including the hours the load was drawing nothing. That is what accounts for the periods a thermostat would have switched it off anyway: those idle hours sit in the divisor, so they pull the average down and the saving down with it. Dividing by the drawing hours alone would credit us for hours the load would have been off regardless, which is the error this method exists to avoid.

This produces a substantially smaller number than the rated-power arithmetic would, and that is deliberate. It is the part of the method that reduces the figure we get to claim, and it does so on every load, every week.

The averages are not fixed, and that is part of this mechanism rather than a separate one. Monitored data from every site rolls up daily and refines them, so the record behind your numbers grows every week rather than sitting still. One site gives us a baseline; a programme gives us a benchmark we can stand behind.

2. We prove it week against week, on your own site, before you pay anything

We monitor until your consumption has settled, then take the seven days ending the day before the control goes on as your baseline. The control goes on, and the seven days beginning the day after is measured against it. The changeover day itself is excluded from both periods; the control is applied part-way through that day at no fixed time, so it belongs to neither. That is 14 of 15 consecutive days, and we state the gap rather than leave it unexplained.

So the difference is measured, not modelled. Equal seven-day periods mean weather, occupancy and trade mix are as close as they can practically be, which is why we use them rather than comparing across seasons. It is the closest control available and it needs no assumptions. Both periods are read straight off the same meter, with no scaling or normalisation applied; neither figure is modelled.

The full detail of that comparison, why equal weeks are the strongest test available, what we deliberately do not correct for, and what happens when a site works one week on and one week off, is on one week on, one week off.

The proof is your own site. The rate comes from everybody’s

These are two different things and it is worth being exact about which is which.

The two weeks that prove the control works are yours, your cabins, your meter, nothing pooled. That is mechanism 2 above, and it is the part that decides whether you pay us anything.

What an hour of switching-off is worth is drawn from everything we monitor, matched on those three conditions. Your figures are measured against the largest record of welfare-cabin energy behaviour in UK construction, and it grows every week.

That is deliberate, and it is the stronger way round. An average built from one site is a small number of hours and a shaky figure. Built from every site we monitor, it is the most reliable estimate of what that equipment really draws that anyone in this industry has. It is also why cabins not being interchangeable matters so much, a drying room and a site office have nothing in common electrically, so the record is only useful because every hour in it is matched on the kind of circuit, the outside temperature, and whether it was inside working hours.

And we can tell you how many measured hours sit behind any figure we give you. Where a particular combination is thin, we say so rather than let the number stand unqualified.

Two things we do not do

We do not treat the benchmark as a snapshot taken before the automation goes on. It is neither a one-time measurement nor a static one; the band averages behind every credited saving are refreshed daily as every site we monitor adds to the record.

We do not verify savings against your energy bills, and we would be wrong to claim we did; welfare cabins rarely sit on separate metering, so there is usually no bill isolated to the cabins in the first place. Savings are measured against your site’s own benchmark, at the circuit level, in kWh.

Is the meter itself accurate?

Checked against an independent instrument, not just against itself.

In March to April 2025 we ran a Rayleigh RI-D140, MID-approved energy meter, fitted with HOBUT Micro 19 split-core current transformers (125/5A, Class 1), alongside a site’s own utility billing meter for four weeks.

28 to 31 MarUtility 191 kWh · Gaia 194.2 kWh · +1.7%
31 Mar to 9 AprUtility 912 kWh · Gaia 916.3 kWh · +0.47%
9 to 30 AprUtility 1,802 kWh · Gaia 1,824.1 kWh · +1.23%

The customer approved wider deployment across additional projects on the strength of this comparison.

MID approval is a legal metrology standard, the same class of certification that governs the meter your own electricity supplier bills you from. This is a check on the instrument itself, not on a saving.

What this means for the numbers on this site

Every percentage we publish is a whole-period figure from a real site’s own meter data, and we state the period alongside it.

Sub-periods are labelled as sub-periods. Where a site ran on a generator for part of its life and grid for the rest, the generator figure is higher; generation is less efficient, so there is more waste available to remove. We publish the whole-period figure as the result and show the sub-periods underneath it. A generator result does not transfer to a grid site.

£ savings carry the electricity rate they were calculated at. Rates are supplied by each customer and range from 24p to 30p per kWh across the sites we publish, so a £ figure from one site is not comparable with another. We never average or total £ savings across sites. kWh and percentages are the comparable metrics. Every £ figure we publish is consumption only, standing charge and VAT excluded.

We never headline a summer-only period, and never publish a percentage without its period. Savings peak in spring and autumn, not winter. Summer has low consumption, so there is little waste available to remove. Winter has high consumption, but the heating is genuinely needed and can’t be switched off. The shoulder months carry the most removable waste.

The estate shows it plainly: the same site read 37% across a summer (13 May to 31 July 2025) and 65% across a full year (13 May 2025 to 30 April 2026). It is also why Swynnerton is the highest figure we publish at 89% across March to May 2026, a spring window, not a better site. Quoting the summer figure as a result, or the annual figure without its period, would both be misleading.

Other ways to start

  1. Recommended first step

    Get DataMate free on your next project

    We monitor your cabins until consumption settles, and you get the wastage review.

    Your cost: coordination, and one engineer at the board.

    Get DataMate free
  2. Check how your site compares

    Answer a few questions about your welfare setup, and see whether it looks higher-risk than comparable monitored sites.

    Your cost: 20 seconds and an email address for the result.

    Check how your site compares
  3. Talk about full AutoMate

    For teams ready to standardise cabins across projects.

    Your cost: a 30-minute call.

    Book an AutoMate call

What we don’t publish

The rule-level logic, how each rule decides, its thresholds, and the width of the condition bands behind the averages, stays private. Not because a customer shouldn’t see it, but because our competitors read this website too.

What we do commit to: if you are a customer, you can ask us to walk through any individual saving on your own site and we will. The two mechanisms above are what we hold ourselves to; the arithmetic on your site is available to you.

Get DataMate free See the measured results