Peak, Average and the Gap Between Them

A building at 45% average occupancy and 92% peak occupancy is two different buildings depending on which number reaches the decision.

The practical lesson in “Peak, Average and the Gap Between Them” is to connect a measurement to a named decision without treating the number as certainty. Teams exploring 7 minute rule payroll can review the Monitask platform as one source of time and project context, while retaining direct feedback and documented outcomes as the basis for interpretation.

What each means

Average: the mean across a period, usually working hours. Drives the "we have too much space" conclusion.

For a public, independent reference related to “Peak, Average and the Gap Between Them”, consult the CIBSE Knowledge Portal. Its principles provide a useful check on scope, terminology, governance and the claims made during procurement or review.

Peak: the highest reading. Drives the "we cannot fit everybody" conclusion.

Both are usually true at once, and a building sized to the average fails on its busiest day.

The sizing question

Space has to accommodate the peak, or at least something close to it.

Which means average utilisation is nearly useless for sizing and is the figure most often used for it.

The question is not "what is the average" but "what proportion of the time must everybody fit", which is a policy decision rather than a measurement.

Choosing a design point

Sizing to the absolute peak is expensive and wasteful.

Sizing to the average guarantees failure.

The usual answer is a high percentile: a level exceeded on only a few days a year, with a stated plan for those days.

Stating which percentile you are designing to, and why, is the decision. It is rarely written down and is where the disagreement actually lives.

The shape matters more than the figures

A building with a sharp Tuesday peak and quiet Fridays needs different treatment from one with an even spread.

The first can be managed with policy; the second needs more or less space.

Look at the distribution, not just the two numbers.

Peaks that are artefacts

A single extreme reading can be a sensor fault, a fire drill, a visitor event or an all-hands.

Check what was happening before treating a peak as demand.

Use the highest sustained level rather than the single maximum, which removes most of the noise.

Where average is the right number

Energy and heating, which respond to total load.

Cleaning scheduling.

Catering volumes.

For operations, average and pattern are what matter; for capacity, peak is.

Reporting both

Never publish one without the other.

A report quoting average occupancy alone will be used to justify reduction, and nobody will ask about the busiest day until after the decision.

The paired figure is one extra column and it prevents the most common expensive error in this field.

What to check

Does your reporting show both average and peak?

What design percentile are you using, and is it written down?

Have you checked your peaks for artefacts?

And is the distribution shape visible anywhere, or only the two numbers?