ZEEL

A laboratory’s energy is largely settled by its programme, and the programme is fixed in the first few weeks — long before anyone selects a system, and long before there is a model to test it with. How much open lab, how much vivarium, whether write-up sits inside the lab envelope or outside it: those decisions are made in a spreadsheet, by people who will not run a simulation to check them.

ZEEL takes the floor areas of twenty-one laboratory zone types and returns what that programme implies — estimated EUI, total energy, carbon intensity, and operating cost. It answers as fast as you can type because the arithmetic is deliberately linear: fixed zone intensities scaled by the areas you give them. That is the method, not a shortcut. It is what makes the relationship between programme and energy legible instead of buried.

Where the numbers come from

One IES:VE model of a 115,000 sf STEM building, simulated in 2019 with custom meters on electricity, fossil fuel, air and water flows for every zone. It is a rebuild of an Arup Excel tool, and the study behind it was presented at I2SL 2019 by Rishi Nandi of Perkins & Will and Patrick Pease, then at Arup. That was a conference presentation rather than a published paper, which is why it is not among the work on the research page.

Worth knowing what that means: building-wide plant totals are allocated to zones by flow share, so only room and plug electricity is a genuine per-zone quantity. A zone’s intensity is a signature, not a measurement.

Twelve cases, not forty

The study simulated five climate zones at baseline and eight cases at 5A Boston. Nothing sits in the intersection, so measures are never carried across climate zones — the tool ships a climate comparison and a measure study as two separate modes rather than inventing the twenty-eight cells nobody ran.

The measure results are the useful part, and they are not what people expect. Reducing the air change rate is worth almost three times any other single move. External shading makes a Boston lab very slightly worse. A building this ventilation-dominated does not respond to envelope work, and seeing that in two seconds is most of the value of asking.

Reading it honestly

The headline is estimated EUI, never predicted EUI, and the distinction is deliberate: a tool that says it does not predict cannot label its own headline predictive.

There is no tolerance band, because a band would be false precision. What the tool shows instead is the observed spread — against five real laboratory buildings the method has landed between 30% below and 11% above the designed EUI, low on four of the five. That is a range, not an accuracy claim, and the distinction is the whole point.

Where the source workbooks disagree with themselves, the numbers are carried through unchanged and the discrepancy is recorded against the case rather than quietly corrected. They are a published result; repairing them would be rewriting somebody else’s finding.

What it will not do

Everything runs in the browser. No account, nothing uploaded, nothing kept.

This is beta, and it exists to convey an idea — not to predict a saving. It is not for design submissions, energy targets, compliance paths, or quantified measure savings. Used at the point it is meant for, a week of programme conversations, it will tell you which questions are worth modelling properly.