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Energy savings use case · Model predictive control

Cutting gas and electricity in a school without touching the boilers

How Lozana uses Builtwins Model Predictive Control and a physics-based digital twin to cut electricity and gas use without compromising comfort.

Client
Equans / Provincie Antwerpen
Address
Lange Lozanastraat 223, 2018 Antwerpen
Lozana campus building in Antwerp

Lozana, Antwerp — 4,000 m², gas boilers and radiators, MPC + excitation tests

The Lozana results

Measured at Lozana, Antwerp
42%
less electricity used, measured.
27%
less gas used, measured.
16,865 €
saved on energy per year.
22 t
CO₂ emissions avoided per year.
15
anomalies detected and solved.
<1 yr
payback on the investment.
98.3%
of the time within the comfort range.
99.4%
of the time with CO₂ within limits.
The building

A 4,000 m² building with an underground parking level, seven above-ground floors housing classrooms, a library and offices, and a rooftop level for the technical installations. Heating and cooling are provided by two gas boilers distributed via radiators, with two ventilation units and VAV boxes; a dedicated cooling machine serves the data room exclusively.

The challenge

Gas-fired heating on fixed heating curves cannot anticipate weather, solar gains or the sharply varying occupancy of classrooms, a library and offices. Conventional control treats each system as a separate island and offers little insight into why a building overconsumes — leaving teams to optimise manually.

The Builtwins approach

Builtwins goes beyond monitoring. We build a physics-based digital twin of the building — modelling production, emission and ventilation together across every zone — and run Model Predictive Control on top of it, supported by excitation tests that automatically probe the emission system. At Lozana this surfaced 15 anomalies, while predictive control reduced both electricity and gas use against the measured baseline. Delivered together with our partner Sweco, whose engineering and HVAC expertise complements Builtwins predictive control.

01

Model

A digital twin built from floor plans, HVAC installation and sensors, zone by zone.

02

Predict

Weather, solar radiation, and the building's own thermal response are anticipated per zone — not just today's temperature.

03

Optimise

Energy minimised within comfort limits (temperature, CO₂); re-run every 15 minutes.

04

Detect

Anomalies surfaced automatically, so maintenance acts on the faults that matter most.

Why it matters

Heating, ventilation and cooling drive a large share of a building's energy use — yet most systems are still steered by fixed rules that ignore how the building actually behaves. A digital-twin approach moves beyond dashboards: it reveals the real levers of optimisation, turns technical data into decisions, and helps owners meet tightening energy and ESG requirements.

In partnership with Sweco

Curious about the optimisation potential of your buildings?

Let's turn your HVAC data into measurable savings.

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