Customers frequently ask us whether it makes sense to implement ECCO2 Building Intelligence in buildings that are new or renovated.

After all, such buildings use ultra-modern HVAC technology, right?

Given both regulatory and public expectations, there is no doubt that the HVAC system is perfectly tuned, no?

We have the data to support these statements :

  • In order to fine tune a HVAC system, several iterations are necessary, and the learnings of each step must be analyzed.
  • Modern buildings are extremely sensitive to even the smallest change in HVAC control parameters. Because they react so slowly, the technician is long gone before a large change has started to materialize. For example: adjust forward water temperature by a mere 1°C and watch a swing of 1°C of indoor temperature settle-in after a couple of days.
  • In a modern, energy efficient object, a large portion of the energy required for heating comes from internal gains – human activity inside the building. This cannot be determined accurately until people actually move in.
  • Such buildings typically reach thermal equilibrium – with no heating – at an indoor temperature close to 20°C. Letting the temperature drift by a mere 1°C above this equilibrium point causes a two digit % increase in energy consumption.
  • In the case of new buildings, HVAC companies are usually not required by contract to do fine tuning after inhabitants have moved in and the heating season has started.

Does this sound like a not so perfect world?
Let the data answer this question.

The building depicted below was commissioned in 2020. Its energy reference area (SRE/EBF) is 4’777m2. It is connected to district heating.

After deploying the ECCO2 Building Intelligence solution, we measured an average indoor temperature of 22.8°C during the heating season.

Under the circumstances of the 2022 energy crisis, we agreed with the property manager to set the target indoor temperature at 20.0°C.

This graph shows how our “Artificial Intelligence colleague”, NARA AI adjusted indoor temperature to achieve the set goal of 20.0°C.

In 416 machine learning steps, NARA AI led this HVAC system to a perfect “kiss landing” at 20.0°C. Not 20.5°C, not 19.5°C – exactly 20.0°C. In order to fully appreciate this feat, consider the buildings’ very high inertia and how variable Swiss weather is in autumn:

The next graph tells the backstage story: here you can see how much excess energy NARA removed from the system. Forward water temperature decreased from 35°C to 25°C.

And here is the result :

How much additional effort and investment went into achieving high energy performance: thicker insulation, better windows, sophisticated ventilation, certification, etc…?

There is no better investment than deploying ECCO2 Building Intelligence on new constructions and renovated buildings.


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