You recognize it, you are working on a calculation, you have thought it through well and look forward to the result. Then the result appears on your screen and the feeling of powerlessness comes over you. You blink again or take off your glasses, is it really true? That is not possible … but it appears to be there anyway.

Check and double check

We experienced the above in one of our first projects. After having modeled an entire neighborhood in 3D and loading it into our NEST calculation model, we compared the transmitted energy rating of the average home in the neighborhood with our energy rating of the NEST calculation model…. and they turned out not to be at all consistent. Label D was passed on but label B was our conclusion.

Not nice and not very conducive to a good working atmosphere but obviously not something that could remain unresolved. After check and double check, we have once again examined our energy label calculation and compared it thoroughly with the energy label calculation according to the standard EPA method.


A number of things became clear to us, with the main conclusion that there is a major mismatch between the theoretical energy label and the actual energy label (of practice) on which the NEST calculation model is based. And to go one step further .. this means that a very large part of the buildings / houses actually have the wrong energy rating if you look at the actual consumption. But how is that possible, what is the source of this mismatch?

Different temperatures

Theoretical calculation standards for the calculation (eg EPA) of heat losses are based on the same average indoor temperature of 18 ° C (of course fluctuates during the day) at every insulation level or energy label. The known energy labels are geared to this theoretical consumption and should be an indication of the gas consumption in the home. In reality, this theoretical premise appears to be incorrect. Research from TU Delft has shown that the average indoor temperature in poorly insulated homes is lower than this average and people heat on average to a higher temperature with a higher insulation level. Partly because of behavior and partly because other heat technologies such as, for example, a heat pump work more efficiently if they are not switched off, ie if the temperature does not fluctuate.

Influence on expected energy saving

The effect of this mismatch is that the saving on the energy bill after structural interventions is often lower than is theoretically assumed. Nevertheless, there is still much to be gained in the field of energy saving and therefore in energy costs. TheEarlybirds still use the actual energy rating as standard and calculate with increasing indoor temperatures per label. This way we stay closest to reality and we can find the right energy measures for buildings and neighborhoods.