The TADA predictive models provide good results: an R2 of 99%, a MAPE of 5%, and a RMSE of 1,04 for a mean of 22,13. Effectively managing the thermal behaviors of a building is a complex process. These behaviors are expressed in a collection of thermal energy equations, which, once calculated, will not change over time for a building unless significant renovations are carried out. TADA is a simple alternative to the use of these complex equations.
TADA has selected the following six main criteria out of the ten available in the dataset:
- the relative density, with a weight of 21% in TADA’s decision,
- the surface area, with a weight of 21% in TADA’s decision,
- the wall zone, with a weight of 20% in TADA’s decision,
- the roof zone, with a weight of 16% in TADA’s decision,
- the total height, with a weight of 14% in TADA’s decision,
- the window area, with a weight of 7% in TADA’s decision
An R2 of 99% means that the predictions are very accurate. It is interesting to note that the building’s relative density is a significant criterion in TADA’s decision, before the window area with three times the weight in TADA’s decision.
It is also possible to dynamically evaluate the impact of increasing the window area on the heating or cooling load by using our live predict feature.
In one week, architects gained significant support in their energy consumption estimates for buildings-to-be:
- Calculate a building heating load before the building is built with an R2 of 99%
- Evaluate in seconds the heating impact of doubling the window surface on a new building
Talk to us about how you can make sense of your data and achieve success.