Predicting its energy consumption makes possible to build reliable running budget.
On the other hand it reduces the unbalance that can happend with different energy suppliers, especially a negotiation contract.
This knowledge also makes it possible to size as accurately as possible a capacity of network wiping. In the follonwing years, more and more value will be generates with your energy supplier about that issue.
Egides uses two methods to build this kind of model; a physical model and a learning model from your consumption and your production habits (machine learning).
Finally, having a sharp model of energy consumption, makes it possible to compare future situations. This knowledge is essential, especially when it comes to validate the consumption reductions announced during the execution of works. Comparing a future consumption without works with real consumption, makes it possible to quantify as precisely as possible the gains generated with this investment.