DCBrain: the Waze of physical networks

by Pauline Vettier
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2017-12-07 09:13:41
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France
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3933

With the energy transition, the management and optimization of "smart" physical networks has become an issue for energy suppliers. French start-up DCbrain developed a software solution to support this transition.

How does the DCbrain software work?

Benjamin de Buttet, commercial director: We model the behavior of physical networks, fluid or energy, by relying on the data collected in these networks ... Our solution of big data and artificial intelligence allows our customers to visualize very simply and in real-time the state of the network, identify anomalies and opportunities for optimization and prediction. If one had to make a comparison, DCbrain is a bit of the Waze of physical networks. Waze transforms all measures sent by drivers into information and advice on road traffic, we do the same on electrical networks: if there is a problem with an infrastructure, for example gas or electricity, we can calculate flows and propose solutions.

How was the idea of this software born?

Benjamin de Buttet: Arnaud de Moissac CEO co-founder of DCbrain (with François Olivier-Martin), previously worked on the energy efficiency of data centers for a telecom operator. He has identified a real lack in the market, and a frustration of the operational field with the energy efficiency tools. From there came the idea of creating a start-up.

What is DCbrain's contribution to sustainable buildings and cities?

Benjamin de Buttet: On the city side, optimizing fluid and energy networks enables a better understanding, simulation and operating of multi-energy smart grids, thus contributing to the energy transition of the cities and districts. On the building side, the DCbrain software can also be adapted to complex buildings to allow facility managers to manage them in a very fine way.

Who are DCbrain's customers today?

Benjamin de Buttet: We work with Enedis, SFR, Total, GRDF, or Engie. For our first collaboration with Total, we set up a modeling of a steam network inside a refinery. Steam production is very expensive and its optimization it is crucial for Total, in terms of costs, but also in its approach of reducing its energy consumptions. This type of problem is found among energy suppliers. We have also expanded to logistics networks with RATP and SNCF.