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DCbrain : Smarter Grids
Lauréat du concours « réseaux
électriques intelligents »
Confidentiel. © 2017 DCBrain SAS | 22
Today’s world economy is made of complex flows
Operating Flows
Raw Materials
Complex network managers face 4 major challenges
Respect of SLA /
customer engagements
Electricity Steam Cooling
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Logistic flows
Distribution
Energetic Mix
Sustainable
Developpement
Cost reductions
( energy, maintenance, leaks )Communicate
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Smart Grids: How Big Data helps utilities deal with the paradigm change
Production Transport Distribution Retail
• Local& distributed production units• Complex buildings both producing,
consuming and stocking
• smart metering for pricing skills
• Pro active consumer behavior
• Network interoperability • Flow control • Demand response
How to Insure Lean transparency with regulatory & shareholders
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DCbrain and the Deep Flow Engine
Dynamic Interface representing the graph and its flows(example of an electrical synoptic)
Interface « dynamic dashboard »
Flow visualization and consumptions Diagnosis and analysis Predictive
2 Interfaces
3 fonctionnalities
Audit of the DATA ecosystem
An industrialized process
Cleaning and aggregationof data-basis
Machine learning
Digitalization of flow networks and interface creation
Confidentiel. © 2017 DCBrain SAS | 55
IA and flow networks? Our data-driven approach is unique
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Learn the map Learn the network’s behavior
Propagate flows
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Analyse / Alert Décider
Operational intelligence without the model driven solver’scomplexity
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Apprendre la carte Apprendre le comportement du réseau
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Propager les flux
Customer Customer Customer Customer
Anomalie Detection. Prediction. Classification. Visualization Target and anticipate actions.
Reduce costs Scoring for decision making (Risk, costs …)
Flow propagation. Non-lineary transfer F°. local/global optimum search
Test hypothesis: Data-driven What if scénario Chose the best scenario: Optimum Search Plan your network for efficiency & cost-
cutting
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A Steering help for the Electrical parisian Grid: Enedis
Flow Propagation. Local/Global optimum search Test hypothesis : Data-driven What-if scenarios
Chose the best scenario: optimum search
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Paris RER A : RATP
Understanding impacts of trains flows over electrical
flows Target and anticipate actions
Identify pics Reduce costs
Modèle de courant: f(train) = Intensité Scoring the risk of over-intensity for help to decision
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Engie’s Heat Network: Saint Denis
See unbalances in real time Reduce costs
Identify malfunctions and anomalies
by Machine Learning Target and plan actions
Learning the customer’s profile
Apprentissage des profils clients Better plan the dimension of your service /
network – Reduce costs
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Smart Gas Grid – BioMethane farms injection projects
Propagation of flows. Global and Local Optimum search Chose the best scenario for each biogas injection
Calculate costs and stocks
Dimension your network for the smallest
Mob : +33 7682766672
Mail : [email protected]
Adresse : DC Brain, 23 avenue d’Italie, 75013 Paris
Thomas BibetteBusiness Developer - Export Manager
Merci !
Confidentiel. © 2017 DCBrain SAS | 1212
Peu d’acteurs IA spécialisés sur l’infra (#FranceISAI)