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Energy smart grid-analytics and insights of Intelen patented Technology

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Intelen draft pitch and some Intelen insights of patented technology for smart grid analytics

Intelen draft pitch and some Intelen insights of patented technology for smart grid analytics

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  • 1. Energy - Smart Grid AnalyticsDr. Vassilis NikolopoulosCEO & co-founderIntelen
  • 2. Big Data…the 3 V
  • 3. Big data
  • 4. What is Big Data ? Big data” refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze
  • 5. Smart grids
  • 6. Big Data for the Smart grid
  • 7. IntelenEmerging new company DifferentiationFocus on next generation Smart Grid IT We optimize the value for Utility customers over aTop 100 start-up global (red herring) unified Engagement 2.0 Cloud PlatformRapid and Adaptive development Services Big Data Analytics over cloudLEAN innovation procedures for Demand Response & Energy efficiencyMany world recognitions Adaptable Environments Cloud services over IPv6Presence in Greece, Cyprus and US User EngagementStrong Management & Advisory Boards Social Nets, Game mechanics & Mobile apps Revenue model License-based cloud model over retailer networks
  • 8. Intelen Intelen’s 3-tier service layers Advanced algorithmics for Data managementData Analytics and metering Ability to handle & visualize Pbytes in real-timeBig Data & Info-graphics Engage customers using behavioral dynamicsGame mechanics and Social
  • 9. Intelen’s cloud IPv6 Social extensions Buildings dynamics  with human  Game extensions behaviors Big Data Analytics Cloud cross  PVs Analytics platform EVs Storage  Harvesting Industry dynamics  with production  behaviors  Utility MDM
  • 10. Intelen’s Analytics
  • 11. Intelen’s Analytics
  • 12. Big Data Energy cases - 1 We have variable dynamic data basis: energy – Target: find correlated customers for pricing – Question: Find X customers that in a specific timeframe have the same energy/power peak based on similar weather conditions… – Really tough, we need stream analytics – Result: offer variable energy pricing contracts according to variable Time-Of-Use (ToU) Demand – Metrics: pricing ($, euro), Pmax, Pmin, Timestamps, customer metadata, utility production costs, SMP, etc
  • 13. Examples: Dynamic pricing Pricing zones Load profiles 14 12 10 8 6 4 2 0 0 0 0 0 0 0 0 00 00 00 00 00 :0 :0 :0 :0 :0 :0 :0 Time 0: 2: 4: 6: 8: 10 12 14 16 18 20 22 Different ToU ζώνες for each profile / day / week
  • 14. Big Data Energy cases - 2 We have variable dynamic data basis: building – Target: find optimal energy efficiency strategy – Question: Find X buildings that in a specific timeframe have correlated energy efficiency metrics, according to local climate conditions, human behaviors and building metadata – Really tough, we need stream analytics – Result: offer variable predictive maintenance and personalized energy efficiency services – Metrics: KWh/m2, Pmax, Pav, Temp, degreedays, weather, human behavior, demographics, building metadata, customer financial data
  • 15. Example: case-if-scenario analytics KPI Τιμή Μονάδα y = x*13.4474 + (-124.2227) 320 300 Μέση ημερήσια Κατανάλωση 185 [kwh/day] 280 Μέση ημερήσια Κατανάλωση 260 229 [kwh/day] Ενέργεια(KWH/day) 240 εργάσιμων 30000 220 Αιχμή Ημέρας [W] 200 180 Αιχμή Νυκτός 1837 [W] 160 [wh/m2/ 140 Ειδική Κατανάλωση 2926 month] 120 21 22 23 24 25 26 27 28 29 30 31 Κατανάλωση ανά βαθμοημέρα [wh/m2/ 91 Εξωτερική Θερμοκρασία(C) ανά επιφάνεια HDD] Φορτίο Βάσης 1359 [W] Συντελεστής Φορτίου Νυκτός 11 [%]
  • 16. Big Data Energy cases - 3 We have variable dynamic data basis: microgrid – Target: find optimal RES balancing nodes – Question: Find X correlated buildings that match their consumption and peak metrics to Y Solar/Wind/EVs RES sources in a isolated grid – Really tough, we need stream analytics – Result: offer variable nodal pricing, according to the local RES injection to the grid – Metrics: RES production, weather conditions, consumption profiling, nodal pricing, EVs position (GIS), load grid estimation, etc
  • 17. Example: micro-grid analytics
  • 18. Intelen Algos insights , [C iNj = xi , j yi, j ]gN ⎛1 → ⎞ eiNj = ⎜ ∑ Ed μ , ⎜n ⎟ N ⎟ ⎝ i∈d ( n ) ⎠ g N =1 = {m1 , m2 K mn }∈ g gN CiNj , eiNj , g1 g2 g3 C(x,y)1 C(x,y)2 C(x,y)3 e1 e2 e3 32 22 36 (4.2, 0.78) (5.9, 0.94) (9.2, 0.95) 0.67 0.84 1.02 14 29 46 (4.1, 0.76) (5.9, 0.92) (9.9, 0.94) 0.98 1.85 3.25 21 18 51 (5.4, 0.95) (12.8, 0.81) (15.1, 0.82) 0.71 2.81 2.95 34 25 31 (8.1, 0.99) (11.4, 0.81) (15.4, 0.83) 3.10 2.98 2.15 17 24 49 (4.9, 0.99) (8.1, 0.80) (12.2, 0.82) 0.95 4.15 3.46 29 33 28 (7.9, 0.99) (11.8, 0.99) (15.1, 0.99) 1.84 1.75 1.96
  • 19. Intelen Algos insights , [ C iNj = xi , j yi, j ] gN ⎛1 ⎞ g N =1 = {m1 , m2 K mn }∈ g → eiNj = ⎜ ∑ Ed μ , ⎜n N ⎟ ⎟ ⎝ i∈d ( n ) ⎠
  • 20. Conclusions Big data is the future Data scientists is a future position Smart grids will move towards IoT IoT will create a world “data havoc” Correlations & data fusion the future of Big Data Soon data variations will project our lives Trend analytics will predict things
  • 21. Think Big…Googling: intelen v.nikolopoulos@intelen.com http://gr.linkedin.com/in/vnikolop http://twitter.com/intelen http://www.intelen.com