Energy - Smart Grid Analytics

Dr. Vassilis NikolopoulosCEO & co-founderIntelen

Big Data…the 3 V

Big data

What is Big Data ?

Big data” refers to datasets whose size is beyond the abilityof typical database software tools to capture, store, manage, and analyze

Smart grids

Big Data for the Smart grid

Intelen

DifferentiationWe optimize the value for Utility customers over a unified Engagement 2.0 Cloud Platform

ServicesBig Data Analytics over cloud for Demand Response & Energy efficiency

Adaptable EnvironmentsCloud services over IPv6

User EngagementSocial Nets, Game mechanics & Mobile apps

Revenue modelLicense-based cloud model over retailer networks

Emerging new company

Focus on next generation Smart Grid IT

Top 100 start-up global (red herring)

Rapid and Adaptive development

LEAN innovation procedures

Many world recognitions

Presence in Greece, Cyprus and US

Strong Management & Advisory Boards

Intelen

Advanced algorithmics for Data managementData Analytics and metering

Big Data & Info-graphics

Game mechanics and Social

Ability to handle & visualize Pbytes in real-time

Engage customers using behavioral dynamics

Intelen’s 3-tier service layers

Intelen’s cloud

Buildings dynamics with human behaviors

PVsEVs

Storage Harvesting

Industry dynamics with production 

behaviors 

IPv6IPv6

Social extensionsSocial extensions

Game extensionsGame extensions

Utility MDMUtility MDM

Big Data AnalyticsBig Data Analytics

Cloud cross Cloud cross Analytics platformAnalytics platform

Intelen’s Analytics

Intelen’s Analytics

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

Examples: Dynamic pricing

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Pricing zones Load profiles

Different ToU ζώνες for each profile / day / week

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

KPI Τιμή Μονάδα

Μέση ημερήσια Κατανάλωση 185 [kwh/day]

Μέση ημερήσια Κατανάλωσηεργάσιμων 229 [kwh/day]

Αιχμή Ημέρας 30000 [W]

Αιχμή Νυκτός 1837 [W]

Ειδική Κατανάλωση 2926 [wh/m2/ month]

Κατανάλωση ανά βαθμοημέραανά επιφάνεια 91 [wh/m2/

HDD]Φορτίο Βάσης 1359 [W]

Συντελεστής Φορτίου Νυκτός 11 [%]

21 22 23 24 25 26 27 28 29 30 31120

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Ενέργεια(

KW

H/d

ay)

Εξωτερική Θερμοκρασία(C)

y = x*13.4474 + (-124.2227)

Example: case-if-scenario analytics

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

Example: micro-grid analytics

Intelen Algos insights

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

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Intelen Algos insights

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Conclusions

Big data is the futureData scientists is a future positionSmart grids will move towards IoTIoT will create a world “data havoc”Correlations & data fusion the future of Big DataSoon data variations will project our livesTrend analytics will predict things

Think Big…

GooglingGoogling: : intelenintelen

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