Energy - Smart Grid Analytics

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Energy - Smart Grid Analytics

Dr. Vassilis Nikolopoulos

CEO & co-founder

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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

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Intelen

Differentiation

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

Services

Big Data Analytics over cloud for Demand Response & Energy efficiency

Adaptable Environments

Cloud services over IPv6

User Engagement

Social Nets, Game mechanics & Mobile apps

Revenue model

License-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

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Intelen

Advanced algorithmics for Data management

Data Analytics and metering

Big Data & Info-graphics

Game mechanics and Social

Ability to handle & visualize Pbytes in real-time

Engage customers using behavioral dynamics

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Intelen’s cloud

Buildings dynamics with human behaviors PVs EVs Storage Harvesting Industry dynamics with production behaviors IPv6 IPv6 Social extensions Social extensions Game extensions Game extensions Utility MDM Utility MDM

Big Data Analytics

Cloud cross

Analytics platform

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Big Data Energy cases - 1

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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

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Examples: Dynamic pricing

0 2 4 6 8 10 12 14 0:00 2:00 4:00 6:00 8:00 ₁₀:00 ₁₂:00 _14:00 ₁₆:00 ₁₈:00 ₂₀:00 _22:00 Time

Pricing zones Load profiles

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Big Data Energy cases - 2

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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

– 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

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KPI Τιμή Μονάδα Μέση ημερήσια Κατανάλωση 185 [kwh/day] Μέση ημερήσια Κατανάλωση εργάσιμων 229 [kwh/day] Αιχμή Ημέρας 30000 [W] Αιχμή Νυκτός 1837 [W] Ειδική Κατανάλωση 2926 [wh/m2/ month] Κατανάλωση ανά βαθμοημέρα ανά επιφάνεια 91 [wh/m2/ HDD] Φορτίο Βάσης 1359 [W] Συντελεστής Φορτίου Νυκτός 11 [%] 21 22 23 24 25 26 27 28 29 30 31 120 140 160 180 200 220 240 260 280 300 320 Εν έρ γε ια (K W H /day ) ΕξωτερικήΘερμοκρασία(C) y = x*13.4474 + (-124.2227)

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Big Data Energy cases - 3

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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

– 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

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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 N j i e_, N j i C_, N g

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]

N g j i j i N j i x y C _, = _, _, ⎟⎟ ⎠ ⎞ ⎜⎜ ⎝ ⎛ = ∑ ∈ → ) ( , 1 n d i N N j i Ed n e μ {m m m } g g_N₌₁ = ₁, ₂_K _n ∈

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Intelen Algos insights

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N g j i j i N j i x y C_, = _, _, ⎟⎟ ⎠ ⎞ ⎜⎜ ⎝ ⎛ = ∑ ∈ → ) ( , 1 n d i N N j i Ed n e μ gN=1 ={m1,m2Kmn}∈g

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Conclusions

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Big data is the future

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Data scientists is a future position

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Smart grids will move towards IoT

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IoT will create a world “data havoc”

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Correlations & data fusion the future of Big Data

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Soon data variations will project our lives

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Think Big…

Googling

Googling: : intelenintelen

[email protected]

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