GRIDPOCKET PERSONAL SMARTGRID SOLUTIONS!

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Conrad Moody
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1 BigFoot project Filip GLUSZAK Paris - Sophia Antipolis 20/03/2012 1

2 Contents SmartGrid applications for BigData Grid monitoring Smart metering Intelligent home BigFoot research project Consortium Platform Objectives Conclusions GRIDPOCKET 2009 CONFIDENTIAL 2

3 About GridPocket Provider of value added services platforms for energy GRID = electric network, POCKET = personal, established in 2009, 10 employees GridPocket Systems SA R&D Center Poland, Gdansk GRIDPOCKET Head-Quarters France, Sophia-Antipolis GRIDPOCKET 3

«kwh» to «Energy Services» GRIDPOCKET paradigm shift Technology disruption Environmental constraints New needs and opportunities: Data Services for

4 «kwh» to «Energy Services» GRIDPOCKET paradigm shift Technology disruption Environmental constraints New needs and opportunities: Data Services for Energy GRIDPOCKET Market deregulation Smart Grid data analytics market will generate $11.3 billion cumulative from 2011 to 2014 (Pike Research 2011) 4

5 Data sources in SmartGrid Grid monitoring Smart Meters Intelligent home / M2M 5

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time Reporting rate of up to 60 frames per second to

7 Synchrophasors data Phaser Measurement Units provide phaser information (magnitude and phase angle) in real time Reporting rate of up to 60 frames per second to Phaser Data Concetrators Source : Siemens.com Source : Siemens.com 7

Example projects Tennessee Valley Authority (TVA) Hadoop OpenPDC (data from Phaser Measurement Units) standard project done by Claudera Data volumes : Serving 9 million consumers Planning 1000 PMDs

8 Example projects Tennessee Valley Authority (TVA) Hadoop OpenPDC (data from Phaser Measurement Units) standard project done by Claudera Data volumes : Serving 9 million consumers Planning 1000 PMDs 30 readings per second 4.2 billion samples per day Need 500TB storage Application MapReduce oscillation scan : detection of power grid anomalies, creating power grid maps and evaluating power consumption history Source : claudera.com 8

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10 Smart metering deployments GRIDPOCKET Smart metering projects world-wide (Engage Consulting) Electricity, Gas and Water markets 270 millions smart meters in 2013 (ABI, Gartner) $200 billion investment by 2015 (Pike) 10

Regulations supportin smart GRIDPOCKET metering EU energy-climate package 20/20/20 EU directive supporting smart metering (2009/72/CE) 80% in 2020

11 Regulations supportin smart GRIDPOCKET metering EU energy-climate package 20/20/20 EU directive supporting smart metering (2009/72/CE) 80% in 2020 Directive EU obligation for energy reduction of 1,5% per year Building energy performance directive (2002/91/EG) RT 2012 new constructions regulation 11

Commercial Billing Valuation, Estimation and Editing

pricing methods, rate schedules Time of use (TOU),

Production of billing data, settlement statements,

12 Commercial Billing Valuation, Estimation and Editing (VEE) prepare data for billing Application of complex pricing methods, rate schedules Time of use (TOU), Critical peak pricing (CPP), Peak day pricing (PDP) Production of billing data, settlement statements, transaction reports, production and distribution billing 12

Social applications Variable pricing on peaks Social impact Ambient (alerts) PC displays

13 Consumer applications Energy consumption visualization Bill analysis (where the usage comes from, reduce call center load) Rebound effect management, behavioral influencing Social applications Variable pricing on peaks Social impact Ambient (alerts) PC displays Direct display Indirect % d économie d énergie Source: Oxford 2006 report based 13

Source; Eurpean Energy Exchange (28 Dec 2009) http://www.eex.

14 Demand-side management GRIDPOCKET Grid assets usage analysis and forecasting and backcasting Load research and trend analysis Remote load control demand response mangement Outage management and distribution optimization Source; Eurpean Energy Exchange (28 Dec 2009) %20Spot/Intraday%20Chart %20 %20Spot/spot-intra-chart/ /1d 14

Revenue management GRIDPOCKET De-regulation of energy

levels Product differentiation, customers segmentation

Revenue loss detection (customer accounts analysis) Source

15 Revenue management GRIDPOCKET De-regulation of energy markets High utilities consumers churn at 10-15% annual levels Product differentiation, customers segmentation Customer retention (loyalty) tools, behavioral analysis Revenue loss detection (customer accounts analysis) Source CACI Scottish and Southern Energy Case 2008 Source Poweo financial report Sept

per year (depending on reading frequency and format) Gas

16 Volumes of data For 35 million meters (France), data reading every 10,30, or 60 minutes Generates : TB per year (depending on reading frequency and format) Gas and water grid under deployment Source : ERDF! Source : ERDF 16

17 Example projects with GRIDPOCKET metering data Ontario Power Authority (OPA) experimentation planned 4.5 million meter collecting data every hour system wide analysis Southern California Edison with Teradata 100TB warehousing for user metering data Itron partners with IBM, SAP and Teradata to form Active Smart Grid Analytics enabling applications such as Settlements, Network Loss, and Revenue Protection Opower - 56 billions meter reads per year, planning to use Hadoop Data Sources BigFoot System: * DataStores * Data Engines Applications: * Front-end * Data analysis Interactive Query Engine Distributed Data Store Dashboard Q Q Q External data sources Ad hoc Queries Distributed FileSystem Operational Databases Batch Analytics Engine Data Mining Algorithms Statistical Patterns Analysis 17

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19 Intelligent home applications Security (detec,on, camera) Smart metering Ligh,ng control Smart appliances Comfort control / HVAC Mul,media And more 19

20 Volumes of data Multiple standards for sensors interconnectivity ETSI M2M framework For 35 million meters, 4-20PB per year in the future (100 sensors per HH) 20

21 BIG Data Analytics of Digital FOOTprints 21

22 BigFoot project consortium Eurecom (France) Project leader Ecole Polytechnique Fedérale de Lausanne TU Berlin / Deutsche Telecom Lab (T-Lab) (Germany) Symantec (Ireland) GridPocket (France) Projet Proposal to EU FP7 Call 22

23 Eurecom experimental platform 100 nodes single ARM v5, 1.2GHz, 512MB RAM, 64GB Power efficient 5 watts per node Simple network GB Ethernet 23

Eurecom platform - research GRIDPOCKET results Benchmarking platform for performance evaluation and comparative analysis of the inner components in the DFS and parallel processing framework System

24 Eurecom platform - research GRIDPOCKET results Benchmarking platform for performance evaluation and comparative analysis of the inner components in the DFS and parallel processing framework System optimization for map reduce, using synthetic workloads (eg. load similar to Facebook jobs or others) 2 papers published «ACM SIGOPS LADIS : Hbase, bulk data input» - resultas integrated in the Hbase source (v0.9.x) Data analysis for a telecom operator (A1 Austria) log files (billing and anomaly detection) algorithm 24

BigFoot fundamental objectives APPLICATIONS: DATA ANALYTICS SCALABLE ALGORITHM DESIGN Cross-layer approach system optimization Service-oriented query engine - transform high level declarative query

25 BigFoot fundamental objectives APPLICATIONS: DATA ANALYTICS SCALABLE ALGORITHM DESIGN Cross-layer approach system optimization Service-oriented query engine - transform high level declarative query language into parallel programs Novel data placement mechanisms for storage layer optimization - physical optimization (layout, typically data is sorted in time, optimize the way data is written) Go beyond best-effort on virtualized clusters (Performance guarantee) Currently virtualization servers are applications agnostic, (eg. If several VM share same I/O there is no benefit) HIGH-LEVEL QUERY LANGUAGE HIGH-PERFORMANCE BATCH ANALYTICS ENGINE Distributed File System DATA STORES VIRTUALIZATION LATENCY-SENSITIVE QUERY ENGINE Distributed Data Base GRIDPOCKET 2009 CONFIDENTIAL 25

BigFoot experimental objectives Experimental test beds Fine-grained measurements on all system layers Design, implementation and evaluation of

26 BigFoot experimental objectives Experimental test beds Fine-grained measurements on all system layers Design, implementation and evaluation of algorithms for large data processing IT Logs Cyber Attacks analysis (Symantec) Grid data analysis (GridPocket) GRIDPOCKET 2009 CONFIDENTIAL 26

27 BigFoot open-source objectives Contribution to the existing open-source projects (eg. Apache Hadoop) Establishment of new opensource projects for BigFoot when relevant GRIDPOCKET 2009 CONFIDENTIAL 27

BigFoot - SmartGrid data GRIDPOCKET processing tasks Data analysis tasks apply, for example, to the following cases: Consumer billing: a monthly scan of all customer data to produce consumption

28 BigFoot - SmartGrid data GRIDPOCKET processing tasks Data analysis tasks apply, for example, to the following cases: Consumer billing: a monthly scan of all customer data to produce consumption reports; Consumer Dashboard Web applications: analysis of the whole consumption data to create personalized customer reports; Load analysis: non intrusive detection of the loads mix, detection of anomalies, consumer behavioral analysis Consumer segmentation: execution of complex algorithms to classify consumers based on their consumption patterns and produce, for example, personalized contract; Assets usage analysis: analysis of geographical consumption patterns and design of predictive algorithms to help operators in provisioning of their electric network. 28

29 BigFoot SmartGrid scenario SmartGrid Apps BigFoot Platform 29

30 Conclusions Smart Grid and big data Generates significant amounts of data New needs and applications are coming Hadoop technology Promising, however still need to gain on maturity Complex setup and maintenance Interoperability Limited skills and support available in Europe Next steps BigFoot and other BigData projects w GridPocket and BigFoot are open for cooperation Looking for comments and suggestions 30

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Investor Presentation. Fourth Quarter 2015

Investor Presentation. Fourth Quarter 2015 Investor Presentation Fourth Quarter 2015 Note to Investors Certain non-gaap financial information regarding operating results may be discussed during this presentation. Reconciliations of the differences