Smart Grid Analytics: All That Remains to be Ready is You

Smart Grid Analytics:
All That Remains to be Ready is You
Copyright © 2012 eMeter Corp. All rights reserved.
Free Webcast | June 12, 2012

The Panel
Krishan Gupta
Director, Product Management
eMeter, A Siemens Business
Elliott McClements
Big Data Business Solutions Executive, Energy
and Utilities
IBM Software Group, IM

eMeter Analytics
Update
The Power of
Analytics
What We Can Do
Together

Copyright © 2011 eMeter Corp. All rights reserved.://www.spiegelau.com/
How did Walmart use Garden Hoses to
Increase Weekend Sales of Beer by 17%?

http://media.oregonlive.com/
What does Bird Watching have to do with
your Credit Score?

http://en.wikipedia.org/wiki/File:Hernando_de_Soto_Bridge_Memphis.jpg
How did the Memphis Police Department
spend .5% of their budget to reduce crime
by 30%

Is this the Grid of the Future?
eMeter Corporate Logo
To ensure the integrity of the eMeter brand it is necessary to understand how to use
the logos.
Minimum Clear Area
A minimum clear area has been created around each logo. This area should always be
kept free of any graphic elements and/or messages. The gray lines in these illustrations
show how the minimum clear area is calculated. In all cases, an area equal to the cap
height of eMeter “r” must remain clear on all sides of the logo. The gray bars in these
illustrations indicate the visual height, width, vertical center and horizontal center of
the logo.
(see the next slide)
Slide 6 Copyright © 2011 eMeter Corp. All rights reserved.
Who stole $6 Billion Last Year?

the logos.
Minimum Clear Area
the logo.
Who am I lending to?

Unbilled Usage by Billing Cycle

Unbilled Usage Summary

the logos.
Minimum Clear Area
the logo.
$360 Million Stolen Each Year in USTransformers Fail. But Why?

Outage Details by Distribution Node

Service Point Metering

Virtual Metered Transformer

Transformers Load Monitoring

http://earthdaytolucalake.wordpress.com/
15% of infrastructure is used 1% of
time. What can we do about it?

System Load

Individual Peak Loads

Time of Use Analysis

Targeted Demand Response

The Possibilities Are Endless…
Grid Loss Identification
Pricing Analysis
Customer Profiling
& Segmentation
Load Modeling & Forecasting
Demand Response Evaluation
Distribution Planning

© 2012 IBM Corporation
Information Management

Introducing:
Elliott McClements
Big Data Business Solutions
Executive, Energy and Utilities
IBM Software Group, IM

IBM Netezza Analytic Appliance for Utilities
• Netezza pioneered the Data Warehouse Analytic Appliance
market in 2003
• Our solution is an enterprise-class data analytic appliance that
combines database, server and storage
• Purpose built for complex query and ad hoc analysis of
terabytes of dynamic, detailed data.
• Delivers 10-100x the performance with lower TCO
• 600+ Customers
• Acquired by IBM in November of 2010
• IBM’s foundation for bringing Analytics to the masses
Data Mining and StatisticsSpatial AnalyticsBusiness Intelligence

Speed Scalability
SmartSimplicity
IBM Netezza Value Across Industries
1 PB on Netezza
7 years of historical data
100-200% annual data growth
“NYSE … has replaced an Oracle IO relational
database with a data warehousing appliance from
Netezza, allowing it to conduct rapid searches
of 650 terabytes of data.”
ComputerWeekly.com
“…when something took 24 hours I could
only do so much with it, but when
something takes 10 seconds, I may be
able to completely rethink the business
process…”
- SVP Application Development, Nielsen
15,000 users running
800,000+ queries per day
50X faster than before

Traditional Data Warehouse Complexity

Data Warehousing – Simplified

Information ManagementInformation Management
29
 Purpose-built analytics engine
 Integrated database, server and storage
 Standard interfaces
 Low total cost of ownership
 Speed: 10-100x faster than traditional system
 Simplicity: Minimal administration and tuning
 Scalability: Peta-scale user data capacity
 Smart: High-performance advanced analytics
TwinFin™
The true data warehousing appliance.

Information ManagementInformation Management
Inside the TwinFin
30
Optimized Hardware + Software
Purpose-built for high performance
analytics; requires no tuning
True MPP
All processors fully utilized for
maximum speed and efficiency
Deep Analytics
Complex analytics executed
in-database for deeper insights
Streaming Data
Hardware-based query acceleration
for blistering-fast results

Data Stream Processing
FPGA Core CPU Core
Decompress Project
(columns)
Restrict
Visibility
(rows)
Complex ∑
Joins, Aggs, etc.

IBM Netezza Analytics
Business Overview
Developer
Custom Analytics
R, Hadoop, Java, C,
C++, Python, Fortran
Analyst
Model Building & Scoring
IBM SPSS, Revolution
Analytics, Fuzzy Logix,
ESRI, SAS, R …
Business Manager
BI & Visualization
IBM Cognos,
Microstrategy, SAP,
SAS, MS Excel …
Predictive
Analytics
Data Mining Geospatial
Analytics
IBM Netezza Appliance
Advanced
Statistics
Data Prep

Accelerating the Analytic Process
Business
Value
Time To Intelligence
CompetitiveAdvantage
Data
Transfor-
mationData
CleansingBusiness
Require-
ments
Data
Exploration
Model
Deployment
Model
Development
Model
Testing
Model
Execution
Data
Preparation

Large Scale Geospatial Analytics
 US Utility running a sophisticated GIS analytical process to determine optimum location for
Smart Meter Comms infrastructure. They were unable to run the analysis on their
Oracle/ESRI environment in less than 30 days.
 They provided an ESRI File Geodatabase containing
– meter locations (3.4M),
– elevations (44 M features)
– and foliage (80 M) features layers.
 Task was to merge the meter layer with the elevation and foliage layers in an effort to
determine elevation and foliage obstructions.
Task NZ Time
Create GRID < 1 min
Terrain – Grid Intersection 10 min 32 sec
Foliage – Grid Intersection 15 min 35 sec
Meter – Terrain Intersection 1 hr 37 min
Meter – Foliage Intersection 2 hr 14 min
Meters within Distance 5 sec
Create Line Segments 2 sec
Foliage Height – Line Intersection (3000 meter Radius) 90 sec
Total Time
< 5 hours

EnergyIP™ Analytic Foundation
3rd Party
Analytic
Apps
3rd Party
Reporting Tools
EnergyIP™
Core Database
EnergyIP™
Apps
CIS & Customer
Info
Meter Reads
& Event
Data
GIS &
Grid Info
EnergyIP™ Advanced Graphical
Reporting Framework
EnergyIP™
Analytics
Database
EnergyIP™
ETL
EnergyIP™ Analytics
Foundation

EnergyIP™
Analytics
Database
EnergyIP™
ETL
EnergyIP™ Analytics Powered by IBM Netezza
3rd Party
Analytic
Apps
3rd Party
Reporting Tools
EnergyIP™
Core Database
EnergyIP™
Apps
CIS & Customer
Info
Meter Reads
& Event
Data
GIS &
Grid Info
EnergyIP™ Advanced Graphical
Reporting Framework
EnergyIP™
Netezza
Analytical
Appliance
EnergyIP™
Netezza
Adapter
EnergyIP™ Analytics Powered
by IBM Netezza

Customer
Domain
Work and Asset
Domain
Grid
Operations
Domain
Communications
Security
Integration
Process Automation
Regulatory Compliance
Smart Metering
HAN
Portal
Electric Vehicles
Distributed Energy
Resources
Substation
Automation
Line Automation
Distribution Mgmt.
System
Outage Mgmt.
System
Planning
Construction
Demand Response
Control Room
Remote Asset Monitoring
Condition Based Monitoring
Remote Device
Monitoring
Scheduling
Crew Optimization
Asset Mgt
Mobile Workforce
Managment
Enterprise
Optimization
Customer Analytics Work and Asset Analytics
Grid Analytics
Utility operating domains are growing and becoming inter-related.
Mobile devices
• There are new ‘participants’ in the
Energy Value Chain that the Utility has
to take into account.
• There are more applications and
technologies to consider
• The information that an Operating
Domain requires to increase
performance is also in the other
Domains and outside of the Utility itself
• OT and IT technologies are
converging
Social Media
Smart Metering

Enterprise
Optimization
Work and Asset
Domain
Customer
Domain
Grid
Operations
Domain
Each domain requires analytical capabilities which are inter-related

Customer Optimization
360 degree view of customer
Macro segmentation
Customer value calculation
Micro Segmentation
Simple optimization
Full optimization

Operational Efficiency
360 degree view of the business
Consumption Analysis
Micro Generation Optimization
Grid/Workforce Optimization
Revenue Protection
Risk optimization

Demand Response Optimization
Develop Demand Forecast Models
Forecast Hourly Load vs. Capacity
Establish Optimized DR Program
Execute DR Scheme
Monitor Load in Real Time
Effective DR

Data Warehousing provides unique business value
• Bring the analytics to the data
• Consolidate, manage and
reconcile data for enterprise
business intelligence
• Establish trust, quality and
governance where necessary
• Customer data
• Energy Usage data
• Financial data
• External data
• Combine deep and operational
analytics
• Maintain history for trending and
historical reporting Image: David Castillo Dominici

Thank you!
Q&A
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for our white paper:
"Smart Grid Analytics:
All that Remains to be
Ready is You"

Smart Grid Analytics: All That Remains to be Ready is You

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