smart grid analytics: all that remains to be ready is you
DESCRIPTION
This presentation brought to you by Krishan Gupta and Elliott McClements provides information on how analytics can change the speed of the smart grid business today, allowing users to unleash their creativity and focus on what matters: data-driven insights that benefit the business.TRANSCRIPT
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Smart Grid Analytics: All That Remains to be Ready is You
Copyright © 2012 eMeter Corp. All rights reserved.
Free Webcast | June 12, 2012
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The Panel
Copyright © 2012 eMeter Corp. All rights reserved.
Krishan Gupta Director, Product Management eMeter, A Siemens Business
Elliott McClements Big Data Business Solutions Executive, Energy and Utilities IBM Software Group, IM
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eMeter Analytics
Update
The Power of
Analytics
What We Can Do
Together
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Slide 3 Copyright © 2011 eMeter Corp. All rights reserved. http://www.spiegelau.com/
How did Walmart use Garden Hoses to Increase Weekend Sales of Beer by 17%?
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http://media.oregonlive.com/
What does Bird Watching have to do with your Credit Score?
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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%
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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?
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Identifying Theft Patterns
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Energy Diversion Dashboard
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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 9 Copyright © 2011 eMeter Corp. All rights reserved.
Who am I lending to?
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Unbilled Usage by Billing Cycle
Slide 10 Copyright © 2012 eMeter Corp. All rights reserved.
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Unbilled Usage Summary
Slide 11 Copyright © 2012 eMeter Corp. All rights reserved.
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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 12 Copyright © 2011 eMeter Corp. All rights reserved.
$360 Million Stolen Each Year in US Transformers Fail. But Why?
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Outage Details by Distribution Node
Slide 13 Copyright © 2012 eMeter Corp. All rights reserved.
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Service Point Metering
Slide 14 Copyright © 2012 eMeter Corp. All rights reserved.
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Virtual Metered Transformer
Slide 15 Copyright © 2012 eMeter Corp. All rights reserved.
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Transformers Load Monitoring
Slide 16 Copyright © 2012 eMeter Corp. All rights reserved.
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http://earthdaytolucalake.wordpress.com/
15% of infrastructure is used 1% of time. What can we do about it?
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System Load
Slide 18 Copyright © 2012 eMeter Corp. All rights reserved.
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Individual Peak Loads
Slide 19 Copyright © 2012 eMeter Corp. All rights reserved.
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Time of Use Analysis
Slide 20 Copyright © 2012 eMeter Corp. All rights reserved.
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Targeted Demand Response
Slide 21 Copyright © 2012 eMeter Corp. All rights reserved.
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The Possibilities Are Endless…
Slide 22 Copyright © 2012 eMeter Corp. All rights reserved.
Grid Loss Identification
Pricing Analysis
Customer Profiling & Segmentation
Load Modeling & Forecasting
Demand Response Evaluation
Distribution Planning
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© 2012 IBM Corporation
Information Management
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© 2012 IBM Corporation
Information Management
Introducing:
Elliott McClements
Big Data Business Solutions Executive, Energy and Utilities
IBM Software Group, IM
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© 2012 IBM Corporation
Information Management
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 Statistics Spatial Analytics Business Intelligence
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© 2012 IBM Corporation
Information Management
Speed Scalability
Smart Simplicity
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
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© 2012 IBM Corporation
Information Management
Traditional Data Warehouse Complexity
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© 2012 IBM Corporation
Information Management
Data Warehousing – Simplified
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© 2012 IBM Corporation
Information Management Information 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.
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© 2012 IBM Corporation
Information Management Information 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
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© 2012 IBM Corporation
Information Management
Data Stream Processing
FPGA Core CPU Core
Decompress Project (columns)
Restrict Visibility (rows)
Complex ∑ Joins, Aggs, etc.
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© 2012 IBM Corporation
Information Management
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
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© 2012 IBM Corporation
Information Management
Accelerating the Analytic Process
Business Value
Time To Intelligence
Co
mp
etit
ive
Ad
van
tag
e
Data Transfor- mation Data
Cleansing Business Require-
ments
Data Exploration
Model Deployment
Model Development
Model Testing
Model Execution
Data Preparation
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© 2012 IBM Corporation
Information Management
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
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© 2012 IBM Corporation
Information Management
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
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© 2012 IBM Corporation
Information Management
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
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© 2012 IBM Corporation
Information Management
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
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© 2012 IBM Corporation
Information Management
Enterprise Optimization
Work and Asset Domain
Customer Domain
Grid Operations
Domain
Each domain requires analytical capabilities which are inter-related
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© 2012 IBM Corporation
Information Management
Customer Optimization
360 degree view of customer
Macro segmentation
Customer value calculation
Micro Segmentation
Simple optimization
Full optimization
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© 2012 IBM Corporation
Information Management
Operational Efficiency
360 degree view of the business
Consumption Analysis
Micro Generation Optimization
Grid/Workforce Optimization
Revenue Protection
Risk optimization
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© 2012 IBM Corporation
Information Management
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
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© 2012 IBM Corporation
Information Management
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
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Thank you!
Q&A
Copyright © 2012 eMeter Corp. All rights reserved.
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"Smart Grid Analytics: All that Remains to be
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