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

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%?

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?

Identifying Theft Patterns

Energy Diversion Dashboard




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 US Transformers 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 Statistics Spatial Analytics Business Intelligence



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



Traditional Data Warehouse Complexity



Data Warehousing – Simplified


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.


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



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

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



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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"Smart Grid Analytics: All that Remains to be

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