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Power your IT infrastructure with Analytics
- 1. “POWER” YOUR IT
INFRASTRUCTURE
Analytics Drives Quicker ROI
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 2. Analytics
Analytics is the science of analysis as to how an business
arrives at an optimal or realistic decision based on
existing data with the application of statistical analysis,
forecasting, operations research, probability and predictive
modeling / data mining.
Analytics is used to discover and understand historical
patterns with an eye to predicting and improving
business performance in the future by integrating silos of
data (structured / unstructured)
Source - Wikipedia
Uncertainty Measurement of Uncertainty Risk Measurement of Risk
2
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 3. Reactive Decision Making
“What happened?”
“Where exactly is the problem?”
“What if these trends continue?”
“How many, how often, where?”
“What’s the best that can happen?”
“What will happen next?”
“What actions are needed?”
“Why is this happening?”
3
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 4. Proactive Decision Making
“What happened?”
“Where exactly is the problem?”
“What if these trends continue?”
“How many, how often, where?”
“What’s the best that can happen?”
“What will happen next?”
“What actions are needed?”
“Why is this happening?”
4
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 5. Benefits of Analytics
By applying analytics of payment history, propensity to pay and
economic data, a major utility was able to significantly reduce risk and
improve collections from its commercial and industrial customers.
• 0% - 50% improvement in corporate collections
• $8 million improvement in first year
• $45 million improvement over five years
By forecasting demand with precise prediction a major utility company
was able to reduce costs in connection up to 50 percent.
By applying analytics on historical demand data and integrating
weather data, major utility companies were able to increase the day
ahead forecast accuracy
• by 3 % saving 7.2 million pounds in the first year of operation.
• By 1 % saving 2 million PLN (Zloty) every year
5
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 6. Opportunity
Utilities have a significant opportunity to transform the
accuracy and reliability of power generation and distribution
through the adoption of more intelligent and responsive energy
management systems.
6
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 7. Critical Areas of Application
7
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 8. Load Forecasting
Over Forecasting
• Loss of revenue due to excess supply
• Obsolence of energy
• Equipment damage and grid indisicpline
Economic grid Promote trade in Economic load High Revenue
Demand discipline energy & capacity dispatch generation
• High Procurement Cost
Under Forecasting
• Reduced Customer Satisfaction
• Loss of revenue due to under-supply
8
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 9. Manage Weather & Event Risk
Temperature Various Factors
Time of the year Day of the week 9
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 11. Asset Management
As a utility operations manager, I must decide
whether to take equipment out of service for needed
maintenance. If I take the equipment out of service
now, during our critical peak demand period, what will
be the impact upon the cost of energy to our
customers and to the company? If I defer the
maintenance, the risk of equipment failure,
unplanned outage and even higher costs is present.
How do I improve the availability of network?
My maintenance costs are going up and my %
unplanned shutdowns are increasing ? What
should be the maintenance strategy I need to adopt?
How do I reduce cost? How do I reduce my
unplanned shutdown by 20 % as I am unable to
locate the root cause of failure? How do I reduce the When failure is not an option,
risk of aging assets failing in groups? Each Utilities opt for Analytics
equipment seems to have a different pattern of
failure? How do I develop early warning signals for
Partial Discharge?
11
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 12. Manage the Asset Reliability Risk
Predictive
Component Progression
to Failure
Predictive Analytics
Failure Symptoms
Condition Monitoring DCS
Alarm
Trip
Maintenance Cost
X
Reactive
Failure 12
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 13. RCA using Analytics
Incident
Normal
Operation
What happened here?
time
13
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 14. 80 % of Transformer failures are predictable
“if a transformer experiences a voltage spike above a certain defined
operational level, which lasted longer than a specified duration and
the voltage spread was greater than a defined range, then this is an
event likely to cause a failure”
Early Warnings for Partial Discharge
(PD) for insulation breakdown has > 75 %
probability of preventing a failure
Root Causes is typically unique for
each asset based on its own
environment envelope in a network
14
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 15. Customer Management
Ability to charge on a real-time basis based on Time of Use (ToU) can help shape
consumption, reduce peak loads to an extent and help shift certain loads to other times of the
day – when the price may be lower
Pricing would be real time, available to only a select set of customers and needs to be
communicated to the customer.
Who are my target customers, What should be the offer price that I should give them?
What would be the impact of that offer? Will an offer of 20 % reduction in price result in
desired reduction in demand ? Am I able to maximize revenue?
Analytics
can help acquire this capability
15
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 16. Analytics – Redefining the need
Power Supply Chain
Management
Utilities business
needs
?
intelligence by adopting
analytics 16
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 17. SAS Analytics Framework for Utilities
Customer Service
Optimize Risk Excellence / High
Segment Operational
Risk Efficiency
Customers
Metrics
Fraud
GIS Modeling
Based
Monitoring
Performance Customer DSM
Modeling
Optimize Asset Demand
Asset ST / Lt
Availability Load
Forecast
Root Cause
Analysis Market
Modeling
Failure Spares
Achieve Asset Early Reduce Loses and
Integrity Forecast
Warnings Increase Savings
Excellence
17
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 18. Utilities - Strategy to Execution
Dashboard Demand Asset Customer Spare parts Risk
Reports Analytics Analytics Analytics Analytics Mgmt
Analytics Processes & Models
Business Rules
Sensor/condition data Customer Data
Inspection / Maintenance Utilities Billing Data
Analytics
Network / Asset Data Data Item / Materials Mgmt
Repository
Analysis Rate Structure
Weather Data Pricing
Complaint Data 18
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 20. Case Study
Step 2
Copenhagen Energy
Challenge Solution Results
Accurate forecasting of Automate forecasting "We expect the costs in connection with the
precise predictions to be able to be reduced
energy demand for process with SAS by up to 50 percent. So it is a matter of
minimal loss Forecasting solution considerable savings."
- Mikael Gynther, Energy Market Manager,
Integrate solution with Copenhagen Energy
operational system
20
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 21. Case Study
Leading Energy
Distribution
Company in India
Challenge Solution Results
Accurate forecasting Automate Successfully being used to forecast energy
forecasting demand at 15 minute intervals
of energy demand for
minimal UI charges process with SAS
Forecasting
solution
21
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 22. Case Study
RWE AG – one of the top 5 largest energy
companies in Europe (43 bln Euro turnover, 20
mln customers in electricity,10 mln in natural gas,
63000 employees)
RWE Poland - electricity distributor for more than
850 000 customers in Warsaw and surrounding
area.
One of 2 private energy companies in Poland (the
other is Vattenfall)
Improvement of accuracy of forecasts by 1% leads
to savings amounted to 2 mln PLN yearly for RWE
Poland
22
Copyright © 2010, SAS Institute Inc. All rights reserved.
- 23. Select SAS Customers in Utilities Industry
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Copyright © 2010, SAS Institute Inc. All rights reserved.
- 24. Thank You
www.sas.com
Copyright © 2010 SAS Institute Inc. All rights reserved.