Power your IT infrastructure with Analytics

“POWER” YOUR IT
INFRASTRUCTURE
Analytics Drives Quicker ROI

Copyright © 2010, SAS Institute Inc. All rights reserved.

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


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


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


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


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


Critical Areas of Application

7


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


Manage Weather & Event Risk

Temperature Various Factors

Time of the year Day of the week 9


Powering the forecasting process

10


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


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


RCA using Analytics
Incident

Normal
Operation

What happened here?
time
13


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


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


Analytics – Redefining the need

Power Supply Chain
Management

Utilities business
needs
?
intelligence by adopting
analytics 16


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


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


Typical Deployment

19


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


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


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


Select SAS Customers in Utilities Industry

23


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