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Power your IT infrastructure with Analytics
1.
âPOWERâ 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
âWhat happened?â âWhere exactly is the problem?â âWhat if these trends continue?â âHow many, how often, where?â âWhatâs the best that can happen?â âWhat will happen next?â âWhat actions are needed?â âWhy is this happening?â 3 Copyright © 2010, SAS Institute Inc. All rights reserved.
4.
Proactive Decision Making
âWhat happened?â âWhere exactly is the problem?â âWhat if these trends continue?â âHow many, how often, where?â âWhatâs the best that can happen?â âWhat will happen next?â âWhat actions are needed?â âWhy is this happening?â 4 Copyright © 2010, SAS Institute Inc. All rights reserved.
5.
Benefits of Analytics ï§
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. âą 0% - 50% improvement in corporate collections âą $8 million improvement in first year âą $45 million improvement over five years ï§ By forecasting demand with precise prediction a major utility company was able to reduce costs in connection up to 50 percent. ï§ By applying analytics on historical demand data and integrating weather data, major utility companies were able to increase the day ahead forecast accuracy âą by 3 % saving 7.2 million pounds in the first year of operation. âą 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.
10.
Powering the forecasting
process 10 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 âif 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 failureâ 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 â 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 â 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.
19.
Typical Deployment
19 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 ï§ RWE
AG â 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) ï§ RWE Poland - electricity distributor for more than 850 000 customers in Warsaw and surrounding area. ï§ One of 2 private energy companies in Poland (the other is Vattenfall) ï§ 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 23 Copyright © 2010, SAS Institute Inc. All rights reserved.
24.
Thank You
www.sas.com Copyright © 2010 SAS Institute Inc. All rights reserved.