Leveraging Energy Data June 5, 2002 Creating Knowledge From Energy Data

LeveragingEnergyData020605.ppt

PredictPower Proprietary and Confidential Information

1

Leveraging Energy DataJune 5, 2002

Creating Knowledge

From Energy Data

LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 2

Agenda

Introduction to PredictPower PredictPower Solutions Value-add for PowerLogic Discussion Next Steps


PredictPower

Advanced technology company based in San Diego, CA

Founded in 1999 Leader in energy market forecasting and enterprise

energy load modeling. Designs and deploys enterprise energy solutions

Advises commercial and industrial customers, utilities, and energy traders on energy policy and specific energy programs

Provides system integration and project management services for energy projects


PredictPower Capabilities

Energy Planning and Information Center Modeling and forecasting Tailored and segmented forecasting Systems engineering Solutions integration Project management Energy consulting

7 56

121110

8 4

21

9 3Independent Meters and

Monitoring Devices

Energy ManagementInformation Systems

Equipment Specificationsand Operating Schedules Utility Invoice Data

text

Specialized Weather Data Sources

EnterpriseOrganization, Structure,

Business Rules, etc.

LaggardsLate

MajorityEarly

MajorityEarly

AdoptorsInnovators

"TheChasm"

Technology Adoption Process

Historical Analyses / Trends / Anomalies

Dat

aIn

form

atio

nK

now

ledg

e

$ $ $

Energy Load and DemandData Warehouse

PredictPower EnterpriseData Warehouse

PredictPower Historical WeatherData Warehouse

PredictPower Enterprise Data Repository

Enterprise EnergyForward-Looking Model

Energy MarketLoad and Price Forecasts

Market FundamentalsData Warehouse

Enterprise Energy Decision Model

Enterprise Energy Presentation Platforms(Alert Technologies / Business Intelligence Platforms)

InvoicesInvoices

Device Monitoring Network

Managed Data Feeds

Energy Market Fundamentals

!Notifications Operational

DecisionsPlanning

and Strategy

!MarketWarnings


{ }

We unite market and enterprise data to create knowledge to move forward

{Energy Market Information}

+ {Enterprise Energy Information}Unique Modeling Technology

= Forward-looking Enterprise Energy Profile

Knowledge that supports better decisions

Making energy a part of your business strategy


Solutions - Enterprise

Baseline

ROI

Energy BusinessIntelligence Platform


Energy Planning andInformation Center

Energy Business Intelligence platform Supports the six components

of ROI improvement Provides management dashboards with

different perspectives Manages both consumption and cost Manages energy as an enterprise resource Manages energy as a financial portfolio


Functionality for Management

Develop energy baselines Prepare and manage energy budgets Develop Key Performance Indicators (KPIs) Identify opportunities for improvement Establish performance objectives and

energy improvement initiatives Monitor energy improvement progress Provide visibility of energy information


Features

Performance objectives and monitoring Energy budgeting and budget management Enterprise consumption modeling and forecasting Climatological (weather / seasonal) normalization Determinant factor analyses (where feasible) Exception reporting and analysis Alerts / notifications Green light / red light metaphors Energy Information Dashboard

Incremental implementation


Key Performance Indicators

Enterprise Structure, Business Rules, etc.


Independent Meters andMonitoring Devices

InvoicesInvoices

Utility Invoice andConsumption Data

text

Weather and ExternalEnvironmental Data

7 56

121110

8 4

21

9 3

Equipment Specifications

and Operating Schedules

Market Fundamentals& Market Forecasts

Historical BaselinesIndustry StandardsComparative BenchmarksLike FacilitiesFacility AttributesProductivity Statistics (Quantity, Cost, etc.)Personnel Measures (Accident Rate / Turnover Rate, etc.)Distribution CostsAvailability / Reliability MeasuresMaintenance CostsOperating CostsCustomer Satisfaction Measures

Generally manage consumption, e.g.: KWhr per Square Foot KWhr per served customer KWhr per delivered product KWhr per $revenue KWhr per operating hour

Analyze Cost, e.g.: $ as percentage of budget $ as percentage of production cost Incremental cost to reduce maintenance cost Incremental cost to improve availability


Determinant Factors

Enterprise Structure, Business Rules, etc.


Independent Meters andMonitoring Devices

InvoicesInvoices

Utility Invoice andConsumption Data

text

Weather and ExternalEnvironmental Data

7 56

121110

8 4

21

9 3

Equipment Specifications

and Operating Schedules

Market Fundamentals& Market Forecasts

Baseline Factors

Climatological Factors

Environmental Factors

Operational Factors

Discretionary Factors


ROI, Aggregate Savings

Comparison, Managed and Unmanaged Electricity Cost

Date

Re

lati

ve C

um

ula

tive

Co

st

Unmanaged

Pentech-managed


ROI, Interval Savings

Hourly Cost, Managed and Unmanaged

Hour of Day

Re

lati

ve H

ou

rly

Co

st

Unmanaged

Pentech-managed


ROI, Reduced Demand

UnmanagedPentech-managed

S10

25

50

75

Current Demand


Aggregated Historical Baseline

Total MBTU, Del Mar Facility

80%

90%

100%

110%

120%

130%

140%

1 4 7

10

13

16

19

22

25

28

31

34

37

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43

46

49

52

Week

Pe

rce

nt

of

Av

era

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20

00

W

ee

kly

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um

pti

on

Year 2000 actuals

Year 2001 actuals

Total kilowatt-hours, Commercial Operations, US


Budget


80%

90%

100%

110%

120%

130%

140%

1 4 7

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Week

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Year 2000 actuals

Year 2001 actuals

Year 2002 budget



Actuals


80%

90%

100%

110%

120%

130%

140%

1 4 7

10

13

16

19

22

25

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31

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37

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Week

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Year 2000 actualsYear 2001 actuals

Year 2002 budgetYear 2002 actuals



Projected


80%

90%

100%

110%

120%

130%

140%

1 4 7

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Week

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Year 2000 actualsYear 2001 actualsYear 2002 budgetYear 2002 actualsYear 2002 projected



Forecast


80%

90%

100%

110%

120%

130%

140%

1 4 7

10

13

16

19

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25

28

31

34

37

40

43

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Week

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Year 2000 actualsYear 2001 actualsYear 2002 budgetYear 2002 actualsYear 2002 projectedYear 2002 forecast



Core Technology - Forecasts

Price forecast by market region Load forecasts by service territory or enterp

rise


Price Signals


Weather Data by ZIP Code- <PredictPower>

- <WeatherReports>- <RequestedLocation>

<ZipCode>92024</ZipCode> <CityName>Encinitas, CA</CityName>

</RequestedLocation>- <Record>

<Station>Carlsbad, McClellan-Palomar Airport</Station> <Distance>5mi N</Distance> <Time>3:53 AM</Time> <Condition>Mostly Cloudy</Condition> <Temperature>44.1</Temperature> <Dewpoint>39.9</Dewpoint> <Humidity>85</Humidity> <Wind>E 6</Wind>

</Record>


Analytical Capabilities

Proprietary physical and market-based models– Custom-fit nonlinear parameterized models– Comprehensive expert knowledge of market dynamics– Scores of high bandwidth, real-time data streams– Data-intensive computations from online database– Computationally intensive nonlinear optimization training

Has the advantages of both nonlinear regression and neural networks – and avoids their weaknesses

– Correctly captures real-world effects– Flexibility and adaptability for complex modeling – Efficient capture of knowledge of energy market dynamics– Avoids overfitting due to large functional search space


Management Team

Peter Czajkowski, President - Akamai, SAIC, system engineering, project management, marketing

Alan Creutz, Ph. D., VP Corp. Strategy - SCT Corporation, $100M/yr energy software division. Director, PDMA. Product management, marketing

Elmer Hung, Ph.D., Chief Scientist - MIT AI Lab, Xerox PARC

Mark Juergensen, VP Sales - President of LAPA. Solar Turbines, SAMS and Advanced Turbine Systems Groups, Sterling Energy


Management Team

Jackson Mueller, Energy Market Analyst - Simpson Paper, Home Depot, Luby’s Diners

Howard Axelrod, Ph.D. - Federal Govt. advisor, utility consultant, econometrician

Charles E. Bayless - Dynegy board member, 3- time utility CEO


Why PredictPower?

Vision Market knowledge Technology Forward-looking analyses Collaborative approach Flexible implementation Strong team Provides you with a competitive edge

Transforms your data into energy knowledge

LeveragingEnergyData020605.ppt

PredictPower Proprietary and Confidential Information

31

Leveraging Energy DataJune 5, 2002

Creating Knowledge

From Energy Data

Documents

Leveraging Energy Data June 5, 2002 Creating Knowledge From Energy Data