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

1. Leveraging Energy Data June 5, 2002 Creating Knowledge From Energy Data PredictPower Proprietary and Confidential Information

2. Agenda • Introduction to PredictPower • PredictPower Solutions • Value-add for PowerLogic • Discussion • Next Steps PredictPower Proprietary and Confidential Information

3. 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 Proprietary and Confidential Information

4. PredictPower Capabilities • Energy Planning and Information Center • Modeling and forecasting • Tailored and segmented forecasting • Systems engineering • Solutions integration • Project management • Energy consulting PredictPower Proprietary and Confidential Information

6. 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 PredictPower Proprietary and Confidential Information

7. Solutions - Enterprise Baseline Analyze Predict Energy Business Intelligence Platform React ROI Monitor PredictPower Proprietary and Confidential Information

8. 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 PredictPower Proprietary and Confidential Information

9. 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 PredictPower Proprietary and Confidential Information

10. 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 PredictPower Proprietary and Confidential Information

11. Key Performance Indicators Historical Baselines Industry Standards Comparative Benchmarks Like Facilities Facility Attributes Productivity Statistics (Quantity, Cost, etc.) Personnel Measures (Accident Rate / Turnover Rate, etc.) Distribution Costs Availability / Reliability Measures Maintenance Costs Operating Costs Customer 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 Enterprise Structure, Business Rules, etc. Energy Management Information Systems Independent Meters and Monitoring Devices Utility Invoice and Consumption Data Equipment Specifications and Operating Schedules Weather and External Environmental Data Market Fundamentals & Market Forecasts PredictPower Proprietary and Confidential Information

12. Determinant Factors Discretionary Factors Operational Factors Environmental Factors Climatological Factors Baseline Factors Enterprise Structure, Business Rules, etc. Energy Management Information Systems Independent Meters and Monitoring Devices Utility Invoice and Consumption Data Equipment Specifications and Operating Schedules Weather and External Environmental Data Market Fundamentals & Market Forecasts PredictPower Proprietary and Confidential Information

14. ROI, Aggregate Savings PredictPower Proprietary and Confidential Information

15. ROI, Interval Savings PredictPower Proprietary and Confidential Information

16. ROI, Reduced Demand PredictPower Proprietary and Confidential Information

17. Aggregated Historical Baseline Total kilowatt-hours, Commercial Operations, US PredictPower Proprietary and Confidential Information

18. Budget Total kilowatt-hours, Commercial Operations, US PredictPower Proprietary and Confidential Information

19. Actuals Total kilowatt-hours, Commercial Operations, US PredictPower Proprietary and Confidential Information

20. Projected Total kilowatt-hours, Commercial Operations, US PredictPower Proprietary and Confidential Information

21. Forecast Total kilowatt-hours, Commercial Operations, US PredictPower Proprietary and Confidential Information

24. Core Technology - Forecasts • Price forecast by market region • Load forecasts by service territory or enterprise PredictPower Proprietary and Confidential Information

25. Price Signals PredictPower Proprietary and Confidential Information

26. 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> PredictPower Proprietary and Confidential Information

27. 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 PredictPower Proprietary and Confidential Information

28. 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 PredictPower Proprietary and Confidential Information

29. 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 Management Team PredictPower Proprietary and Confidential Information

30. 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 PredictPower Proprietary and Confidential Information

31. Leveraging Energy Data June 5, 2002 Creating Knowledge From Energy Data PredictPower Proprietary and Confidential Information