1. Texas Smart Grid Consortium November 8 th 2010

2. Transition Probability Chaos Theory attractors Signal to Noise ratio MultiColinearity Remove the Complexity

3. Egg Hatchling Pond Scum Juvenile 20% 80% 5% Transition Probability Golden Shiner

4. Child Star “Adult” Star Super Star Pond Scum Rehab Politician 10% 70% 20% 40% 40% 100% Late Night Infomercial Late Night Infomercial Security Guard Security Guard 20% Transition Probability

5. Interval Read Poor Signal Quality Pond Scum 92% 2% 6% MeterSense Repository Versioning Auditing AggregationMeterSense Repository Versioning Auditing Aggregation Estimate Override Edit Guide Edits Estimate Override Edit Guide Edits Metersense Certified 10 rules Based Validation routines Metersense Certified 10 rules Based Validation routines Service Orders Service Orders 98% 2% 1% 99% Quality Reads Quality Reads 100% Transition Probability AMI Data Cleansing

7. Noise Signal Can’t Hear Hear Hear Clearly Ratios Signal to Noise

8. Disgust Desperation Won’t Use Can’t Refuse Locked Inside Taking a Snooze Ratios Port-a-Potty Behavior Alcohol

9. Complexity of Integration Data Quality Won’t Use Use Drives Innovation Ratios MultiCollinearity Amount of Data

11. Chaos Theory Attractors

12. Attractor Attractors

13. Attractors

14. Attractors

16. Savings $645.00 Total Cost $3.58 Daily Cost 1145 Total Kwh 610 On Peak Flat Rate $534.00 Total Cost $2.96 Daily Cost 1037 Total Kwh 558 On Peak Time of Use Rate Savings$111.00 Time based rates comparison Avg. Temp 25 F

17.  Identify high line losses  Loss revenue loss protection  Blink momentary interruption analysis  Secondary line theft Identification  Balance Primary loads  Phase Balancing and circuit utilization  Equipment trouble shooting  Transformer optimization incorporated with weather conditions  Scheduled Preventative Maintenance  Improve Voltage regulation and capacitor placement  Customer load profiles  Customer class load profiles  Network location load profiles  Top contributors to system peaks  Water non-compliance usage  Water leak detection  Water main leak detection  Water pressure analysis MDM Iphone / Android structure Reporting Structure Application sharing Engineering Firms Consultants User Community Web Based sharing engine Application sharing Engineering Firms Consultants User Community Web Based sharing engine BPO Decision Automation

18. Wholesale Energy Purchases – Retail Member energy sales = Lost revenue Electric Distribution Line Loss

19. Peak Load Contribution by Rate Class Load Analytics

20. Loss 4-8% Diagnostic System Losses Causes ; Theft, Meter issues, Voltage variance, equipment sizing placement, disturbances

21. Select System Top Circuit line loss by percentage of revenue System Line Loss Report Total Losses for this period Same Period last year

22. Loss 4-8% Diagnostic System Losses Secondary Power Theft

24. Municipal Utility Energy Demand 30 to 40 percent of the electricity is used by water utilities Reduce Peak demand charges by 20% Identify Lift Station Malfunctions Run Pumps Off Peak Run Pumps Off Peak

25. Conservation Monitoring Conservation and threshold monitoring of specific energy classes allowing performance comparison and Device monitoring and Energy Resource Management

26. Charleston SC. expects to save $18.5 million over 15yrs through performance contracts that include saving water and energy. Galveston TX. Through more accurate water meters expects to save $1.3 million per year Glendale WI. Water Efficiency contributes to LEED certification