AEIC Annual Load Research Conference September 12, 2006 - Reno, NV

1. 1 AEIC Annual Load Research Conference September 12, 2006 - Reno, NV

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3. 3 ERCOT requires a fifteen (15) minute settlement interval Vast majority of Customers do not have this level of granularity. Profiles are created using adjusted static models Models are dependent on season, day of week, time of day and weather Backcasted Profiles are generated the day following a trade day and used for all settlements (initial, final and true-up) Load Profiling: Converts monthly NIDR reads to fifteen (15) minute intervals Enables the accounting of energy usage in settlements Allows the participation of these Customers in the retail market (reduces barrier to entry)

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5. 5 Minimum of two weather stations per weather zoneMinimum of two weather stations per weather zone

6. 6 ERCOT in conjunction with Profiling Working Group establishes the rules for Profile ID assignment and publishes in the form of a Decision Tree on the ERCOT website Annual Validation is a process established by the Market to annually review and update Profile ID assignments based on the rules defined in the Decision Tree Historically, May 1 thru April 31 meter reads were used to determine the Annual Validation assignment. The process normally began in June and completed in January.

7. 7 Oct. 2001 Initial Validation Profile IDs were assigned by TDSPs prior to Market Open Validation started in 2001 and was not completed until Sept. 2002 2002 Annual Validation Not performed due to 2001 Initial Validation still in progress PWG sub team changed methodology from using billing month to usage month 2003 Annual Validation Large volume of migrations (1.5 million out of 4.9 million ESIIDs) 2004 Annual Validation Large volumes of changes were identified (1.0 million out of 5.4 million ESIIDs) Annual Validation suspended to allow time to improve assignment process 2005 Annual Validation Some methodology changes were identified which still resulted in large volumes of migrations (0.5 million out of 5.1 million ESIIDs) Market delayed sending in transactions and ultimately decided to only send in a subset of changes identified

8. 8 Residential Assignment Rules2001 - 2004 Winter Ratio >=1.5 RESHIWR Winter Ratio < 1.5 RESLOWR

9. 9 Preliminary Residential Assignment Rules for Annual Validation 2005 Do not replace a non-default assignment with a default assignment Apply Dead-Bands RESHIWR goes to RESLOWR if WR = 1.0 RESLOWR goes to RESHIWR if WR > 1.8 Dead-Bands do not apply if currently a default assignment kWh Minimums WR numerator = 20 then assign RESLOWR

10. 10 Additional Profile Assignment Improvement Ideas Use a statistical approach to correlate premise usage to profile usage. Use a residential survey to obtain the necessary data to relate usage patterns to heating system type. More accurately account for weather variations Account for periods of low/no occupancy Move calculation responsibility to ERCOT from TDSPs Change time period for submission of assignment change transactions During the original October/November timeframe for submitting changes, the RESHIWR and RESLOWR profiles are significantly different RESHIWR and RESLOWR profiles are quite similar during the summer months

11. 11 Design: 41,000 bilingual survey forms mailed Stratified by Weather Zone and Profile Type 2,563 RESHIWR per Weather Zone 2,562 RESLOWR per Weather Zone Response Survey responses were identified to allow connecting the response to usage history 4,669 responses as of 09/30/2005 11.4% response rate

12. 12 Questions from Residential Survey pertinent to Electric Heat Analysis

13. 13 8.5% 12.6% 10.5% 9.9% 13.6% 11.3% 10.6% 14.1%

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18. 18 Performed visual inspection of usage patterns for each survey response 4,630 responses indicated either a �Single-Family Dwelling� or �Multi-Family Dwelling� and a primary home heating type of either �Electricity� or �Natural gas or bottled gas (propane/butane) 673 (14.5%) responses to the home heating type were deemed invalid by examination of their seasonal usage pattern 3,957 (85%) responses were used to develop an improved Profile Type classification algorithm

19. 19 Survey Response Validation - Electric Heat Example

20. 20 What we found out from the Survey Saturation of Electric Heat varied considerably across weather zones Saturation of Electric Heat was inconsistent with breakdown between RESHIWR and RESLOWR 30% of Survey responders reporting Electric Heat were assigned to RESLOWR 14% of Survey responders reporting No Electric Heat were assigned to RESHIWR There is very little year-to-year change in heating system fuel actually occurring The percent of newer homes using electric heat varies considerably across weather zones (37% Coast � 84 South %)

21. 21 Why Does Assignment Accuracy Matter? Profile assignment errors create two types of load profile estimation errors Assignment of billing kWh to the days within the billing period (RESHIWR assigns more kWh than RESLOWR to cold days) Assignment of daily kWh to the intervals within the day (RESHIWR assigns more kWh to morning intervals)

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23. 23 Residential Profile Comparison - FWEST Reshiwr vs. Reslowr

24. 24 Findings and Next Steps ERCOT�s Profile ID Assignment process has resulted in unacceptably high migration rates Dead - bands would reduce migration but could do more harm than good in terms of assignment accuracy The impact of Profile ID miss-assignment is significant at the ESIID level Undertake an effort to develop a new and improved assignment process with a goal of reducing migration and improving accuracy More improvements are needed

25. 25 Classification Algorithm Overview Use Residential Survey response data in conjunction with responder usage data to build an algorithm to predict heating fuel Use regression between actual meter readings for a premise and the RESHIWR and RESLOWR profile kWh for the same time periods Use reads during shoulder and winter months for several (4.5) years Omit reads during periods of very low use (no/low occupancy) Omit outlier reads and require some reads to exceed a minimum kWh/day threshold in order to assign RESHIWR Assign the better fitting profile to the ESIID

26. 26 For each ESI ID with a survey response usage values were selected from Lodestar for the January 2002 � September 2005 time period Each usage value was converted into units of kWh/day and any read covering a period longer than 44 days was dropped Each usage value was classified as a winter or shoulder reading Only shoulder and winter readings were used in the analysis Winter/Shoulder: start > September 20 and stop < May 11 Winter: start > November 15 and stop < March 15 Shoulder: all others Usage values were screened for high and low outlier usage values

27. 27 For each ESI ID compute a mean and standard deviation of the kWh/day values for the winter and shoulder readings and use these to �normalize� each usage value Usage value dropped if: Z > 3 and kWh/day > 100 Z > 3.5 Z < -2 kWh/day < 5 Low Occupancy

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30. 30 1,006 ESI IDs (21.7%) with one or more usage values screened 2,414 usage values were screened out 1,825 usage values screened out because < 5 kWh/day If an ESI ID had fewer than 3 winter readings or fewer than 3 shoulder readings it was classified as �RESLOWD� (Residential Low Winter Ratio Default) and was not used for fine tuning the algorithm

31. 31 If an ESI ID has (and uses) electric heating, then the winter and shoulder usage values for that premise should be more similar to the RESHIWR profile kWh than to the RESLOWR profile kWh The profile kWh for a day reflects the weather conditions associated with that day in the specific weather zone as well as the day type (day-of-week/holiday) and season of the year To perform the comparison for an ESI ID, the profile kWh is summed across the intervals for the days in each of its meter reading periods (shoulder and winter months only)

32. 32 For each fall-winter-spring time period e.g., fall 2004 � spring 2005 the profile kWh is scaled to equal the sum of the ESI ID�s meter kWh for that time period The correlation between the actual metered kWh and the scaled profile kWh for those readings is computed for each ESI ID The R2 correlation is determined with a weighted linear regression analysis with no intercept term Each reading is weighted as follows: Shoulder reading weight = 1 Winter reading weight = Winter reading weight = 1 if RESHIWR kWh < RESLOWR kWh The weighting process associates more importance with winter readings for which the RESHIWR kWh is greater than the RESLOWR kWh

33. 33 New Algorithm Improvement Example

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35. 35 If the highest winter reading kWh/day is less than 15 kWh/day then assign �RESLOWR� If R2RESHIWR > 0.60 and R2RESHIWR > R2RESLOWR then assign �RESHIWR� If the number of readings available > 9 and R2RESHIWR > 0.90 and (R2RESHIWR + 0.010) > R2RESLOWR and Winter Max kWh/day > 50 then assign �RESHIWR� If the number of readings available > 9 and R2RESHIWR > 0.95 and (R2RESHIWR + 0.015) > R2RESLOWR and Winter Max kWh/day > 60 then assign �RESHIWR� Otherwise assign �RESLOWR�

36. 36 Algorithm fine tuning was an iterative process to tune each classification criterion on the previous slide individually Each classification criterion was adjusted to minimize misclassification error based on validated survey responses For each iteration, misclassified ESI IDs were examined graphically to assess the accuracy of the Profile Type assignment and to establish new criteria When the fine tuning was complete 184 (4.6%) validated survey responses regarding heating system type were different than the algorithm classification � most had usage patterns which were ambiguous

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39. 39 For the final version of the algorithm 3,773 (95.4%) validated survey responses regarding heating system type agreed with the algorithm classification

40. 40 62% of the 578,572 AV 2005 Profile Type changes agreed with the algorithm classification Changes to RESHIWR were significantly more accurate (78.4%) than changes to RESLOWR (43.5%) Accuracy of the changes by weather zone ranged from a low of 59.8% in the SOUTH zone to a high of 68.8% in the EAST zone The Residential population would have had somewhat more accurate Profile Type assignments as a result of conducting AV 2005 (81.4% vs. 78.7%) The market decided to allow only changes which were in agreement with the algorithm (358,000 changes were submitted)

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43. 43 Conclusions The survey successfully provided data necessary to build a classification algorithm for electric heating and establish its accuracy. The classification algorithm at 96% accuracy was a significant improvement over the winter ratio method The improved accuracy will lead to assignment stability Profile assignments and shapes are in a feedback loop and improve each other The new algorithm uses load profile shapes to make profile assignments With updated load research analysis based on the new assignments, more accurate load profile shapes will be developed as a result of a more homogeneous population The more accurate load profile shapes should lead to better assignments ERCOT has completed load research analysis using the new profile assignments and is developing new profile models based on those latest estimates

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