2007 Annual Validation Preliminary Review of Residential Algorithm & Estimate of Migrations February 27, 2007

2 Overview Algorithm Accuracy –Usage window ( thru ) Evaluate extending window vs sliding window –Evaluate current algorithm using survey results with new models –Modify current algorithm to optimize classification accuracy Residential Migration –Estimate migrations for 2007 Annual Validation –Estimate future year to year migrations

3 Review of Algorithm Classification Use Residential Survey response data in conjunction with responder usage data to build an algorithm to predict heating fuel for each ESIID Use regression between actual meter readings for a premise and the RESHIWR and RESLOWR new model profiles kWh for the same time periods Use reads during shoulder and winter months for selected usage window Omit reads during periods of very low use (no/low occupancy) from the regression 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

4 Apply current algorithm, with new models and additional usage data Classify each usage value 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 First Steps Taken

5 1.If the highest winter reading kWh/day is less than 15 kWh/day then assign “RESLOWR” 2.If R 2 RESHIWR > 0.60 and R 2 RESHIWR > R 2 RESLOWR then assign “RESHIWR” 3.If the number of readings available > 9 and R 2 RESHIWR > 0.90 and (R 2 RESHIWR ) > R 2 RESLOWR and Winter Max kWh/day > 50 then assign “RESHIWR” 4.If the number of readings available > 9 and R 2 RESHIWR > 0.95 and (R 2 RESHIWR ) > R 2 RESLOWR and Winter Max kWh/day > 60 then assign “RESHIWR” 5.Otherwise assign “RESLOWR” Current Classification Algorithm Rules

6 Apply the current algorithm with new models for the 3,938 validated survey responses and compare heating system response to the algorithm classification Overall Accuracy 93.6% –92.2% for Reshiwr –95.2% for Reslowr Current Algorithm with new models is some what biased towards Reslowr Analysis of extending vs sliding the usage window indicated that extending produced more accurate results Definitely not electric heat! Current Classification Algorithm Results

7 Adjusted algorithm to improve accuracy, and minimize classification bias between RESHI and RESLO Classify each usage value 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 >= December 1 and stop <= March 1 Shoulder:start >= September 20 and stop <= December 1 start >= March 1 and stop <= May 11 Seasonal Crossover Reads If 60% or more of the days in a reading are in the shoulder season the reading is classified as a shoulder month If 60% or more of the days in a reading are in the winter season the reading is classified as a winter month Other wise the reading is discarded Next - Steps Taken to Optimize Accuracy

8 1.If the highest winter reading kWh/day is less than 18 kWh/day then assign “RESLOWR” 2.If R 2 RESHIWR > 0.60 and R 2 RESHIWR > R 2 RESLOWR then assign “RESHIWR” 3.If the number of readings available > 9 and R 2 RESHIWR > 0.90 and (R 2 RESHIWR ) > R 2 RESLOWR and Winter Max kWh/day > 56 then assign “RESHIWR” 4.Otherwise assign “RESLOWR” Proposed Classification Algorithm Rules

9 Apply the proposed algorithm with new models for the 3,936 validated survey responses and compare heating system response to the algorithm classification Overall Accuracy 94.0% –94.0% for Reshiwr –93.9% for Reslowr Proposed Algorithm with new models is unbiased Definitely not electric heat! Results Using Proposed New Algorithm

10 Annual Validation 2007 Estimate of Migrations

11 Estimate of Future Year to Year Migrations

12 Current algorithm will need to be fine tuned based on new models and additional usage Residential Annual Validation for 2007 is likely to result in somewhat higher migrations than had been projected With continued use of the new models, future annual migration rates should be lower than the 2007 rate Finalized algorithm will be developed in March based on usage available at that time –PWG approval March –COPS approval April –TAC approval May –List to TDSP in June as required in LPG Fine tuning the algorithm should be considered annually prior to starting Annual Validation Conclusions