ERCOT Analysis of 2005 Residential Annual Validation Using the Customer Survey Results

ERCOT Analysis of 2005 Residential Annual Validation Using the Customer Survey Results

ERCOT Load Profiling

Presented to PWG - October 26, 2005

2

Residential Survey AnalysisResidential Survey Analysis

Objective is to quantify the accuracy of the 2005 Annual Validation Residential Profile Assignment Changes

Based on the survey responses in conjunction with each responder’s usage history build an accurate algorithm to predict presence and use of electric space heating

Apply the algorithm to each of the 578,572 ESI IDs with 2005 Annual Validation Profile Assignment changes

Determine the percent of changes which are correct

RESHIWR with electric heat RESLOWR without electric heat

3

Residential Survey AnalysisResidential Survey Analysis

If the majority of changes are correct then AV 2005 is improving Profile Assignment Accuracy

Survey status

41,000 surveys were mailed on August 30 (plus earlier Pilot Survey)

Returns have been received and processed

Response rate is 11.4% (4,669 returns) as of 09/30/2005 cut-off

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 responses were used to develop the final Profile Type classification algorithm

4

Survey Response ValidationSurvey Response Validation

An initial algorithm was developed using Pilot Survey responses as a basis

The initial algorithm was applied to each of the 4,630 responses (with valid dwelling type and home heating type) to identify heating type responses which were inconsistent with the algorithm classification

857 inconsistent responses were identified … the usage patterns of each were examined graphically to assess whether the survey response was highly likely to be incorrect or not.

673 responses were deemed to be incorrect and were eliminated from the subsequent analysis undertaken to fine tune the algorithm

184 responses were deemed to be either correct or possibly correct and were retained in the analysis

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Survey Response ValidationSurvey Response Validation

SURVEY RESPONSE VALIDATION SUMMARY

Weather Zone

Valid Dwelling and Heating Type

Used in Algorithm Development

Percent Used

COAST 432 374 87%EAST 642 555 86%

FWEST 536 455 85%NCENT 503 446 89%NORTH 691 609 88%SCENT 575 480 83%SOUTH 538 443 82%WEST 713 595 83%

TOTAL 4,630 3,957 85%

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Algorithm Development – Usage ScreeningAlgorithm Development – Usage Screening

For each ESI ID with a survey response usage values were selected from Lodestar for the January 2002 – September 2005 time period

Usage values were screened to prevent high and low outlier usage values as well as usage during periods of low/no occupancy from unduly affecting the Profile Type classification for an ESI ID and to improve the response validation process

Each usage value was converted into units of kWh/day to offset the impact of long or short reads (any read covering a period longer than 44 days was dropped from the analysis)

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

7

Algorithm Development – Usage ScreeningAlgorithm Development – Usage Screening

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

nStDeviatio

MeanUsageValueZ

Outliers

8

Usage Screening ExamplesUsage Screening Examples

Start Date Stop Date kWh kWh/day ZDropped

kWhDropped kWh/day

3/4/2002 4/3/2002 865 28.8 0.02 . .4/3/2002 5/3/2002 445 14.8 -0.61 . .

10/30/2002 12/3/2002 1,581 46.5 0.82 . .3/4/2003 4/3/2003 495 16.5 -0.53 . .4/3/2003 5/2/2003 309 10.7 -0.80 . .

10/1/2003 11/3/2003 380 11.5 -0.76 . .11/3/2003 12/4/2003 248 8.0 -0.92 . .

3/4/2004 4/1/2004 170 6.1 -1.00 . .4/1/2004 5/5/2004 185 5.4 -1.03 . .

9/30/2004 10/29/2004 352 12.1 -0.73 . .10/29/2004 12/2/2004 281 8.3 -0.90 . .

3/4/2005 4/5/2005 981 30.7 0.10 . .4/5/2005 5/3/2005 889 31.8 0.15 . .1/4/2002 2/4/2002 1,309 42.2 0.62 . .2/4/2002 3/4/2002 1,815 64.8 1.64 . .

12/3/2002 1/7/2003 2,189 62.5 1.54 . .1/7/2003 2/3/2003 1,802 66.7 1.73 . .2/3/2003 3/4/2003 1,984 68.4 1.80 . .

12/4/2003 1/7/2004 240 7.1 -0.96 . .1/7/2004 2/4/2004 171 6.1 -1.00 . .2/4/2004 3/4/2004 . . 132 4.6

12/2/2004 1/5/2005 1,228 36.1 0.35 . .1/5/2005 2/2/2005 1,437 51.3 1.03 . .2/2/2005 3/4/2005 1,175 39.2 0.49 . .

mean 28.3428standard deviation 22.2221

Usage less than 5 kWh/day dropped

9


Usage with Z < -2.00 dropped


kWhDropped kWh/day

2/25/2002 3/25/2002 1931 68.964 0.22 . .3/25/2002 4/25/2002 1570 50.645 -0.81 . .9/23/2002 10/22/2002 1880 64.828 -0.02 . .

10/22/2002 11/20/2002 1784 61.517 -0.20 . .2/24/2003 3/26/2003 2284 76.133 0.62 . .3/26/2003 4/25/2003 1710 57 -0.46 . .9/24/2003 10/22/2003 . . -2.47 595 21.25

10/22/2003 11/20/2003 1488 51.31 -0.78 . .2/26/2004 3/25/2004 1561 55.75 -0.53 . .3/25/2004 4/23/2004 1527 52.655 -0.70 . .9/23/2004 10/22/2004 1791 61.759 -0.19 . .

10/22/2004 11/22/2004 1720 55.484 -0.54 . .2/24/2005 3/24/2005 1424 50.857 -0.80 . .3/24/2005 4/26/2005 1296 39.273 -1.46 . .1/25/2002 2/25/2002 2492 80.387 0.86 . .

11/20/2002 12/23/2002 2719 82.394 0.97 . .12/23/2002 1/24/2003 3349 104.656 2.23 . .1/24/2003 2/24/2003 2877 92.806 1.56 . .

11/20/2003 12/26/2003 2526 70.167 0.28 . .12/26/2003 1/26/2004 2297 74.097 0.51 . .1/26/2004 2/26/2004 2676 86.323 1.19 . .

11/22/2004 12/27/2004 2592 74.057 0.50 . .12/27/2004 1/26/2005 1980 66 0.05 . .1/26/2005 2/24/2005 1871 64.517 -0.03 . .


10


Usage with Z > 3.50 dropped


kWhDropped kWh/day

3/5/2002 4/4/2002 355 11.8333 -1.21 . .4/4/2002 5/7/2002 433 13.1212 -1.02 . .

10/4/2002 11/1/2002 435 15.5357 -0.67 . .11/1/2002 12/5/2002 589 17.3235 -0.41 . .3/10/2003 4/8/2003 324 11.1724 -1.31 . .

4/8/2003 5/8/2003 459 15.3 -0.70 . .10/3/2003 11/4/2003 504 15.75 -0.64 . .11/4/2003 12/4/2003 491 16.3667 -0.55 . .

3/6/2004 4/6/2004 451 14.5484 -0.81 . .4/6/2004 5/5/2004 552 19.0345 -0.15 . .

10/5/2004 11/3/2004 . . 3.54 1282 44.206911/3/2004 12/4/2004 692 22.3226 0.33 . .

3/9/2005 4/7/2005 444 15.3103 -0.70 . .4/7/2005 5/9/2005 773 24.1563 0.60 . .1/8/2002 2/5/2002 476 17 -0.45 . .2/5/2002 3/5/2002 725 25.8929 0.85 . .

12/5/2002 1/9/2003 870 24.8571 0.70 . .1/9/2003 2/5/2003 667 24.7037 0.68 . .2/5/2003 3/10/2003 690 20.9091 0.12 . .

12/4/2003 1/7/2004 678 19.9412 -0.02 . .1/7/2004 2/6/2004 682 22.7333 0.39 . .2/6/2004 3/6/2004 619 21.3448 0.19 . .

12/4/2004 1/7/2005 949 27.9118 1.15 . .1/7/2005 2/8/2005 745 23.2813 0.47 . .2/8/2005 3/9/2005 508 17.5172


11

Usage Screening ResultsUsage Screening Results

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

12

Algorithm BasicsAlgorithm Basics

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 loads than to the RESLOWR profile loads

The profile loads for a day reflect 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 loads are summed across the intervals for the days in each of its meter reading periods (shoulder and winter months only)

Example: If the reading starts on January 15, 2002 and ends on February 15, 2002, the RESHIWR and RESLOWR profile loads are summed for January 15 – February 14 to obtain comparable profile readings for the same time period

13

Algorithm BasicsAlgorithm Basics

For each fall-winter-spring time period e.g., fall 2004 – spring 2005 the profile loads are scaled to equal the sum of the ESI ID’s readings for that time period

If the ESI ID used 5,000 kWh for the fall-winter-spring time period and the RESHIWR profiled loads for that time period summed to 4,000 kWh, each of the RESHIWR profiled loads would be multiplied by 1.25

If the RESLOWR profiled loads for that time period summed to 2,500 kWh, each of the RESLOWR profiled loads would be multiplied by 2.0

The correlation between the actual readings and the sum of the scaled profile loads for those readings is computed for each ESI ID

The R2 correlation is determined with a weighted linear regression analysis

Each reading is weighted as follows:

Shoulder reading weight = 1

Winter reading weight = 1 or

Winter reading weight = 1 if RESHIWR kWh < RESLOWR kWh

The weighting process associates more importance with winter readings for which the RESHIWR is greater than the RESLOWR kWh

kWhRESLOWR

kWhRESHIWR2

14

Algorithm Basics – An ExampleAlgorithm Basics – An Example

START DATE STOP DATE KWHRESHIWR

KWHSCALED RESHIWR

KWHRESLOWR

KWHSCALED RESLOWR

KWH WEIGHT

01/18/02 02/18/02 1,525 1,245 1,535 683 1,231 3.6502/18/02 03/19/02 1,301 1,030 1,270 648 1,168 3.1803/19/02 04/18/02 910 755 931 741 1,337 1.00

3,736 3,029 3,736 2,072 3,736

10/17/02 11/14/02 692 670 850 672 1,274 1.0011/14/02 12/18/02 1,658 1,177 1,492 715 1,356 1.0012/18/02 01/21/03 1,843 1,439 1,824 768 1,456 3.7501/21/03 02/18/03 1,749 1,199 1,520 622 1,179 3.8502/18/03 03/20/03 1,297 980 1,242 649 1,231 1.0003/20/03 04/21/03 713 808 1,025 768 1,456 1.00

7,952 6,273 7,952 4,194 7,952

10/16/03 11/18/03 702 858 970 948 1,455 1.0011/18/03 12/16/03 1,026 934 1,057 606 930 3.0912/16/03 01/21/04 1,834 1,406 1,591 805 1,236 3.4901/21/04 02/19/04 1,675 1,254 1,419 631 968 3.9802/19/04 03/17/04 772 722 817 567 871 1.0003/17/04 04/21/04 786 831 941 870 1,336 1.00

6,795 6,005 6,795 4,426 6,795

10/14/04 11/15/04 1,199 984 1,279 1,062 1,825 1.0011/15/04 12/17/04 1,286 934 1,214 709 1,217 1.0012/17/04 01/18/05 1,941 1,354 1,759 753 1,294 3.5901/18/05 02/18/05 1,656 1,174 1,525 668 1,148 3.5102/18/05 03/22/05 1,012 911 1,183 679 1,166 1.0003/22/05 04/19/05 701 643 835 667 1,145 1.00

7,795 6,000 7,795 4,539 7,795

RSQUARE RESHIWR RESLOWR

99.14% 91.52%

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Algorithm Basics – An ExampleAlgorithm Basics – An Example

Example ESIID Plotted

-

500

1,000

1,500

2,000

2,500

Feb-0

2

Apr-0

2

Jun-

02

Aug-0

2

Oct-02

Dec-02

Feb-0

3

Apr-0

3

Jun-

03

Aug-0

3

Oct-03

Dec-03

Feb-0

4

Apr-0

4

Jun-

04

Aug-0

4

Oct-04

Dec-04

Feb-0

5

Apr-0

5

Reading Mid-point

kWh

ACTUAL SCALED RESHIWR SCALED RESLOWR

16

Algorithm - Classification RulesAlgorithm - Classification Rules

1. If the highest winter reading kWh/day is less than 15 kWh/day then assign “RESLOWR”

2. If R2RESHIWR > 0.60 and R2

RESHIWR > R2RESLOWR

then assign “RESHIWR”

3. If the number of readings available > 9

and R2RESHIWR > 0.90

and (R2RESHIWR + 0.010) > R2

RESLOWR

and Winter Max kWh/day > 50


4. If the number of readings available > 9

and R2RESHIWR > 0.95

and (R2RESHIWR + 0.015) > R2

RESLOWR

and Winter Max kWh/day > 60


Otherwise assign “RESLOWR”

17

Algorithm – Fine TuningAlgorithm – Fine Tuning

Algorithm fine tuning was an iterative process to tune each classification criterion on the previous slide individually

Each classification criterion was adjusted to minimize miss-classification error based on validated survey responses

For each iteration, miss-classified 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

18


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

Definitely not electric heat!

19

Correctly Classified ExampleCorrectly Classified Example

Survey and algorithm both indicated electric heat, “RESHIWR”

20

Correctly Classified ExampleCorrectly Classified Example

Survey and algorithm both indicated gas heat, RESLOWR”

21


For the final version of the algorithm 184 (4.6%) validated survey responses regarding heating system type disagree with the algorithm classification – these usually had ambiguous usage patterns

22

Survey and Algorithm Disagree on ClassificationSurvey and Algorithm Disagree on ClassificationCOAST ExampleCOAST Example

Survey said gas heat, algorithm said electric

23

Survey and Algorithm Disagree on ClassificationSurvey and Algorithm Disagree on ClassificationEAST ExampleEAST Example

Survey said gas heat, algorithm said electric

24

Survey and Algorithm Disagree on ClassificationSurvey and Algorithm Disagree on ClassificationEAST ExampleEAST Example

Survey said electric heat, algorithm said gas

25

Survey and Algorithm Disagree on ClassificationSurvey and Algorithm Disagree on ClassificationNCENT ExampleNCENT Example

Survey said electric heat, algorithm said gas

26

Algorithm to Annual Validation ComparisonAlgorithm to Annual Validation Comparison

534 (13.5%) validated survey responses had 2005 Annual Validation assignments which were different than the assignments for the finalized algorithm … these were examined graphically to determine whether the algorithm was making more accurate assignments

27

AV 2005 and Algorithm Disagree on ClassificationAV 2005 and Algorithm Disagree on ClassificationEAST ExampleEAST Example

AV 2005 said change to RESLOWR, algorithm said RESHIWR

28

AV 2005 and Algorithm Disagree on ClassificationAV 2005 and Algorithm Disagree on ClassificationFWEST ExampleFWEST Example


29

AV 2005 and Algorithm Disagree on ClassificationAV 2005 and Algorithm Disagree on ClassificationNCENT ExampleNCENT Example


30

AV 2005 and Algorithm Disagree on ClassificationAV 2005 and Algorithm Disagree on ClassificationNORTH ExampleNORTH Example


31

AV 2005 and Algorithm Disagree on ClassificationAV 2005 and Algorithm Disagree on ClassificationSOUTH ExampleSOUTH Example


32

AV 2005 and Algorithm Disagree on ClassificationAV 2005 and Algorithm Disagree on ClassificationWEST ExampleWEST Example


33



34

AV 2005 and Algorithm Disagree on ClassificationAV 2005 and Algorithm Disagree on ClassificationCOAST ExampleCOAST Example

AV 2005 said change to RESHIWR, algorithm said RESLOWR

35



36

AV 2005 and Algorithm Disagree on ClassificationAV 2005 and Algorithm Disagree on ClassificationNCENT ExampleNCENT Example


37

AV 2005 and Algorithm Disagree on ClassificationAV 2005 and Algorithm Disagree on ClassificationNORTH ExampleNORTH Example


38

AV 2005 and Algorithm Disagree on ClassificationAV 2005 and Algorithm Disagree on ClassificationWEST ExampleWEST Example


39

SUMMARY OF 2005 ANNUAL VALIDATION PROFILE TYPE CHANGES

AV 2005 CHANGES POPULATION BEFORE POPULATION AFTERWeather

Zone Profile Type Total Percent Total Percent Total Percent

RESHIWR 91,482 50.0% 365,518 19.8% 365,590 19.9%COAST RESLOWR 91,410 50.0% 1,476,158 80.2% 1,476,086 80.1%

TOTAL 182,892 1,841,676 1,841,676 NET TO HIGH 72

RESHIWR 13,260 58.6% 67,553 35.8% 71,444 37.9%EAST RESLOWR 9,369 41.4% 121,069 64.2% 117,178 62.1%

TOTAL 22,629 188,622 188,622 NET TO HIGH 3,891

RESHIWR 10,751 55.2% 42,262 29.7% 44,277 31.1%FWEST RESLOWR 8,736 44.8% 99,952 70.3% 97,937 68.9%

TOTAL 19,487 142,214 142,214 NET TO HIGH 2,015

RESHIWR 141,773 57.1% 819,348 39.6% 854,445 41.3%NCENT RESLOWR 106,676 42.9% 1,250,562 60.4% 1,215,465 58.7%

TOTAL 248,449 2,069,910 2,069,910 NET TO HIGH 35,097

RESHIWR 9,896 57.5% 49,958 34.6% 52,544 36.4%NORTH RESLOWR 7,310 42.5% 94,392 65.4% 91,806 63.6%

TOTAL 17,206 144,350 144,350 NET TO HIGH 2,586

RESHIWR 5,402 53.4% 25,177 24.8% 25,857 25.5%SCENT RESLOWR 4,722 46.6% 76,255 75.2% 75,575 74.5%

TOTAL 10,124 101,432 101,432 NET TO HIGH 680

RESHIWR 25,951 44.5% 139,534 26.1% 133,113 24.9%SOUTH RESLOWR 32,372 55.5% 395,011 73.9% 401,432 75.1%

TOTAL 58,323 534,545 534,545 NET TO HIGH (6,421)

RESHIWR 10,480 53.8% 45,833 32.4% 47,331 33.5%WEST RESLOWR 8,982 46.2% 95,494 67.6% 93,996 66.5%

TOTAL 19,462 141,327 141,327 NET TO HIGH 1,498

RESHIWR 308,995 53.4% 1,555,183 30.1% 1,594,601 30.9%TOTAL RESLOWR 269,577 46.6% 3,608,893 69.9% 3,569,475 69.1%

TOTAL 578,572 5,164,076 5,164,076 NET TO HIGH 39,418

40

ACCURACY OF AV 2005 PROFILE TYPE CHANGES

Algorithm AssignmentWeather

ZoneAnnual Validation

Assignment RESHIWR RESLOWD RESLOWR TotalTotal w/o

RESLOWD Accuracy

RESHIWR 64,308 5,095 22,079 91,482 86,387 74.4%COAST RESLOWR 47,584 3,668 40,158 91,410 87,742 45.8%

TOTAL 111,892 8,763 62,237 182,892 60.0%

RESHIWR 10,603 669 1,988 13,260 12,591 84.2%EAST RESLOWR 4,472 1,241 3,656 9,369 8,128 45.0%

TOTAL 15,075 1,910 5,644 22,629 68.8%

RESHIWR 7,979 311 2,461 10,751 10,440 76.4%FWEST RESLOWR 4,222 1,051 3,463 8,736 7,685 45.1%

TOTAL 12,201 1,362 5,924 19,487 63.1%

RESHIWR 106,441 6,314 29,018 141,773 135,459 78.6%NCENT RESLOWR 58,537 4,939 43,200 106,676 101,737 42.5%

TOTAL 164,978 11,253 72,218 248,449 63.1%

RESHIWR 6,652 330 2,914 9,896 9,566 69.5%NORTH RESLOWR 2,524 1,116 3,670 7,310 6,194 59.3%

TOTAL 9,176 1,446 6,584 17,206 65.5%

RESHIWR 4,261 287 854 5,402 5,115 83.3%SCENT RESLOWR 2,290 320 2,112 4,722 4,402 48.0%

TOTAL 6,551 607 2,966 10,124 67.0%

RESHIWR 22,223 1,572 2,156 25,951 24,379 91.2%SOUTH RESLOWR 19,412 3,125 9,835 32,372 29,247 33.6%

TOTAL 41,635 4,697 11,991 58,323 59.8%

RESHIWR 8,183 354 1,943 10,480 10,126 80.8%WEST RESLOWR 3,796 1,348 3,838 8,982 7,634 50.3%

TOTAL 11,979 1,702 5,781 19,462 67.7%

RESHIWR 230,650 14,932 63,413 308,995 294,063 78.4%TOTAL RESLOWR 142,837 16,808 109,932 269,577 252,769 43.5%

TOTAL 373,487 31,740 173,345 578,572 62.3%

41

RESIDENTIAL POPULATION ACCURACYBEFORE AV 2005 CHANGES


Zone Current Assignment RESHIWR RESLOWD RESLOWR TotalTotal w/o

RESLOWD Accuracy

RESHIWR 285,473 5,436 74,609 365,518 360,082 79.3%COAST RESLOWR 285,181 91,778 1,099,199 1,476,158 1,384,380 79.4%

TOTAL 570,654 97,214 1,173,808 1,841,676 79.4%

RESHIWR 57,844 2,054 7,656 67,553 65,499 88.3%EAST RESLOWR 29,633 12,618 78,818 121,069 108,451 72.7%

TOTAL 87,477 14,671 86,474 188,622 78.6%

RESHIWR 33,639 1,799 6,824 42,262 40,463 83.1%FWEST RESLOWR 20,166 9,051 70,735 99,952 90,901 77.8%

TOTAL 53,805 10,850 77,559 142,214 79.5%

RESHIWR 703,640 9,470 106,238 819,348 809,878 86.9%NCENT RESLOWR 237,865 75,387 937,310 1,250,562 1,175,175 79.8%

TOTAL 941,505 84,857 1,043,548 2,069,910 82.7%

RESHIWR 38,885 1,708 9,365 49,958 48,250 80.6%NORTH RESLOWR 13,104 8,777 72,511 94,392 85,615 84.7%

TOTAL 51,989 10,485 81,876 144,350 83.2%

RESHIWR 20,758 563 3,856 25,177 24,614 84.3%SCENT RESLOWR 14,627 5,926 55,702 76,255 70,329 79.2%

TOTAL 35,385 6,488 59,558 101,432 80.5%


TOTAL 307,440 39,448 187,656 534,545 59.1%

RESHIWR 36,233 1,892 7,708 45,833 43,941 82.5%WEST RESLOWR 22,660 8,931 63,903 95,494 86,563 73.8%

TOTAL 58,893 10,823 71,611 141,327 76.7%

RESHIWR 1,296,148 27,911 231,124 1,555,183 1,527,272 84.9%TOTAL RESLOWR 811,001 246,927 2,550,966 3,608,893 3,361,966 75.9%

TOTAL 2,107,149 274,837 2,782,090 5,164,076 78.7%

42

RESIDENTIAL POPULATION ACCURACYAFTER AV 2005 CHANGES


Zone Future Assignment RESHIWR RESLOWD RESLOWR TotalTotal w/o

RESLOWD Accuracy

RESHIWR 302,197 6,863 56,530 365,590 358,727 84.2%COAST RESLOWR 268,457 90,351 1,117,278 1,476,086 1,385,735 80.6%

TOTAL 570,654 97,214 1,173,808 1,841,676 81.4%

RESHIWR 63,975 1,482 5,988 71,444 69,962 91.4%EAST RESLOWR 23,502 13,190 80,486 117,178 103,988 77.4%

TOTAL 87,477 14,671 86,474 188,622 83.0%

RESHIWR 37,396 1,059 5,822 44,277 43,218 86.5%FWEST RESLOWR 16,409 9,791 71,737 97,937 88,146 81.4%

TOTAL 53,805 10,850 77,559 142,214 83.1%

RESHIWR 751,544 10,845 92,056 854,445 843,600 89.1%NCENT RESLOWR 189,961 74,012 951,492 1,215,465 1,141,453 83.4%

TOTAL 941,505 84,857 1,043,548 2,069,910 85.8%

RESHIWR 43,013 922 8,609 52,544 51,622 83.3%NORTH RESLOWR 8,976 9,563 73,267 91,806 82,243 89.1%

TOTAL 51,989 10,485 81,876 144,350 86.9%

RESHIWR 22,729 530 2,598 25,857 25,327 89.7%SCENT RESLOWR 12,656 5,959 56,960 75,575 69,616 81.8%

TOTAL 35,385 6,488 59,558 101,432 83.9%


TOTAL 307,440 39,448 187,656 534,545 61.2%

RESHIWR 40,620 898 5,813 47,331 46,433 87.5%WEST RESLOWR 18,273 9,925 65,798 93,996 84,071 78.3%

TOTAL 58,893 10,823 71,611 141,327 81.5%

RESHIWR 1,383,961 26,035 184,605 1,594,601 1,568,566 88.2%TOTAL RESLOWR 723,188 248,803 2,597,485 3,569,475 3,320,672 78.2%

TOTAL 2,107,149 274,837 2,782,090 5,164,076 81.4%

43

ConclusionsConclusions

62% of 2005 Annual Validation Profile Type changes are accurate

Changes to RESHIWR are significantly more accurate (78.4%) than are changes to RESLOWR (43.5%)

Accuracy of the changes by weather zone range from a low of 59.8% in the SOUTH zone to a high of 68.8% in the EAST zone

Although AV 2005 has identified 578,572 Profile Type changes, the net impact of the changes overall is to increase the number of RESHIWR ESI IDs only by 39,418 … a 2.5% increase and a corresponding 1.1% decrease for RESLOWR

The Residential population will have somewhat more accurate Profile Type assignments as a result of conducting 2005 Annual Validation (81.4% vs. 78.7%)

44

ConclusionsConclusions

As a result of completing AV 2005 the RESHIWR assignment accuracy will be 88.2% (vs. 84.9%) and the RESLOWR assignment accuracy will be 78.2% (vs. 75.9%)

A significant amount of room exists to improve the overall Profile Type assignment accuracy

A significant limitation to the current Profile Type assignment algorithm is that it considers usage during a single fall-winter-spring season

Profile Type changes are much more frequently made as a result of occupancy and/or weather related changes than as a result of heating system changes … and as a result are frequently “out of phase” with settlement

This analysis supports the PWG conclusions to continue making use of the Residential Survey data to investigate ways to tailor the assignment algorithm based on the weather zone and on the actual weather during the assignment window

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Questions?

Documents

ERCOT Analysis of 2005 Residential Annual Validation Using the Customer Survey Results