May 9, Case No. U New Customer Estimated Bill Procedures

Transcription

1 DTE Energy nd Ave., Detroit, MI Richard Middleton, Esq. Direct Dial: (313) May 9, 2004 Ms. Mary Jo. Kunkle Executive Secretary Michigan Public Service Commission 6545 Mercantile Way P.O. Box Lansing, Michigan Re: Case No. U New Customer Estimated Bill Procedures Dear Ms. Kunkle: Please find enclosed an original and 4 copies of the Joint Application of Michigan Consolidated Gas Company and The Detroit Edison Company for approval of bill estimation methods, along with a Proof of Service. Very truly yours, RPM/kbt Encl. Richard P. Middleton

2 STATE OF MICHIGAN BEFORE THE MICHIGAN PUBLIC SERVICE COMMISSION In the matter of the joint application of Michigan ) Consolidated Gas Company and the Detroit ) MPSC Case No. U Edison Company for Authority to Implement ) New Customer Estimated Bill Procedures ) Joint Application of Michigan Consolidated Gas Company and The Detroit Edison Company Michigan Consolidated Gas Company ( MichCon ) and the Detroit Edison Company ( Edison ) (collectively the DTE utilities ) apply to the Michigan Public Service Commission ( Commission ) for approval to implement new estimated bill procedures for commercial and residential customers. The DTE utilities file the procedures proposed in this Application and accompanying Attachments, pursuant to the Commission s Rules and Regulations for Residential Customers Rule R (1). In support of this application the DTE utilities state as follows: 1. MichCon is a subsidiary of DTE Energy, Inc., a corporation organized under the laws of the State of Michigan with its principal office at nd Avenue, Detroit, Michigan MichCon is a public utility subject to the jurisdiction of the Commission and is engaged in the purchase, transmission, distribution and sale of natural gas at retail to approximately 1.2 million customers within the State of Michigan. Edison is a wholly-owned subsidiary of DTE Energy Company, supplying retail electric service to customers located in Michigan. Edison is also a public utility subject to the jurisdiction of Commission. 2. Initially, the current CSB bill estimating process utilized for the estimation of DTE utilities electric and gas utility bills was approved by the Commission in its August 27, 1

3 1998 Order in Case No. U (See Attachment 3 which contains the description of the current bill estimation procedures approved by the Commission.) In May, 2001, MichCon became a subsidiary of DTE Energy, Inc. as a result of the merger of MCN Corporation and DTE Energy. A key ingredient supporting this merger was the opportunity for the merged enterprise to realize efficiencies through integrated system operations such as billing. Accordingly, MichCon s CSS billing procedures were migrated and integrated into Edison s existing billing system called CSB. Implementation of the current CSB estimated bill procedure was necessary to avoid costly CSB customization that would have been required to maintain MichCon s prior bill estimating procedures. In its Order in Case No. U-13188, the Commission approved the current CSB estimated bill procedure for MichCon. Today, Detroit Edison and MichCon both use the same CSB bill estimation to bill their utility customers when actual meter reads are not possible. 3. The DTE utilities are constantly striving to improve the efficiencies of their joint billing system. These continual process improvements have lead to the development of a more accurate method of estimating electric and gas utility bills when actual meter reads are not possible. The current bill estimation method relies on historic site usage to which appropriate weighting criteria are applied. While this has been an acceptable industry methodology, the current method does not take into consideration changes in weather conditions from one year to the next. Thus, the DTE utilities are proposing to implement a new bill estimation procedure that incorporates changes in weather and applies non-linear customer behavioral calculations within the algorithm. For these reasons, the DTE utilities believe the new bill estimation process produces a more accurate estimation of energy use for both electric and gas customers. (See Attachment 1 to this Application) 2

4 4. After developing what they believe is a more accurate bill estimation algorithm, the DTE utilities tested the reasonableness of using the proposed CSB bill estimation procedures for electric and natural gas customers by comparing the current CSB estimation error rate with the error rate produced by the new CSB bill estimation procedure. As shown in Attachment 2, the new CSB bill estimation procedure significantly improves the DTE utilities ability to estimate energy usage for both gas and electric in total, and more importantly provides significant improvement in the estimation of customers bills during those months where the energy usage is highest. 5. The improved accuracy of the new CSB estimation procedures and the cost effectiveness of maintaining equivalent electric and gas bill estimation processes demonstrates that the proposed CSB bill estimation methods provide a reasonable and appropriate replacement for MichCon s and Detroit Edison s current CSB method. At this time the CSB bill estimation procedures are scheduled to be operational upon approval of this Application by the Commission. 6. Approval of the CSB bill estimation procedures described in this Application will not increase the rates or charges for any customer. Approval of these new procedures will not increase or decrease the cost of natural gas used by MichCon s customers or electricity used by Detroit Edison s customers. Implementation of the new bill estimation procedure will not affect the current CSB handling of high/low meter reads. 7. Commission approval of these procedures will continue the cost effective integration of gas and electric billing methods in CSB that allows for the prompt and efficient delivery of customer bills. Therefore, it is in the public interest for the Commission to approve 3

5 the DTE utility s proposed bill estimation methods as described in this Application and Attachments. 8. Implementation of the new bill estimation procedures will not increase the rates or charges for any customers or increase the cost of natural gas or electricity used by either Detroit Edison or MichCon customers, and therefore the Commission may approve this Application without notice or hearing as provided by MCL 460.6a. 9. The Commission has the jurisdiction over DTE utilities businesses necessary to grant the relief requested in this Application pursuant to various provisions of 1909 PA 300, as amended, MCL et. seq., 1919 PA 419, as amended; MCL et. seq.; 1939 PA 3, as amended, MCL et. seq., 1969 PA 306, as amended; MCL et. seq. 4

6 WHEREFORE, Detroit Edison and MichCon respectfully request that the Commission issue an order approving the proposed bill estimation procedures as described in this Application without the time and expense of a hearing, and a) Find that the new CSB bill estimation procedures as described in the Application and Attachments are in the public interest and should be implemented by Detroit Edison and MichCon; and b) Grant such other relief as reasonably appropriate and necessary. Respectfully submitted jointly by THE DETROIT EDISON COMPANY and MICHIGAN CONSOLIDATED GAS COMPANY Dated: May 9, 2005 By Richard P. Middleton (P41278) nd Avenue, 688 WCB Detroit, Michigan (313)

7 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description Attachment 1 Case No. U Executive Summary DTE Energy proposes to modify its method of estimating energy use when an actual meter reading is not secured. The prior estimation method relied on historic site usage with an appropriate weighting criteria applied. While this is an acceptable industry methodology, this method does not take into consideration, extreme changes in weather conditions from one year to the next. The industry methodology also assumes a linear relationship between temperature and energy use i.e., as temperature rises in the summer, electricity use increases and as temperatures decrease in the winter, natural gas use increases. Finally, the methodology does not consider behavioral impacts of customers within a given product class. DTE Energy believes that taking weather into consideration as well as applying nonlinear behavioral calculations within the algorithm produces a more valid estimation of energy use. The weather-adjusted estimation algorithm will be used for the following product types that reflect the customer classification served by DTE Energy. Residential Electric Rate Code 060 (24) Commercial Electric Rate Code 110 (02) Interruptible Air Conditioning Rate Code 086 (01) Residential Gas Heating Rate Code 024 (207) Commercial Gas Heating Rate Code 015 (202) These product codes comprise 95% of the electric and gas services provided to customers at DTE Energy. The existing estimation algorithm, which utilizes a reference to historical consumption, will be used to estimate all remaining product types at DTE Energy. DTE Energy believes that using the weather-adjusted estimating algorithm will yield the following positive results due to the increased validity of estimated usage. An increase in customer satisfaction A reduction of inbound customer inquiries A reduction in special meter reads based upon customer requests A reduction in billing exceptions (events) processing This document provides an explanation of the methodology used in the weather-adjusted estimating algorithm both when 12-month historical usage is available and when the usage history is not available. Version 3.0 Page 1 of 13

8 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description A. Estimated Energy Use for Accounts With No Historical Data The overall process to derive the estimated usage for an account with no historical data is as follows. 1 Obtain Average Daily Use for Historical Period from the Rate Average Table 2 Calculate Weather Index for Historical Period 3 Calculate Weather Index for Current Month 4 Calculate Estimated Daily Use 5 Calculate Estimated Monthly Use 1 Average Daily Use (DU) is obtained from the Rate Average Table. At the end of each year a Rate Average Table is derived for each product. The table contains usage history by product type (residential electric, commercial electric, interruptible air conditioning, residential gas and commercial gas), by register type (kw, kwh, Time of Day and CCF) and by calendar month. The sum of all use by product, by register type, by month is divided by the number of associated meters for the product type to derive Average Daily Use (DU). The Rate Average Table is referenced when historical usage history is not available for an account at the time of billing. 2 The Weather Index for the 12 th Month (I 12 ) is derived by multiplying the Product Weight (product specific) by the percentage of hours at each of 11 temperature levels for the month one year prior to the billing month. Weather Indices are develop for each of the eight (8) Weather Stations in Michigan. Refer to an Appendix A for a list of Weather Stations and Appendix B for a sample calculation of the Weather Index I 12. The Product Weight (PW) is a derived variable that predicts behavioral response to a change in temperature for a given product type. Customers of a different product type do not typically respond to a temperature change in the same manner e.g., if the temperature in March was 89 degrees one day, a residential electric customer may not turn on their air conditioning, but a commercial electric customer may try to keep their employees and customers comfortable. Version 3.0 Page 2 of 13

9 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description 3 The Weather Index for the Current Month (I CU ) is derived by multiplying the Product Weight (product specific) by the percentage of hours at each of 11 temperature levels for the current billing month. Appendix B contains a list of the temperature levels and a sample calculation of Weather Index I CU. 4 The Estimated Daily Use (DU cu )is derived by multiplying the ratio of the historical and current weather indexes by the average daily use of the historical period. I CU /I 12 X DU 12 = DU cu 5 To derive the estimated monthly use for the billing period, the Estimated Daily Use (DU cu ) is multiplied by the number of days in the billing month (Days CU ). DU cu X Days CU = Weather-Adjusted Estimated Monthly Use Example March 2004, Residential Gas Product, Detroit Weather Station 0.402/0.425 = X 6.83 CCF = 6.46 X 31 = CCF Version 3.0 Page 3 of 13

10 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description B. Estimated Usage for Account With Historical Data The overall process to derive the estimated usage for an account with historical data is as follows. 1 Obtain Historical Daily Usage 2 Calculate Weather Index for Each Historical Period 3 Calculate Adjusted Daily Usage for Each Historical Period 4 Calculate Total Weight 5 Calculate Estimated Daily Use 6 Calculate Estimated Monthly Use 1 Actual Daily Usage is obtained for each of the following monthly periods: Previous 12 th month (DU 12 ) actual meter readings e.g., if the current billing period is for March 2004, then the previous 12 th period is March 2003 Previous 11 th month (DU 11 ) actual meter readings e.g., if the current billing period is for March 2004, then the previous 11 th period is April 2003 Previous month (DU PR ) actual meter readings e.g., if the current billing period is March 2004, then the previous month period is February 2004 If one of the stated months does not have actual usage, that month is not used in the calculation. Average Daily Use is derived from taking the total consumption for the month divided by the number of days in the billing period. 2 The Weather Index is calculated for each of the historical monthly periods (current month, pervious month, 11 th month and 12 th month) by multiplying the Product Weight (product specific) by the percentage of hours at each of 11 temperature levels. Appendix C contains a list of the temperature level and a Sample Weather Index. I CU = Current Month I PR = Previous Month I 11 = 11 th Month I 12 = 12 th Month Version 3.0 Page 4 of 13

11 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description 3 The historical Daily Use (DU) is multiplied by a ratio of the Weather Index (I) for each historical period to derive the weather-adjusted daily use (ADU). 4 I CU /I 12 X DU 12 = ADU 12 I CU /I 11 X DU 11 = ADU 11 I CU /I PR X DU PR = ADU PR To calculate a Total Weight (TW), the Absolute Difference (ABS Diff) between the Weather Index Current (I CU ) and the Weather Index for each historical billing period to the power of the unique product Weight Factor ( PWF ). The Weight Factor is a derived variable that predicts behavioral response to a change in temperature for a given product type and is a constant that is calculated to balance the contribution of historical blocks of usage. A sample of the Product Weight Factor is contained in the Appendix D. ABS Diff (I CU I 12 ) PWF + ABS Diff (I CU I 11 ) PWF + ABS Diff (I CU I PR ) PWF = TW 5 To calculate the Estimated Daily Use (EDU) for the billing month, the Estimated Daily Use for each historical period is multiplied by the Absolute Difference (ABS Diff) between the Weather Index for the current and historical period to the power of the Product Weight Factor ( PWF ). The product of this calculation is divided by the Total Weight (TW). Refer to the Appendix for a more detailed explanation of the Product Weight Factor. ADU 12 X ABS Diff (I CU I 12 ) PWF + ADU 11 X ABS Diff (I CU I 11 ) PWF + ADU PR X ABS Diff (I CU I PR ) PWF = EDU TW TW TW 6 To calculate the estimated monthly use, the Estimate Daily Use (EDU) from the previous calculation is multiplied by the number of days in the current billing month (Days CU ). EDU X Days CU = Estimated Monthly Usage Version 3.0 Page 5 of 13

12 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description Enclosures Appendix A Weather Stations and County Assignment to Weather Station Appendix B Weather Index Temperature Levels and Sample Weather Index with No Historical Data Appendix C Sample Weather Index with Historical Data Appendix D Product Weights and Product Weight Factor Revision History Document Version Author Subject Matter Experts Document Date Approval Date Description of Document/Revision 1.0 T. McCarter 2.0 W. Cloutier 3.0 W. Cloutier W. Cloutier T. Chase B. Maruthavanan W. Cloutier B. Maruthavanan W. Cloutier B. Maruthavanan 3/22/05 Draft Document 5/2/05 5/2/05 5/4/05 5/4/05 Modified description of calculation for accounts with no historical data to reflect 12 th month period reference only. Page #1 -Corrected product reference number for rate codes 110 & 086 to reflect CSB system. Page #9 Changed temperature levels from 0-10 to 1-11 to match example tables. Version 3.0 Page 6 of 13

13 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description Appendix A State of Michigan Weather Station Alpena Detroit Metro Airport Grand Rapids Houghton Lake Iron Mountain Saginaw Sault Saint Marie Traverse City Michigan County ALCONA ALGER ALLEGAN ALPENA ANTRIM BAY BENZIE CHARLEVOIX CHEBOYGAN CHIPPEWA CLARE CLINTON CRAWFORD DELTA DICKINSON DUMMY EMMET GENESEE GLADWIN GRAND TRAVERSE GRATIOT HURON INGHAM IONIA IOSCO IRON ISABELLA KALKASKA KENT LAKE LAPEER LEELANAU LENAWEE Weather Station Code APN DTW GRR HTL IMT MBS SSM Weather Station APN IMT GRR APN MBS APN SSM GRR HTL IMT IMT DTW MBS HTL MBS MBS GRR GRR MBS IMT GRR GRR MBS DTW Version 3.0 Page 7 of 13

14 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description Appendix A LIVINGSTON MACOMB MANISTEE MARQUETTE MASON MECOSTA MENOMINEE MISSAUKEE MONROE MONTCALM MONTMORENCY MUSKEGON NEWAYGO OAKLAND OCEANA OGEMAW OSCEOLA OSCODA OTSEGO OTTAWA PRESQUE isle ROSCOMMON SAGINAW SAINT CLAIR SANILAC SHIAWASSEE TUSCOLA WASHTENAW WAYNE WEXFORD DTW DTW IMT GRR IMT DTW GRR APN GRR GRR DTW GRR HTL HTL HTL GRR APN HTL MBS DTW MBS MBS MBS DTW DTW Version 3.0 Page 8 of 13

15 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description Appendix B Weather Index Temperature Levels The Weather Index is derived by multiplying the Product Weight by the percentage of hours at each temperature. There are 11 temperature levels (1 to 11) in the Weather Index formula as follows: (1) Less than zero degrees (2) Zero to 10 degrees (3) 11 to 20 degrees (4) 21 to 30 degrees (5) 31 to 40 degrees (6) 41 to 50 degrees (7) 51 to 60 degrees (8) 61 to 70 degrees (9) 71 to 80 degrees (10) 81 to 90 degrees (11) Greater than 90 degrees Each hour of a day has the temperature adjusted by either the Heat Index (summer) or Wind Chill Factor (winter) by weather station. Sample Weather Index No Historical Data Residential Gas Product Detroit Weather Station I 12 March 2003 Temperature Level (adjusted) Number of Hours (billing month) Percentage of Hours Product Weight 2 (0-10 F) 24 3% (11-20 F) % (21-30 F) 96 13% (31-40 F) % (41-50 F) % (51-60 F) % % I CU March 2004 Temperature Level (adjusted) Weather Index Number of Hours (billing month) Percentage of Hours Product Weight 4 (21-30 F) % (31-40 F) % (41-50 F) % (51-60 F) 96 13% % Weather Index Version 3.0 Page 9 of 13

16 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description Appendix C Sample Weather Index With Historical Data Residential Gas Heating Detroit - March 2004 I 12 March 2003 Temperature Level Number of Hours Percentage of Product Weight Weather Index (adjusted) (billing month) Hours 2 (0-10 F) 24 3% (11-20 F) % (21-30 F) 96 13% (31-40 F) % (41-50 F) % (51-60 F) % % I 11 April 2003 Temperature Level Number of Hours Percentage of Product Weight Weather Index (adjusted) (billing month) Hours 4 (21-30 F) 48 7% (31-40 F) % (41-50 F) % (51-60 F) % (61-70 F) 96 13% % I PR February 2004 Temperature Level Number of Hours Percentage of Product Weight Weather Index (adjusted) (billing month) Hours 3 (11-20 F) 48 7% (21-30 F) % (31-40 F) % (41-50 F) 24 3% % I CU March 2004 Temperature Level Number of Hours Percentage of Product Weight Weather Index (adjusted) (billing month) Hours 4 (21-30 F) % (31-40 F) % (41-50 F) % (51-60 F) 96 13% % Version 3.0 Page 10 of 13

17 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description Appendix D Product Weights and Product Weight Factor Measuring Accuracy of an Estimate A model is created for each combination of product/weather station location. Thousands of customers exist for each model. Their relative usage for a given period can vary considerably. Measuring percentage of error rather than absolute error keeps from biasing a model toward higher usage customers. This is important, as a customer s satisfaction is relative to their personal billing history. A customer with a lower usage rate is going to be more sensitive in absolute terms than a higher usage customer. That is why a percentage error has been employed when developing the models. There is a lower absolute level where most customers become insensitive e.g., a $5.00 bill estimate when actually the bill is $10.00 (100% error) is probably not that significant to most customers and should not be treated the same as a $50.00 bill that is actually $ (also 100% error). For this reason, accuracy measurement as a percentage has been modified on the low end. We have defined low usage threshold as 10% of the average usage of the historical meter reads for a given product/location model (the 5th percentile). If the absolute error is below this amount, it is considered to be 0% error. If the absolute error is above 10% of average usage, then the percentage error calculated. At the high end, extreme percentage error estimates (>100%) are capped at 100% so that the optimization algorithm does not attempt to bias the weather-based model to historical usage that is not weather dependent (e.g., behavior change, bad read, bad meter, etc.). The modified approach to a straight percentage error estimate provides a means to optimize a model by standards that are more reflective of what a customer would consider a good estimate. It also makes the problem non-linear. Non-Linear Optimization Finding optimal solutions to non-linear problems can be much more difficult than their linear counterparts. The approach that has been used to generate the 12 parameters (11 Product Weights and the Weight Factor) for each product/location model is based on Particle Swarm Theory. Particle Swarm Theory is a relatively new non-linear optimization technique. Particle Swarm Theory has its origins in fields of social psychology and evolutionary computation. Particle Swarm optimization algorithms are patterned after models on how ideas are evolved in society. An excellent reference book for Particle Swarm Optimization (PSO) is Swarm Intelligence, James Kennedy and Russell C. Eberhart, Academic Press, The book contains the basic algorithms that were used to generate the parameters for the meter estimation models. The algorithms have proven to be fast in solving and robust in finding optimal solutions (an issue with non-linear problems). Version 3.0 Page 11 of 13

18 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description Appendix D The weighting factors are described as follows. 1. Product Weight (PW) a series of 11 constants that are calculated to derive the Weather Index which are unique to each product. 2. Product Weight Factor (PWF) a constant that is calculated to balance the contribution of historical blocks of usage which is unique to each product (PWF is not used when estimating use for accounts without historical data). From 1 to 3 estimates are generated based on historical meter reads (previous month, 11 th month and 12 th month from the current billing month). The question becomes how to combine multiple estimates (when they exist) into a single estimate for the given customer s billing period. A simple approach would be to take a straight average of all of the available estimates. A more complex approach, but more accurate approach, is to weight the estimates based on how close their historical Weather Indexes are to the current billing period Weather Index. A potential heuristic is that the closer a historical billing period Weather Index is to the current billing period Weather Index (relative to the other historical billing period Weather Indexes), the more accurate it is and the more it should be weighted in the final single estimate. This implies that the weighting is inversely proportional to the absolute difference of the Weather Indexes. The Product Weight Factor parameter provides this capability. A model for a given product/location combination has a Product Weight Factor that can vary from 0 to 3. All historical estimates are multiplied by their absolute difference of their Weather Index with the current billing period weather index raised to the Product Weight Factor power. A Product Weight Factor value of 0 implies a simple straight average. The more negative the Product Weight Factor, the more the final estimate is biased toward the estimate(s) that have the smallest Absolute Difference with the current billing period weather index. The Product Weight and Product Weight Factor are unique to each Weather Station and product combination e.g., a residential customer in Detroit that has gas supplied by DTE Energy would have one unique set of Product Weights and a Product Weight Factor, which would differ from a residential customer in Grand Rapids that has gas supplied by DTE Energy. Note: the Product Weight and Product Weight Factor do not vary from month to month. Version 3.0 Page 12 of 13

19 Weather Adjusted Algorithm for Estimating Monthly Energy Use Methodology Description Appendix D Example: Product Weights Residential Gas Heating - Detroit Cnstnt W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 Temp o F <0 0to10 10to20 20to30 30to40 40to50 50to60 60to70 70to80 80to90 >90 PW Example: Product Weight Factor Residential Gas Heating - Detroit PWF Version 3.0 Page 13 of 13

20 DTE Energy Utilities Comparison of Current and Proposed Estimated Billing Algorithm Case No. U Attachment 2 Residential Gas Heating (Rate Code 024) Current Estimation Algorithm New Estimation Algorithm 2004 Sample Month Avg Daily Usage Avg % Mean Absolute % Avg Daily Usage Avg % Mean Absolute % % Improvement Mean Absolute Improvement January % 9.33% % 7.26% 9.8% 2.1% February % 14.72% % 7.96% 11.1% 6.76% March % 23.18% % 10.03% 11.6% 13.15% April % 11.51% % 10.89% -1.0% 0.61% May % 40.32% % 12.18% 20.3% 28.14% June % 55.60% % 9.91% 26.8% 45.68% July % 18.78% % 5.65% 20.4% 13.13% August % 13.85% % 4.73% 16.0% 9.12% September % 43.63% % 4.10% 26.0% 39.54% October % 51.77% % 16.78% 16.3% 34.99% November % 16.94% % 12.12% 0.2% 4.82% December % 12.47% % 8.50% 2.4% 3.97% The average monthly sample size for the Residential Gas Heating test was 2245 meters. The sample size for the January Current Algorithm test was reduced due to availability of data with matched meter read and bill cycle dates. Commercial Gas Heating (Rate Code 015) Current Estimation Algorithm New Estimation Algorithm 2004 Sample Month Avg Daily Usage Avg % Mean Absolute % Avg Daily Usage Avg % Mean Absolute % % Improvement Mean Absolute Improvement January % 11.71% % 14.87% 16.3% -3.2% February % 16.54% % 12.15% 12.3% 4.39% March % 27.75% % 15.71% 4.9% 12.05% April % 14.10% % 15.17% -6.1% -1.07% May % 35.70% % 16.22% 18.9% 19.47% June % 33.20% % 10.90% 20.2% 22.30% July % 24.65% % 5.26% 24.1% 19.39% August % 27.13% % 3.13% 25.9% 24.00% September % 31.42% % 4.02% 30.5% 27.40% October % 41.49% % 14.23% 24.2% 27.26% November % 29.09% % 18.28% -0.6% 10.80% December % 18.08% % 15.88% 1.2% 2.19% The average monthly sample size for the Commercial Gas Heating test was 800 meters. The sample size for the January Current Algorithm test was reduced due to availability of data with matched meter read and bill cycle dates. Page 1 of 3

21 DTE Energy Utilities Comparison of Current and Proposed Estimated Billing Algorithm Case No. U Attachment 2 Residential Electric (Rate Code 060) Current Estimation Algorithm New Estimation Algorithm 2004 Sample Month Avg Daily Usage Avg % Mean Absolute % Avg Daily Usage Avg % Mean Absolute % % Improvement Mean Absolute Improvement January % 13.59% % 10.08% 10.0% 3.5% February % 10.99% % 12.45% -2.5% -1.46% March % 10.53% % 11.79% 4.2% -1.26% April % 9.33% % 11.55% -4.9% -2.22% May % 11.45% % 13.24% 1.2% -1.79% June % 11.91% % 13.89% 0.2% -1.99% July % 13.17% % 15.25% 6.4% -2.08% August % 10.83% % 13.47% -1.2% -2.64% September % 11.74% % 14.16% 1.9% -2.42% October % 17.83% % 12.89% 8.9% 4.93% November % 10.67% % 11.41% -2.2% -0.74% December % 10.78% % 11.14% 3.7% -0.36% The average monthly sample size for the Residential Electric test was 2331 meters. The sample size for the January Current Algorithm test was reduced due to availability of data with matched meter read and bill cycle dates. Commercial Electric (Rate Code 110) Current Estimation Algorithm New Estimation Algorithm 2004 Sample Month Avg Daily Usage Avg % Mean Absolute % Avg Daily Usage Avg % Mean Absolute % % Improvement Mean Absolute Improvement January % 5.87% % 5.85% 1.2% 0.0% February % 6.21% % 6.94% 1.1% -0.73% March % 6.23% % 9.46% -1.1% -3.23% April % 4.50% % 9.41% 2.8% -4.91% May % 7.95% % 8.48% 1.1% -0.53% June % 7.34% % 7.43% 2.3% -0.09% July % 7.40% % 10.91% 5.4% -3.52% August % 5.40% % 7.62% -0.6% -2.22% September % 5.39% % 9.06% -1.8% -3.67% October % 9.20% % 9.75% 4.6% -0.55% November % 8.14% % 8.02% 4.9% 0.12% December % 5.99% % 9.22% 0.7% -3.23% The average monthly sample size for the Commercial Electric test was 2300 meters. The sample size for the January Current Algorithm test was reduced due to availability of data with matched meter read and bill cycle dates. Page 2 of 3

22 DTE Energy Utilities Comparison of Current and Proposed Estimated Billing Algorithm Case No. U Attachment 2 Interruptible Air Conditioning (Rate Code 086) 2004 Sample Month Current Estimation Algorithm Avg Daily Usage Avg % Mean Absolute % New Estimation Algorithm Avg Daily Usage Avg % Mean Absolute % % Improvement Mean Absolute Improvement June % 39.57% % 50.72% -32.7% -11.2% July % 37.23% % 29.56% 6.9% 7.67% August % 34.36% % 24.07% 1.4% 10.30% September % 28.01% % 25.18% 8.3% 2.83% October % 53.76% % 31.15% -9.0% 22.61% The average monthly sample size for the Interruptible Air Conditioning test was 2282 meters. The sample size for the January Current Algorithm test was reduced due to availability of data with matched meter read and bill cycle dates. Page 3 of 3

23 DESCRIPTION OF CURENT BILL ESTIMATION PROCESS Attachment 3 Case No. U The current CSB estimating procedure looks at four components of usage when calculating an estimated bill. The four components are last months bill, year ago data, 365 day average, and monthly data from 11 months prior to the current billing month. These factors are weighed individually by residential and commercial rate class as indicated in the chart below: One Year Ago Previous Month 11 Months Ago 365 Day Average Residential 53% 21% 21% 5% Commercial 21% 53% 21% 5% The four estimating components in the current CSB estimating procedure are used in sequence to arrive at an estimate of usage when the Company is unable to obtain a monthly meter read. When an adequate record of actual usage exists for a specific customer the estimate is based on a weighted average of the four components as described above. Where the actual usage record is incomplete and no data, or insufficient date, is available to calculate all of the components of the estimate, then the estimate is based on reweighing the remaining components on a pro rata basis.

24 M.P.S.C. No. 9 - Electric Seventh Revised Sheet No. B4-5 The Detroit Edison Company Cancels Sixth Revised Sheet No. B4-5 To Update Estimated Bill Process (Continued From Sheet No. B4-4) B-4.2 (cont'd.) RULES AND REGULATIONS--Continued (2) ESTIMATED BILLING: Meters will be read and bills rendered on a monthly basis. In cases where the Company is unable to read the meter, the consumption for the billing period may be estimated using the following procedure. The consumption for an estimated bill will be calculated using the following methodology, approved by the Michigan Public Service Commission in Case No. U on <month> <date>, 2005: (1) For accounts having actual historical usage information: Consumption will be estimated based on historical average daily usage for the account. The average daily usage is calculated using selected historical data from the current and prior year, adjusted for the impact of the difference in weather between the period being estimated and the weather in the historical periods used to calculate average daily usage. (2) For accounts without sufficient actual historical usage information: Consumption will be estimated based on the average daily usage for all accounts within that rate or service classification for the corresponding billing period of the prior year. The average daily usage is calculated using data from the prior year s actual consumption, adjusted for the impact of the difference in weather between the period being estimated and the weather in the historical periods used to calculate average daily usage. This estimating policy is applicable to residential and commercial customers. A bill rendered on an estimated use is of equal validity with a bill rendered on an actual meter reading. However, where a bill is rendered on an estimated basis and not paid, service will not be disconnected for nonpayment until a meter reading has been made and the estimated bill verified. Where access to the meter has been denied, service will be cut at the pole as provided in Rule B5.4, Access to Premises. Any bill rendered on an estimated basis shall be clearly and conspicuously identified. The Company will not render an estimated bill unless the estimating procedures employed by the Company and any substantive changes in those procedures have been approved by the Commission. Notwithstanding the provisions of the above rule, the Company may estimate the bill of any residential customer if extreme weather conditions, emergencies, work stoppages or other circumstances of force majeure prevent actual meter readings. If the Company is unable to gain access to read a meter, then the Company will undertake reasonable alternative measures to obtain an actual reading including mailing or leaving postpaid pre-addressed postcards upon which the customer may note the reading. If the customer fails to comply with such alternative measures, or makes reading of the meter unnecessarily difficult, then the Company may transmit an estimated bill. If an actual reading cannot be obtained, then the Company will maintain accurate records of the reasons therefore, and efforts it has made to secure the actual reading. (Continued on Sheet No. B4-6)

25 M.P.S.C. No. 9 - Electric Sixth Revised Sheet No. B4-6 The Detroit Edison Company Cancels Fifth Revised Sheet No. B4-6 The first residential bill based on an actual ending meter reading following one or more bills based on estimated ending meter readings(s) shall incorporate an adjustment so that the total amount billed for the period between the two most recent actual meter readings, including the Power Supply Cost Recovery Factor and other applicable charges, is based on the average daily use within that period, except that the period shall begin no earlier than the billing month of June Issued: March 31, 2000 Effective for service rendered on By: L. G. Garberding and after March 17, 2000 under Executive Vice President authority of order of the Michigan and Chief Financial Officer Public Service Commission dated 2000 Second Avenue February 9, 2000 in Case No. U Detroit, Michigan UPON APPROVAL CONTENT WILL BE SHIFTED.

26 M.P.S.C. No. 4 - Gas Michigan Consolidated Gas Company To Update Estimated Bill Process and Tariff Language First Revised Sheet No. B Cancels Original Sheet No. B B6.2 Billing Period and Estimated Bills: (Continued From Sheet No. B-36.00) (A) Billing Frequency Bills for gas service shall be rendered on approximately a monthly basis, and shall be due and payable on or before the due date shown on each bill. (B) Meter Reads The Company shall schedule meters to be read on approximately a monthly basis and will attempt to read meters in accordance with such schedule. When the Company is unable to obtain an actual meter reading for any reason, the bill shall be estimated. Prior period(s) estimated bill(s) shall be adjusted as necessary when an actual meter reading is obtained. (C) Estimated Bills The consumption for an estimated bill will be calculated using the following methodology approved by the Michigan Public Service Commission in Case No. U on <month> <date>, 2005: (1) For accounts having actual historical usage information: Consumption will be estimated based on historical average daily usage for the account. The average daily usage is calculated using selected historical data from the current and prior year, adjusted for the impact of the difference in weather between the period being estimated and the weather in the historical periods used to calculate average daily usage. (2) For accounts without sufficient actual historical usage information: Consumption will be estimated based on the average daily usage for all accounts within that rate or service classification for the corresponding billing period of the prior year. The average daily usage is calculated using data from the prior year s actual consumption, adjusted for the impact of the difference in weather between the period being estimated and the weather in the historical periods used to calculate average daily usage. Estimated bills shall have the same force and effect as those based upon actual meter readings. (D) Customer Meter Reads Any customer may read his/her own meter and provide the readings to the Company using Company authorized and provided forms. ISSUED <DATE> BY <NAME> <TITLE> DETROIT, MICHIGAN EFFECTIVE FOR GAS SERVICE RENDERED ON AND AFTER <DATE> ISSUED UNDER AUTHORITY OF THE MICHIGAN PUBLIC SERVICE COMM. DATED <DATE> IN CASE NO. U-14523

27 M.P.S.C. No. 4 - Gas Michigan Consolidated Gas Company To Update Estimated Bill Process and Tariff Language First Revised Sheet No. B Cancels Original Sheet No. B B6.3 NSF Charges: Checks remitted by customers as bill payments and returned because they are not valid are rebilled to the customer s account. A $5.00 charge will be assessed to customers for handling checks received and returned by banks for reasons of insufficient funds, bank account closed, no account and similar situations. B6.4 Where Bills Are Payable: Bills are payable at an office of the Company in the District in which gas service is supplied and at various specified agencies. B6.5 Prompt Payment Discount: None. UPON APPROVAL CONTENT WILL BE SHIFTED. (Continued on Sheet No. B-38.00) ISSUED <DATE> BY <NAME> <TITLE> DETROIT, MICHIGAN EFFECTIVE FOR GAS SERVICE RENDERED ON AND AFTER <DATE> ISSUED UNDER AUTHORITY OF THE MICHIGAN PUBLIC SERVICE COMM. DATED <DATE> IN CASE NO. U-14523

28 STATE OF MICHIGAN BEFORE THE MICHIGAN PUBLIC SERVICE COMMISSION In the matter of the joint application of Michigan ) Consolidated Gas Company and the Detroit ) MPSC Case No. U Edison Company for Authority to Implement ) New Customer Estimated Bill Procedures ) STATE OF MICHIGAN ) )ss. COUNTY OF WAYNE ) PROOF OF SERVICE Jennifer Evans, being duly sworn, deposes and says that on the 9 th day of May, 2005, she served a copy of the Joint Application of Michigan Consolidated Gas Company and the Detroit Edison Company upon the parties listed on the attached service list by depositing same in the United States mail being postage fully prepaid. Subscribed and sworn to before me On 9 th day of May, JENNIFER EVANS Notary Public 1

29 SERVICE LIST Updated as of May 9, 2005 David Voges Assistant Attorney General Public Service Division 6545 Mercantile Way, Suite 15 Lansing, MI Julie Baldwin Public Service Division 6545 Mercantile Way, Suite 15 Lansing, MI