Multivariate Regression Model Results

Updated: August, 0 Page of Multivariate Regression Model Results 4 5 6 7 8 This exhibit provides the results of the load model forecast discussed in Schedule. Included is the forecast of short term system energy consumption, short term system peak demand, and customer numbers. This exhibit also addresses the impacts of conservation and demand side management ("CDM"), weather normalization methodology, and an assessment of the model performance. Please note that distribution revenues and use-percustomer is discussed in Tab Schedule. 9 0 4 5 6 7 8 9 Short Term System Energy Forecast The energy consumption forecast model introduced and discussed at Schedule performs very well with an adjusted R of 0.987, indicating that 98.7% of the variations in energy consumption from 996 to 0 are explained by the variables in the model. Furthermore, the model statistics indicate a Mean Absolute Percentage Error ( MAPE ) of 0.86% with a monthly mean absolute deviation ( MAD ) of 5,4 MWh. Additional model statistics including coefficient values, standard error, T-Stat and P-Values are included in Appendix A. As illustrated in Table below, system energy consumption under normal weather is expected to increase by 0.06% in 0 relative to weather-corrected 0 consumption. Energy consumption is projected to increase by 0.88% in 0. 0

Updated: August, 0 Page of Table : Actual/Forecast and Weather-Corrected Energy, 006 to 0 Year Actual/Forecast Weather- Weather- Energy Actual Growth Corrected Energy Corrected (%) Growth (%) 006 8,08,676 -.9 8,05,586 0.4 007 8,49,69.6 8,05,075 0. 008 8,096,55 -.86 7,995,947-0.70 009 7,74,44-4.7 7,788,68 -.59 00 7,949,46.67 7,79,098-0.64 0 7,880,490-0.86 7,744,998 0.08 0* 7,749,7 -.66 7,749,7 0.06 0* 7,87,74 0.88 7,87,74 0.88 4 5 6 7 8 * Incremental CDM activities not included Note that the forecast in Table does not include the anticipated impacts of incremental CDM activities, which is discussed later in this exhibit. Figure below illustrates the annual energy consumption, weather-corrected energy consumption, annual growth rates and weather-corrected annual growth rates on an actual basis from 997 to 0, and as forecast from 0 to 0.

Updated: August, 0 Page of Figure : Actual/Forecast and Weather-Corrected Energy, 997 to 0 4 5 6 7 8 9 0 Short Term System Peak Demand Forecast The system peak demand forecast was derived using an extreme weather scenario. (The extreme weather scenario is described more fully below). The model fit was found to be statistically significant with an adjusted R of 0.95 indicating that the model captures 95% of the peak demand variation from 996 to 0. Furthermore, the model statistics indicate a MAPE of.46% with a MAD of 4.6 MW. Figure and Table below provide the actual and forecast system peak demand from 996 to 0, assuming the extreme weather scenario. The forecast indicates a.8% peak demand growth in 0 relative to the actual 0 system peak, and 0.45% peak demand growth in 0, as shown in Table.

Updated: August, 0 Page 4 of Figure : Actual and Forecast System Peak Demand (Extreme Weather), 996 to 0

Updated: August, 0 Page 5 of Table : Actual and Forecast System Peak Demand (Extreme Weather), 997 to 0 Year System Peak Demand (MW) Annual Growth (%) 997,56 8.4 998,8. 999,44 4.76 000,05 -.87 00,477.6 00,509.0 00,505-0.6 004,47-5. 005,570 0.05 006,60.54 007,560 -.0 008,5 -.05 009,506-0.47 00,550.9 0,609.86 0*,66.8 0*,670 0.45 4 5 6 7 8 9 0 * Table excludes incremental CDM savings in 0 to 0 Adjustments for Conservation and Demand Side Management Enersource delivered CDM programs funded through third tranche revenue and is currently delivering CDM programs that are funded through the Ontario Power Authority ( OPA ). On November, 00, the Board issued a Decision and Order which specified the CDM targets which Enersource must meet as a condition of its licence. These targets are 9.98 MW for the 04 Net Annual Peak Demand Savings and 47. GWh for the 0-04 Net Cumulative Energy Savings. Enersource continues to deliver OPA programs to meet these new targets. Board Dockets EB-00-05 and EB-00-06 Decision and Order dated November, 00, Appendix A, line 8.

Updated: August, 0 Page 6 of 4 5 6 7 8 9 0 The impact of historical CDM programs on the load in future years is incorporated in the load forecast presented in Table above as a CDM trending variable is utilized in the load forecast model. The load forecast model however does not incorporate projections of incremental energy savings from the aggressive CDM targets that Enersource will need to deliver in 0 to 0. Hence, Enersource has adjusted the forecast shown in Table with the cumulative increases in CDM over and above those included in the load forecast model over the 0 to 0 period. The incremental CDM energy consumption savings are identified in Table below. Table : CDM Adjustments by Customer Class, 0 to 0 (kwh) Rate Class 0 CDM Adjustment 0 CDM Adjustment Residential (,709,000) (5,84,90) Small Commercial - - Unmetered Scattered Load - - GS < 50 (,60,6) (9,59,9) GS 50-499 (4,49,85) (6,78,6) GS 500-4999 (4,648,05) (7,66,687) Large User (4,74,85) (8,98,655) Street Lighting (5,8,799) (0,95,95) Total (84,7,) (9,46,6) 4 5 6 7 8 Table highlights the adjustment made to the sales forecasts by customer class to reflect the load reductions in 0 and 0 as a result of the incremental CDM activities. A detailed monthly breakdown of the CDM adjustment shown on Table is provided as Attachment to this exhibit. The net result of the CDM adjustments yields an overall consumption forecast as shown in Table 4 below. The forecast data on Table 4 is also shown at Attachment, which provides the actual and forecast sales by rate class, net of CDM impacts, from 008 to 0.

Updated: August, 0 Page 7 of Table 4: Energy Forecast Including CDM Impacts, 0 to 0 (kwh) 0 Energy Forecast (per Table ) CDM Adjustment (per Table ) Energy Forecast Residential,498,8,07 (,709,000),475,59,07 Small Commercial 908,655-908,655 Unmetered Scattered Load 0,66,80-0,66,80 GS < 50 667,05,70 (,60,6) 64,4,07 GS 50-499,04,055,980 (4,49,85),99,706,7 GS 500-4999,6,967,744 (4,648,05),,9,69 Large User,0,67,005 (4,74,85) 996,9,90 Street Lighting 40,8,989 (5,8,799) 4,990,90 Total 7,749,7,964 (84,7,) 7,665,46,8 0 Residential,50,959,64 (5,84,90),475,6,44 Small Commercial 96,49-96,49 Unmetered Scattered Load 0,756,86-0,756,86 GS < 50 67,89,87 (9,59,9) 6,0,54 GS 50-499,,40,707 (6,78,6),6,685,094 GS 500-4999,7,688,588 (7,66,687),0,5,90 Large User,00,566,40 (8,98,655),0,58,747 Street Lighting 40,69,65 (0,95,95) 9,704,4 Total 7,87,740,567 (9,46,6) 7,698,594,05 4 5 6 7 8 9 0 Weather Normalization Methodology Since forecasting weather with confidence is not reasonable, Enersource s load forecasting process utilizes two weather scenarios which are generated based on actual historical weather data for Mississauga. The two scenarios that are used are normal weather used for energy consumption forecasting, and extreme weather for peak system demand forecasting. Normal weather scenario is used for energy consumption since it provides the most typical weather conditions relative to historical experience. The extreme weather scenario is utilized for peak system demand forecasting to project the peak load demand which occurs

Updated: August, 0 Page 8 of 4 5 6 7 8 9 0 4 5 6 7 8 9 0 when the system is under duress. It would not be appropriate to use the extreme weather scenario for energy consumption forecasting as the likelihood of observing sustained extreme weather is highly unlikely. However, in assessing the system s capability to meet a one-hour summer peak, a monthly extreme peak demand forecast would be more appropriate. Enersource utilizes years of historical weather data to generate the normal and extreme weather scenarios. This is consistent with the weather normalization process used by the Independent Electricity System Operator ("IESO") to derive its 8-Month Outlook An Assessment of the Reliability and Operability of the Ontario Electricity System. The practice of weather normalization using 0 years of historical data is also consistent with weather normalization practices at Environment Canada and the World Meteorological Organization. The World Meteorological Organization Climatological Practices (WMO-No.00) indicates that climatological standards for normal weather are computed based on data from consecutive periods of 0 years. 4 The use of years by Enersource, rather than 0, is required as the normal and extreme weather scenarios are based on median data for actual days and not on averages. Selecting a median not based on averages requires an odd number. This is explained more fully below. Source: Methodology to Perform Long Term Assessments, Nov 0, IESO_REP_066v7.0 Outlook Methodology, Page 5. http://www.climate.weatheroffice.gc.ca/climate_normals/index_e.html, http://www.climate.weatheroffice.gc.ca/prods_servs/normals_documentation_e.html 4 www.wmo.int/pages/prog/wcp/ccl/guide/guide_climat_practices.html

Updated: August, 0 Page 9 of 4 5 6 7 8 9 0 4 5 6 7 8 9 0 Enersource also observes that the Board has accepted a -year weather scenario in its adoption of the Ontario Wholesale Electricity Market Price Forecast 5 completed by Navigant Consulting, Ltd. ("Navigant") to underpin the Board's Regulated Price Plan Price Report every six months. Enersource recognizes that Navigant's price forecast is based on the IESO s 8 Month Outlook, which is based on weather normalization using years of actual weather similar to the methodology used by Enersource. The use of 0 years of historical data is also supported by Itron Energy Forecasting Group ("Itron") 6 who are considered industry experts in electricity load forecasting. Enersource submits that the determination of weather normalization using 0 years of data is a common, accepted protocol, as is evidenced by the practices of Environment Canada, the World Meteorological Organization, Navigant, the IESO, Itron and the Board. Thus, Enersource continues to base its load forecast on the same time period. In recent years, some Board proceedings have introduced the use of a shorter period, ten years, for weather normalization purposes. For comparison purposes, Enersource has calculated its energy consumption forcast using this method. The 0 and 0 system energy consumption forecasts using both -year and -year weather are provided in Table 5 below. Table 5 illustrates that the impact of using a shorter period of weather normalization produces a slight (0.59%) increase in the forecasted system energy consumption. 5 http://www.ontarioenergyboard.ca/oeb/_documents/eb-004-005/rpp_wholesaleelectricitypriceforecastrprt_007.pdf 6 Dr. J. Stuart McMenamin, Itron Energy Forecasting White Paper, Defining Normal Weather for Energy and Peak Normalization, 008 Itron Inc.

Updated: August, 0 Page 0 of Table 5: Comparison of -Year to -Year Normal Weather on Energy Forecast Energy Forecast 0 0 Years 7,749,7 7,87,74 Years 7,796,5 7,864,5 % Difference 0.59% 0.59% 4 5 6 7 8 9 0 4 5 6 7 8 9 Enersource utilizes hourly weather information from the Lester B. Pearson International Airport ("Pearson Airport") weather station located in Mississauga. The data used for review, analysis and scenario development was obtained from the National Climate Data Archives at Environment Canada. 7 Enersource's study of normal and extreme weather data used the hourly data from Pearson Airport from 980 to 0. Normal Weather Scenario Process The process used by Enersource to derive its normal weather scenario involves ranking and selecting the median temperature for each day of the year. The process starts with converting historical hourly daily temperatures into daily mean (average) temperatures. Daily average temperatures are then indexed based on similar days in their respective week of the year. For each day of the week, the data was ranked from the highest (maximum) daily average temperature to lowest (minimum) daily average temperature. Based on a list of daily average temperatures for the same day of the same week for each year, the median daily average temperature is selected. This process is repeated for each day of the year to build an entire year of normal weather data. 7 http://climate.weatheroffice.gc.ca

Updated: August, 0 Page of 4 5 6 7 8 9 0 4 5 6 Extreme Weather Scenario Process The process used to derive the extreme weather scenario is similar to that used to derive the normal weather scenario. However, rather than selecting the median daily average temperature for each day, the extreme weather scenario uses the maximum daily average temperature during summer months and the minimum daily average temperature for the winter months. Weather Normalization for Rate Class Sales Enersource has developed multivariate regression models to determine energy consumption for each rate class. These models capture the relationship between rate class sales and a number of explanatory variables including weather, calendar, econometric and other explanatory variables. The models were developed based on energy sales from 004 to 0 and include the same input variables such as weather, calendar, and econometric data as the system energy and peak demand models. The models were then used to derive weathercorrected energy sales for each rate class using the normal weather scenario. Class sales models were created for the following customer groups: 7 8 9 0 Residential; Small Commercial; General Service Less Than 50 kw; General Service 50-499 kw; General Service 500-4999 kw; and Large User. 4 5 6 Actual and forecast energy sales in kwh for all rate classes for 008 through 0, net of CDM adjustments, are provided at Attachment. Weathernormalized actual and forecast energy monthly sales in kwh for all rate classes for 008 through 0 are provided at Attachment.

Updated: August, 0 Page of 4 5 6 7 8 9 0 4 5 6 7 8 9 0 Similarly, actual and forecast energy demand for all applicable rate classes for 008 to 0 is provided at Attachment 4. Weather-normalized actual and forecast energy demand for all applicable rate classes for 008 to 0 is provided at Attachment 5. Energy demand forecasts were determined by applying weather-normalized energy sales to a five-year average load factor by customer rate class to determine weather-normalized billing determinants in kw by customer rate class. The weather-normalized rate class sales models performed very well, with adjusted R statistics ranging from 0.8 to 0.95 and MAPE of.5% to.%. The model statistics and a list of coefficient variables for each rate class model, including standard error, T-statistics and P-values, can be found at Appendix C to Appendix H. Historical Performance of Load Forecasting Table 6 below provides a comparison of forecasted, actual and weathernormalized energy consumption from 004 to 0. When adjusted for annual incremental energy savings as a result of Enersource s CDM activities, the forecasted energy consumption was found to have an average variance of 0.% compared to actual energy consumption. Likewise, the forecasted energy consumption was found to vary from the weather-corrected energy consumption by.7%. On average, Enersource's consumption forecasts tended to exceed actual energy consumption.

Updated: August, 0 Page of Table 6: Forecast Performance Vs. Actual and Weather-Corrected Energy, 004 to 0 Year Forecast Energy Actual Energy Variance of Forecasted to Actual Energy (%) Actual Weather- Corrected Variance of Forecasted to Actual W/C Energy (%) 004 7,898,6 7,940,668-0.54 7,906,647-0. 005 8,0,757 8,8,07 -. 8,04,79-0.0 006 8,5,097 8,08,676.40 8,05,586.44 007 8,44,58 8,49,69-0.06 8,05,075. 008 8,4,669 8,096,55.68 7,995,947.90 009 8,064,6 7,74,44 4.00 7,788,68.4 00 7,866,84 7,949,46 -.05 7,79,098.6 0 7,94,667 7,880,490 0.4 7,744,998.4 Average 0..7 4 5 6 7 8 9 0 The higher variances since 008 are largely attributable to the use of projected econometric drivers from the Province Of Ontario s Economic Outlook and Fiscal Plan that supports the annual Ontario Budget. Since late 0, Enersource has begun using the Conference Board of Canada s outlook for the region of Toronto, which includes Mississauga. This change was a result of an analaysis of Enersource's historical load forecasting performance. Based on revised inputs using the Conference Board of Canada outlooks, the performance of Enersource s load forecasting model was much-improved. The predicted results of the models, when adjusted for annual incremental CDM savings, was found to have a variance of -0.0% to actual energy consumption, and.7% compared to weather-corrected energy consumption as illustrated in Table 7 below.

Updated: August, 0 Page 4 of Table 7: Revised Predicted Performance Vs. Actual and Weather-Corrected Energy, 004 to 0 June YTD Year Predicted Energy Actual Energy Variance of Predicted to Actual Energy (%) Actual Weather- Corrected Variance of Predicted to Actual W/C Energy (%) 004 7,9, 7,940,668-0.5 7,906,647 0.8 005 8,4,68 8,8,07 0.5 8,04,79.6 006 8,0,047 8,08,676 0.77 8,05,586 0.8 007 8,9, 8,49,69-0.7 8,05,075.0 008 8,08,984 8,096,55-0.7 7,995,947 0.54 009 7,78,05 7,74,44-0.06 7,788,68-0.65 00 7,889,84 7,949,46-0.75 7,79,098.9 0 7,88,0 7,880,490 0.0 7,744,998.74 Average -0.0.7 0 YTD June,85,740,8,4-0.066%,8,484-0.0% 4 5 6 7 8 9 0 4 5 6 7 8 Customer Number Forecast The City of Mississauga ( City or Mississauga ) currently has an estimated population of 77,000 residents. The City (and Enersource in parallel) went through a very aggressive expansion period spanning the mid-980 s to the mid- 000 s. More recently, Enersource s expansion has slowed relative to the past periods, and available greenfield space for further development has been significantly reduced. Population growth will be driven primarily through intensification and the City has become more focused on higher density housing forms, particularly apartment and condominium development. Enersource has relied on historical data and information obtained from the City s Planning Department as well as Enersource s own internal measures of development and building service applications to forecast projections for customer growth. Actual and forecasted customer numbers by rate class are provided at Attachments 6 and 7 for 007 through 0. Attachment 6 provides the annual average customer numbers for 007 to 0. Attachment 7 provides the year-end number of customers for the same period.

Updated: August, 0 Page 5 of 4 5 6 7 As shown in Attachment 6, Enersource anticipates annual growth rates of.% and.% in its average number of customers for the 0 Bridge Year and 0 Test Year, respectively. As shown in Attachment 7, the number of customers at year-end has grown by an average of,8 per year from 008 to 0. For the 0 Bridge Year and the 0 Test Year, an additional,68 and,9 customers are forecast at year-end, respectively. 8

Updated: August, 0 Page 6 of Attachment A Short Term System Load Energy Model Statistics Regression Statistics Iterations 8 Adjusted Observations 9 Deg. of Freedom for Error 7 R-Squared 0.988 Adjusted R-Squared 0.987 AIC 7.94 BIC 8. Log-Likelihood -,96.8 Model Sum of Squares 790,56,90,955.78 Sum of Squared Errors 9,5,00,678.5 Mean Squared Error 55,04,055.95 Std. Error of Regression 7,4. Mean Abs. Dev. (MAD) 5,4.50 Mean Abs. % Err. (MAPE) 0.86% Durbin-Watson Statistic.09 Ljung-Box Statistic 5.95 Prob (Ljung-Box) 0.0556 Skewness -0.68 Kurtosis.9 Jarque-Bera.577 Prob (Jarque-Bera) 0.4546 Variable Coefficient StdErr T-Stat P-Value Monthly.MonthlyTimeTrend -869.675 7.8 -.6 0.00% Population.Population -0.7 0.06-4. 0.00% Employment.EmpLand 0.57 0.56.67 0.0% Employment.MajOff 6.05 0.507.44 0.00% Monthly.MonthlyGDP.849 0.77.698 0.0% MonthlyWeather.MonthlyDBCubed -0.9 0.08 -.958 0.5% MonthlyWeather.MonthlyBuildUp 7.97 9.549.487 0.06% MonthlyWeather.MonthlyCDD 04.7 9..96 0.00% MonthlyWeather.MonthlyHDD.4 6.5 8.96 0.00% Monthly.WorkingDays 889.97 464.444 6. 0.00% MonthlyWeather.MonthlyDwPtCubed 0.5 0.04.759 0.0% MonthlyCalTrans.Month_Feb -7044.965 849.08 -.00 0.00% MonthlyCalTrans.Month_Aug00-4.66 65. -6.789 0.00% MonthlyCalTrans.Month_Apr -84.54 706.49-6.78 0.00% MonthlyCalTrans.Month_Nov996-4857.49 6776.57 -.668 0.0% MonthlyCalTrans.Month_Dec999 4056.4 6797.6.59 0.05% AR() 0.9 0.076.8 0.0% SMA() 0.5 0.078 4.5 0.00% 4

Updated: August, 0 Page 7 of Attachment B Short Term System Load Peak Model Statistics Regression Statistics Iterations 0 Adjusted Observations 584 Deg. of Freedom for Error 58 R-Squared 0.950 Adjusted R-Squared 0.949 AIC 7.066 BIC 7.090 F-Statistic 5476.454 Prob (F-Statistic) 0.0000 Log-Likelihood (8,908.7) Model Sum of Squares 7,869,8.9 Sum of Squared Errors 6,795,7.00 Mean Squared Error,67.45 Std. Error of Regression 4.7 Mean Abs. Dev. (MAD) 4.6 Mean Abs. % Err. (MAPE).46% Durbin-Watson Statistic.0 Ljung-Box Statistic 708.89 Prob (Ljung-Box) 0.0000 Skewness -0.0 Kurtosis 0. Jarque-Bera 664.75 Prob (Jarque-Bera) 0.0000 Variable Coefficient StdErr T-Stat P-Value CONST 8.958 0.769.04 0.00% EcononmicDrivers.CPI 48.04.777 0.89 0.00% Calendar.TWT.87.04 0.05 0.00% EcononmicDrivers.Employment_Land 0.00 0.000.08 0.00% WeatherTrans.AveDB 4.76 0.50 9.409 0.00% WeatherTrans.MaxDB.064 0.68.967 0.0% WeatherTrans.BuildUp -0.945 0.7-5.505 0.00% WeatherTrans.CDD 6.57 0.8.89 0.00% WeatherTrans.HDD 9.97 0.40.09 0.00% WeatherTrans.XCDD 4.898.7 4.86 0.00% WeatherTrans.LaggCDD 8.08 0.495 6.07 0.00% SunTime.HoursOfLight -0.58 0.96 -.48 0.00% Daily.WkEnd -5.66.55-0.565 0.00% Daily.Aug00-7.7 6.04 -.5.86% CalTrans.AugWkDay 50.09.448 4.5 0.00% CalTrans.SeptWkDay 7.9.84 8.5 0.00% CalTrans.JulWkDay 49.6.40 4.48 0.00% CalTrans.OfficeHolidays.7.07.75 0.0% AR() 0.487 0.0 6.59 0.00% AR() 0.4 0.0 0. 0.00% SMA() 0.4 0.0 8.558 0.00%

Updated: August, 0 Page 8 of Attachment C Short Term Rate Class Model Statistics Residential Regression Statistics Iterations Adjusted Observations Deg. of Freedom for Error 4 R-Squared 0.96 Adjusted R-Squared 0.95 AIC 9.4 BIC 9.790 Log-Likelihood (48.8) Model Sum of Squares 5,900,847,87.9 Sum of Squared Errors 5,89,,46.5 Mean Squared Error 0,80,00.69 Std. Error of Regression 4,845.0 Mean Abs. Dev. (MAD) 9,8.4 Mean Abs. % Err. (MAPE).% Durbin-Watson Statistic.77 Ljung-Box Statistic 7.9 Prob (Ljung-Box) 0.4947 Skewness 0.407 Kurtosis.775 Jarque-Bera.685 Prob (Jarque-Bera) 0.406 Variable Coefficient StdErr T-Stat P-Value Q_Weather.Q_CDD 764.700.8 4.48 0.00% Q_Weather.Q_HDD.44 8.89 6.7 0.00% Q_EconDrivers.Q_Population 0.78 0.00 8.49 0.00% Q_CalTrans.Q_005-47.50 588.8 -.48.40% Q_CalTrans.Q_008 47.98 5450.0.6.64% Q_CalTrans.Q_007-56.60 506.779 -.00.79% Q_CalTrans.Q4_009 9870.666 595.95.940 6.4% Q_CalTrans.Q_Year004 946.89 800.09.40.0%

Updated: August, 0 Page 9 of Attachment D Short Term Rate Class Model Statistics Small Commercial Regression Statistics Iterations 7 Adjusted Observations Deg. of Freedom for Error R-Squared 0.959 Adjusted R-Squared 0.945 AIC 8.7 BIC 8.785 Log-Likelihood (70.7) Model Sum of Squares,85,60.8 Sum of Squared Errors 78,90.4 Mean Squared Error,4. Std. Error of Regression 58.58 Mean Abs. Dev. (MAD) 7.50 Mean Abs. % Err. (MAPE).5% Durbin-Watson Statistic.07 Ljung-Box Statistic. Prob (Ljung-Box) 0.887 Skewness 0.405 Kurtosis.5 Jarque-Bera.4 Prob (Jarque-Bera) 0.54 Variable Coefficient StdErr T-Stat P-Value Q_CalTrans.Q_TimeTrend -5.847 5.670-4.559 0.0% Q_Weather.Q_AveDB 0.00 0.00.87 0.00% EconomicIndicators.CPI 786.8 4.98 559.084 0.00% Q_CalTrans.Q4_005 808.7.699 4. 0.00% Q_CalTrans.Q4_007-6.6 5.78 -.480.09% Q_CalTrans.Q_009-674.08 47.76-4.56 0.0% Q_CalTrans.Q4_009-46.68 4.80 -.00 0.64% Q_CalTrans.Q_00-794.0 49.60-5.09 0.00% SMA() -.45 0.058-4.75 0.00%

Updated: August, 0 Page 0 of Attachment E Short Term Rate Class Model Statistics General Service Less Than 50kW Regression Statistics Iterations Adjusted Observations Deg. of Freedom for Error 4 R-Squared 0.86 Adjusted R-Squared 0.89 AIC 6.8 BIC 6.46 Log-Likelihood (87.) Model Sum of Squares,69,75,58.57 Sum of Squared Errors 0,57,9.8 Mean Squared Error 8,85,7.7 Std. Error of Regression,895.8 Mean Abs. Dev. (MAD),889. Mean Abs. % Err. (MAPE).0% Durbin-Watson Statistic.90 Ljung-Box Statistic.7 Prob (Ljung-Box) 0.5 Skewness 0.00 Kurtosis.49 Jarque-Bera 0.6 Prob (Jarque-Bera) 0.899 Variable Coefficient StdErr T-Stat P-Value Q_CalTrans.Q_TimeTrend -4.979 0.4-5.097 0.00% EconomicIndicators.CPI 559.99 67.045 9.9 0.00% Q_Weather.Q_CDD 5.889 7.4 7.094 0.00% Q_Weather.Q_HDD 7.8.99 8.946 0.00% Q_CalTrans.Q_Year0-856.5 60.45-5.84 0.00% Q_CalTrans.Q_0 9707.459 454.548.80 0.97% AR() -0.8 0.8 -.084 4.80%

Updated: August, 0 Page of Attachment F Short Term Rate Class Model Statistics General Service 50-499kW Regression Statistics Iterations Adjusted Observations Deg. of Freedom for Error 4 R-Squared 0.909 Adjusted R-Squared 0.88 AIC 9.0 BIC 9.88 Log-Likelihood (4.75) Model Sum of Squares 5,458,6,77.04 Sum of Squared Errors,58,68,0.55 Mean Squared Error 47,44,40.90 Std. Error of Regression,4.59 Mean Abs. Dev. (MAD) 8,56.45 Mean Abs. % Err. (MAPE).49% Durbin-Watson Statistic.988 Ljung-Box Statistic 6. Prob (Ljung-Box) 0.650 Skewness -0.047 Kurtosis.06 Jarque-Bera.50 Prob (Jarque-Bera) 0.55 Variable Coefficient StdErr T-Stat P-Value Q_CalTrans.Q_TimeTrend -97.084 4.967-0.059 0.00% EconomicIndicators.CPI 579044.08 77.878 75.065 0.00% Q_Weather.Q_CDD 97.64 6.79.70 0.% Q_Weather.Q_HDD 45.9 7. 6.77 0.00% Q_CalTrans.Q_006-5885.94 86.5-4.89 0.0% Q_CalTrans.Q_00-4960.59 97.060 -.9 6.54% Q_CalTrans.Q_006 40.90 74.48.467.% Q_CalTrans.Q_004-008.05 6.60 -.7.6%

Updated: August, 0 Page of Attachment G Short Term Rate Class Model Statistics General Service 500-4999kW Regression Statistics Iterations Adjusted Observations Deg. of Freedom for Error 4 R-Squared 0.898 Adjusted R-Squared 0.869 AIC 8.806 BIC 9.7 Log-Likelihood (8.0) Model Sum of Squares 5,9,450,44.5 Sum of Squared Errors,85,9,979.6 Mean Squared Error 8,84,95.8 Std. Error of Regression 0,90.5 Mean Abs. Dev. (MAD) 7,5. Mean Abs. % Err. (MAPE).5% Durbin-Watson Statistic.76 Ljung-Box Statistic 5.49 Prob (Ljung-Box) 0.708 Skewness -0.09 Kurtosis.68 Jarque-Bera 0.79 Prob (Jarque-Bera) 0.94 Variable Coefficient StdErr T-Stat P-Value Q_CalTrans.Q_TimeTrend -58.5 96.54-7.78 0.00% EconomicIndicators.CPI -86550.75 587.56-6.878 0.00% Q_Weather.Q_AveDB 6.76.5 6.659 0.00% Q_EconDrivers.Q_TotalMajOff 8.47.670 5.07 0.00% EconomicIndicators.GDP.68 0.660 5.579 0.00% Q_CalTrans.Q_005-8689.700 78.88 -.45.7% Q_CalTrans.Q4_005 744.077 9.00.4.4% Q_CalTrans.Q_007 097.68 49.69.65.9%

Enersource Hydro Mississauga, Inc. Filed: April 7, 0 Page of Attachment H Short Term Rate Class Model Statistics Large User Regression Statistics Iterations 99 Adjusted Observations Deg. of Freedom for Error R-Squared 0.96 Adjusted R-Squared 0.90 AIC 7. BIC 7.570 Log-Likelihood (09.0) Model Sum of Squares 6,84,77,577.5 Sum of Squared Errors 46,68,69.0 Mean Squared Error,0,074.50 Std. Error of Regression 4,585.96 Mean Abs. Dev. (MAD),.09 Mean Abs. % Err. (MAPE).6% Durbin-Watson Statistic.0 Ljung-Box Statistic 7.0 Prob (Ljung-Box) 0.55 Skewness -0. Kurtosis.05 Jarque-Bera.479 Prob (Jarque-Bera) 0.477 Variable Coefficient StdErr T-Stat P-Value Q_Weather.Q_HDD -4.7.759-8.96 0.00% Q_Weather.Q_CDD -6.5 9.55 -.80 0.0% EconomicIndicators.GDP 0.99 0.00 9.870 0.00% Q_EconDrivers.Q_NumberLU 644.775 56.954.767.% Q_CalTrans.Q_Year0-8788.699 5988.08 -.8 0.48% Q_CalTrans.Q_004 686.846 444.5 6.4 0.00% Q_CalTrans.Q_005-5865.78 575.458 -.776.0% Q_CalTrans.Q4_007 0.947 557.05 4. 0.04% Q_CalTrans.Q_006-406.58 5486.0 -.559.79% MA().450 0.8 6.090 0.00%

Updated: August, 0 Page 4 of Attachment I Monthly System Energy and Peak Demand Results, Actual and Weather-Corrected, 997 to 0 Actual Energy Weather- Corrected Weather Correction Actual Peak Demand (MW) Weather- Corrected Peak (MW) Weather Correction - Peak (MW) Month Jan-97 59,60 588,4,76 99 950 4 Feb-97 5,70 5,79-7,477 96 99 Mar-97 545,585 540,775 4,80 9 886 6 Apr-97 5,75 490,984,768 89 90-8 May-97 504,6 500,748,54 854 849 5 Jun-97 558,780 50,474 8,06, 976 6 Jul-97 604,7 64,077-9,46,56,064 9 Aug-97 558,57 574,79-6,58,04,054 - Sep-97 56,6 5,095,57 968 949 9 Oct-97 50,956 57,759,97 97 856 70 Nov-97 547,008 54,707 5,0 976 974 Dec-97 576,496 575,56 95,009 977 Jan-98 585,8 600,40-5,,005 98 Feb-98 5,095 54,7-8, 964 957 7 Mar-98 570,07 564,46 5,6 948 9 5 Apr-98 505,489 509,8 -,89 880 96-8 May-98 57,85 509,7 8,58,084 870 4 Jun-98 58,9 548,60,78,4,060 74 Jul-98 69,87 65,4,954,8,09 89 Aug-98 6,47 59,44 4,8,4,074 67 Sep-98 56,08 55,7 7,7,6 96 5 Oct-98 54,8 50,55 4,70 97 900 7 Nov-98 55,44 560,64-8,90 970 999-9 Dec-98 578,84 59,808 -,994,09,0 6 Jan-99 600,44 6,446 -,0,04 997 45 Feb-99 59,880 55,9 -,05 980 977 Mar-99 570,446 585,40-4,956 94 95 - Apr-99 5,9 55,577 -,656 890 948-59 May-99 56,0 54,6,580,0 9 Jun-99 60,5 565,05 55,00,59,065 94 Jul-99 687,947 645,76 4,8,44,6 08 Aug-99 6,88 605,7 6,465,6, 4 Sep-99 57,54 548,446 4,807,57,050 08 Oct-99 547,06 546,5 504 96 944-8 Nov-99 56,078 58,605 -,57,008,04-4 Dec-99 69,04 69,76-0,7,088,059 9 Jan-00 69,85 68,890 95,074,006 68 Feb-00 58,55 58,9,96,04,00 Mar-00 585,6 60,890-7,764 965 96 Apr-00 540,07 54,9 5,878 95 979-7 May-00 57,60 55,006 0,595,9 97 Jun-00 595,75 59,757,994,8,08 5 Jul-00 68,485 660,50 -,07,86,59 7 Aug-00 65,999 66,660 6,40,00,05 95 Sep-00 589,47 560,88 9,048,05,06 4 Oct-00 576,968 569,55 7,45 986 9 5 Nov-00 59,90 598,80-6,90,08,07 45 Dec-00 647,66 60,97 6,744,4,077 47 Jan-0 649,509 655,478-5,969,09,064 7

Enersource Hydro Mississauga, Inc. Filed: April 7, 0 Page 5 of Actual Energy Weather- Corrected Weather Correction Actual Peak Demand (MW) Weather- Corrected Peak (MW) Weather Correction - Peak (MW) Month Feb-0 58,94 600,546-8,5,060,08 Mar-0 67,74 60,07 7,57,044,00 4 Apr-0 557,78 555,7,556 970 988-8 May-0 584,49 570,48 4,07,05 960 45 Jun-0 645,564 606,50 9,,7,44 0 Jul-0 659,49 688,67-9,80,7,90 8 Aug-0 7,90 649,46 7,556,477,5 5 Sep-0 60,684 577,97 4,7,89,07 8 Oct-0 60,04 590,507,797,045 98 64 Nov-0 597,480 68,689 -,0,06,064 - Dec-0 64,5 64,45-0,0,090,09 - Jan-0 654,7 676,0 -,865,088,0 - Feb-0 594,86 60,097-5,5,5,07 4 Mar-0 6,565 6,90-7,077,0 44 Apr-0 599,97 579,40 0,698,08,05 8 May-0 6,99 589,80,79,0 986 44 Jun-0 654,990 65,769 9,,40,5 5 Jul-0 78,059 70,79 78,0,509,98 Aug-0 745,06 67,846 7,6,49,0 6 Sep-0 67,569 604,8 69,4,474,055 48 Oct-0 6,948 609,56,79,,006 5 Nov-0 65,85 6,48,70,7,066 5 Dec-0 668,69 658,540 9,79,85,6 69 Jan-0 704,64 694,48 9,96,85,8 67 Feb-0 69,7 67,94,447,64,0 6 Mar-0 660,6 65,6 9,76,4,057 87 Apr-0 6,94 59,64 0,,07,08-0 May-0 6,07 605,44 5,79,055,04 40 Jun-0 65,50 640,50 0,840,505,99 06 Jul-0 7, 7,8 -,60,400,78 Aug-0 68,86 6,95 49,45,46,64 5 Sep-0 6,55 65,8 6,96,64,5 40 Oct-0 6,79 68,084 4,65,06,0 0 Nov-0 60,46 64,9 -,88,7,087 0 Dec-0 670,99 674,04 -,,65,8 7 Jan-04 74,7 699,495 5,6,5,4 7 Feb-04 65,440 64,56 0,878,49,7 Mar-04 67,008 676,76-5,68,0,079 4 Apr-04 67,4 60,77 4,468,064,04 9 May-04 66,05 66,86 9,444,7,045 9 Jun-04 647,95 654,0-6,75,408, 97 Jul-04 69,708 78,46-4,754,47,5 75 Aug-04 689,04 690, -,9,79,60 9 Sep-04 66,780 66,40 5,49,50,5 5 Oct-04 6,87 65,970 6,90,095,056 9 Nov-04 64,60 66,548-9,947,4,09 Dec-04 690,656 68,007 9,649,4,65 75 Jan-05 7,04 70,9 0,67,6,60 65 Feb-05 640,60 659,4-9,75,5,4 Mar-05 686,59 67,684,647,9,04 4 Apr-05 68,570 60,660-4,485,050,075-4 May-05 68,4 6,97 6,55,08,048 Jun-05 75,50 66,549 9,80,59, 6 Jul-05 799, 7,47 6,98,570,86 84 Aug-05 767,44 706,446 60,4,50,5 75 Sep-05 674,96 66,706 7,74,60,8 77

Updated: August, 0 Page 6 of Actual Energy Weather- Corrected Weather Correction Actual Peak Demand (MW) Weather- Corrected Peak (MW) Weather Correction - Peak (MW) Month Oct-05 645,45 67,86 9,6,97,066 Nov-05 650,477 667,74-6,58,66, 45 Dec-05 695,8 697,48,07,0,7 47 Jan-06 69,094 70,489-9,88,7,76-5 Feb-06 640,465 655,079-4,745,58,50 9 Mar-06 68,758 684,746-454,4,4 Apr-06 599,75 6,98 -,49,05,066-4 May-06 649,67 67,45,6,50,064 446 Jun-06 696,076 666,768 9,55,48,57 6 Jul-06 784,70 77,948 46,096,580,9 5 Aug-06 74,96 705,704 9,0,60,4 76 Sep-06 67,769 64,797-7,656,9,77 5 Oct-06 64,867 65,797 5,77,084,090-6 Nov-06 647,9 666,66-9,84,7,48 - Dec-06 67,46 686,04-4,96,0,70 Jan-07 70,98 79,607-8,896,0,74 46 Feb-07 669,445 656,595,60,8,58 80 Mar-07 68,55 685,5 -,6,6,06 55 Apr-07 60,7 64,5 6,58,096,087 9 May-07 65,766 6,07 0,98,00,088 Jun-07 79,099 665,4 5,456,556,45 Jul-07 70,75 740,770-0,79,556, 45 Aug-07 769,76 708,670 60,80,560,70 90 Sep-07 667,9 60,99 6,05,46,9 44 Oct-07 657,75 64,470 9,986,74,058 6 Nov-07 665,955 669,8 -,889,75,7 8 Dec-07 69,88 687,75 5,56,05,69 6 Jan-08 709,65 79,50-0,09,95, -8 Feb-08 665,058 659,9 5,5,00,64 6 Mar-08 680,74 677,966,75,4,5 9 Apr-08 6,890 66,9 5,959,078, -54 May-08 68,07 67,09,7,5,094 4 Jun-08 690,56 658,90,606,5,4 7 Jul-08 749,569 76,09,56,47, 48 Aug-08 706,4 699,00 7,,8,4 56 Sep-08 660,6 67,08,540,79,5 6 Oct-08 644,486 6,776,70,079,077 Nov-08 647,496 65,76-5,679,57,4 5 Dec-08 69,49 678,0,946,00,69 Jan-09 709, 698,686 0,66,9,84 0 Feb-09 69,80 6,749 -,508,8,60 Mar-09 66,78 655,55 7,,56,5 Apr-09 594,57 604,6-0,74,066,069 - May-09 60,89 596,97 4,7,5,060 66 Jun-09 69,97 69,868 5,94,0 74 Jul-09 66,87 75,60-6,5,08, -5 Aug-09 709,006 685,00,906,506,94 Sep-09 6,69 65,46 7,8,07,06 Oct-09 66,09 68,996 7,6,04,067-6 Nov-09 67,8 644,87-7,44,095,75-79 Dec-09 667, 670,468 -,,49,67-8 Jan-0 69,770 686,775 4,995,68,46 Feb-0 6,690 69,06-5,67,40,8 Mar-0 64,40 664,970 -,55,055,099-44 Apr-0 589,69 595, -5,58,0,058-47

Enersource Hydro Mississauga, Inc. Filed: April 7, 0 Page 7 of Actual Energy Weather- Corrected Weather Correction Actual Peak Demand (MW) Weather- Corrected Peak (MW) Weather Correction - Peak (MW) Month May-0 65,69 597,46 54,5,406,04 64 Jun-0 675,489 644,,77,54,0 50 Jul-0 780,7 77,09 6,,550,58 9 Aug-0 75,466 678,950 7,540,58,99 8 Sep-0 64,077 6,98 0,7,506,64 4 Oct-0 608,998 606,7,79,0,045-4 Nov-0 67,864 68,998 -,7,086,4-55 Dec-0 669,80 665,485 4,4,8,57 6 Jan- 70,079 687,98,099,74,5 Feb- 68,060 67,6 89,54,7 6 Mar- 659,056 658,78 70,088,05-7 Apr- 60,746 59,97 8,577,046,089-4 May- 66,46 60,044,40,4,095 45 Jun- 657,499 64,095 4,404,44,47 76 Jul- 786,007 7,76 7,9,609,8 9 Aug- 79,979 685,46 44,6,85,98 86 Sep- 64,09 6,5,757,07,99 08 Oct- 68,879 607,56,4,05,04 0 Nov- 608,45 647,9-8,958,069,0-5 Dec- 69, 667,740-8,59,085,0-45

Updated: August, 0 Page 8 of Attachment CDM Impacts on Load by Customer Class, 0 and 0 0 January February March April May June July August September October November December Total Residential (,89,47) (,89,47) (,89,47) (,89,47) (,89,47) (,89,47) (,89,47) (,89,47) (,89,47) (,89,47) (,89,47) (,89,47) (,709,000) Small Commercial - - - - - - - - - - - - - Unmetered Scattered Load - - - - - - - - - - - - - GS < 50 (,78,84) (,78,84) (,78,84) (,78,84) (,78,84) (,78,84) (,78,84) (,78,84) (,78,84) (,78,84) (,78,84) (,78,84) (,60,6) GS 50-499 (6,488) (6,488) (6,488) (6,488) (6,488) (6,488) (6,488) (6,488) (6,488) (6,488) (6,488) (6,488) (4,49,85) GS 500-4999 (87,8) (87,8) (87,8) (87,8) (87,8) (87,8) (87,8) (87,8) (87,8) (87,8) (87,8) (87,8) (4,648,05) Large User (,6,5) (,6,5) (,6,5) (,6,5) (,6,5) (,6,5) (,6,5) (,6,5) (,6,5) (,6,5) (,6,5) (,6,5) (4,74,85) Street Lighting (45,7) (45,7) (45,7) (45,7) (45,7) (45,7) (45,7) (45,7) (45,7) (45,7) (45,7) (45,7) (5,8,799) Total (7,0,594) (7,0,594) (7,0,594) (7,0,594) (7,0,594) (7,0,594) (7,0,594) (7,0,594) (7,0,594) (7,0,594) (7,0,594) (7,0,594) (84,7,) 0 January February March April May June July August September October November December Total Residential (,986,90) (,986,90) (,986,90) (,986,90) (,986,90) (,986,90) (,986,90) (,986,90) (,986,90) (,986,90) (,986,90) (,986,90) (5,84,90) Small Commercial - - - - - - - - - - - - - Unmetered Scattered Load - - - - - - - - - - - - - GS < 50 (,9,74) (,9,74) (,9,74) (,9,74) (,9,74) (,9,74) (,9,74) (,9,74) (,9,74) (,9,74) (,9,74) (,9,74) (9,59,9) GS 50-499 (559,884) (559,884) (559,884) (559,884) (559,884) (559,884) (559,884) (559,884) (559,884) (559,884) (559,884) (559,884) (6,78,6) GS 500-4999 (597,4) (597,4) (597,4) (597,4) (597,4) (597,4) (597,4) (597,4) (597,4) (597,4) (597,4) (597,4) (7,66,687) Large User (748,68) (748,68) (748,68) (748,68) (748,68) (748,68) (748,68) (748,68) (748,68) (748,68) (748,68) (748,68) (8,98,655) Street Lighting (,74,9) (,74,9) (,74,9) (,74,9) (,74,9) (,74,9) (,74,9) (,74,9) (,74,9) (,74,9) (,74,9) (,74,9) (0,95,95) Total (9,98,864) (9,98,864) (9,98,864) (9,98,864) (9,98,864) (9,98,864) (9,98,864) (9,98,864) (9,98,864) (9,98,864) (9,98,864) (9,98,864) (9,46,6)

Enersource Hydro Mississauga, Inc. Filed: April 7, 0 Page 9 of Attachment Actual and Forecast Sales by Rate Class, Net of CDM Impact, 008 to 0 (kwh) Year Residential Small Commercial GS<50 GS 50-499 GS 499-5000 Large User USL SL TOTAL 008 COS,66,89,8,067,0 685,07,649,46,05,589,474,06,5,0,799,84,46,699 4,54,555 8,4,789,000 008,590,75,9 99,64 698,6,74,98,548,87,84,8,548,07,90, 0,94,769 40,809,94 8,095,94,09 009,554,9,855 885,8 676,509,5,88,0,45,5,678,8,04,6,074 0,95,74 40,684,789 7,747,44,746 00,64,4,648 944,558 684,96,468,07,8,098,86,5,969,087,95,7 0,986,5 4,00,740 7,96,,69 0,64,009,995 959,856 674,8,76,09,46,48,47,74,574,05,99,6,6,44 4,7,806 7,878,45,45 0,475,59,07 908,655 64,4,07,99,706,7,,9,69 996,9,90 0,66,80 4,990,90 7,665,46,8 0,475,6,44 96,49 6,0,54,6,685,094,0,5,90,0,58,747 0,756,86 9,704,4 7,698,594,05 Note: Sales figures above include losses Attachment Actual and Forecast Weather-Normalized Sales by Rate Class, Net of CDM Impact, 008 to 0 (kwh) Year Residential Small Commercial GS<50 GS 50-499 GS 499-5000 Large User USL SL TOTAL 008 COS,66,89,8,067,0 685,07,649,46,05,589,474,06,5,0,799,84,46,699 4,54,555 8,4,789,000 008,54,470,000 946,070 690,40,000,0,650,000,80,70,000,066,00,000,00,90 40,809,94 8,06,049,94 009,547,80,000 890,65 67,500,000,,0,000,66,880,000,08,590,000 0,59,49 40,684,789 7,80,064,789 00,57,070,000 98,65 68,000,000,7,90,000,75,490,000,079,70,000 0,80,48 4,00,740 7,787,0,740 0,54,980,000 964,45 667,480,000,87,00,000,40,90,000,054,640,000,5,575 4,7,9 7,78,4,9 0,475,59,07 908,655 64,4,07,99,706,7,,9,69 996,9,90 0,66,80 4,990,90 7,665,46,8 0,475,6,44 96,49 6,0,54,6,685,094,0,5,90,0,58,747 0,756,86 9,704,4 7,698,594,05 Note: Sales figures above include losses

Updated: August, 0 Page 0 of Attachment 4 Actual and Forecast Sales by Rate Class, Net of CDM Impact, 008 to 0 (kw) Year GS 50-499 GS 499-5000 Large User SL TOTAL 008 COS 6,48, 5,0,,70,956 5,90,564,599 008 6,55,55 5,77,864,84,49 09,605,585,04 009 6,5,48 5,08,457,800,97 0,507,45,9 00 6,0,886 5,084,89,8,545,465,,786 0 6,65,460 4,997,505,87,77,096,,798 0 6,09,64 5,,67,7,059 9,69,0,65 0 6,4,0 5,54,8,77,67 49,889,08,56 Note: Sales figures above include losses Attachment 5 Actual and Forecast Weather-Normalized Sales by Rate Class, Net of CDM Impact, 008 to 0 (kw) Year GS 50-499 GS 499-5000 Large User SL TOTAL 008 COS 6,48, 5,0,,70,956 5,90,564,599 008 6,66,494 5,70,97,8,54 09,605,579,840 009 6,45,99 5,5,76,86,66 0,507,504,55 00 6,00,86 5,060,,87,765,465,90,79 0 6,0,459 4,98,47,840,076,089,7,040 0 6,09,64 5,,67,7,059 9,69,0,65 0 6,4,0 5,54,8,77,67 49,889,08,56 Note: Sales figures above include losses

Enersource Hydro Mississauga, Inc. Filed: April 7, 0 Page of Attachment 6 Actual and Forecast Average Number of Customers &/or Connections by Rate Class, 007 to 0 Year Residential Small Commercial GS<50 GS 50-499 GS 499-5000 Large User Total % Growth USL SL 007 6,6 9 6,04,977 467 9 8,940,865 48,78 008 COS 66,85 80 6,08,986 470 9 87,55,08 48,55 008 64,9 75 6,8,954 469 0 85,6.%,874 48,70 009 67,085 77 6,47,9 48 0 88,6.6%,889 48,688 00 69,768 74 6,70,99 48 0 9,56.6%,95 49,000 0 7,46 70 7,000,986 47 9,98.5%,9 49,0 0 74,659 68 7,87,947 464 0 96,54.%,97 49,507 0 76,865 68 7,54,950 464 9 98,990.%,94 49,985 Note: Includes the impact of CDM Attachment 7 Actual and Forecast Year-End Number of Customers &/or Connections by Rate Class, 007 to 0 Year Residential Small Commercial GS<50 GS 50-499 GS 499-5000 Large User Total % Growth USL SL 007 6,775 90 6,04 4,04 460 9 8,58,865 48,84 008 COS 70,80 80 6,5,986 475 9 9,8, 48,475 008 65,88 77 6,8,867 477 0 86,7.8%,88 48,556 009 68,88 76 6,64,956 486 0 89,540.5%,896 48,89 00 7,47 7 6,86 4,06 480 0 9,77.7%,94 49,8 0 7,444 68 7,6,945 46 95,94.%,9 49,79 0 75,874 68 7,4,948 464 9 97,875.4%,94 49,76 0 77,856 68 7,657,95 464 9 00,04.%,940 50,5 Note: Includes the impact of CDM