EB04-06 Corrected: 0 Jan Page of LOADS, CUSTOMERS AND REVENUE 4 Toronto Hydro s total load, customer and distribution revenue forecast is summarized in Table. The revenue forecast is calculated based on proposed distribution rates, excluding commodity, rate riders, and all other non-distribution rates. 6 7 Table : Total Load, Revenues and Customers Total Normalized Total Normalized YEAR GWh MVA Total Distribution Revenue ($M) Total Customers 009 Actual,7.8 4,74.7 $47. 689,99 00 Actual,607. 4,7. $9. 696,79 0 Actual,49.0 4,00. $. 70,76 0 Actual,69. 4,44. $6. 7,09 0 Actual,. 4,68.7 $8.6 74,44 04 Bridge,08. 4,7.7 $9.4 76,974 0 Test 4,99. 4,697. $66. 749,679 06 Test,07.4 4,806. $696.8 76,09 07 Test 4,84.6 4,6. $7.9 77,80 08 Test 4,696.9 4,84.4 $809.9 78,07 09 Test 4,6.4 4,9. $87. 796,86 Notes:. Total Normalized GWh are purchased GWh (before losses), and are weather normalized to the Test Year heating and cooling degree day assumptions.. Total Normalized MVA are weather normalized MVA.. Total Distribution Revenue is weather normalized and includes an adjustment for the Transformer allowance. 4. Total Customers are as of mid-year and exclude street lighting devices and unmetered load connections. /C 8 9 0 The detailed load forecasts by rate class are shown at,,, Appendix B. Forecasts of customers by rate class are shown at,,, Appendix C. Forecast of distribution revenues by rate class are shown at,,, Appendix E.
EB04-06 Corrected: 04 Sep Page of The information provided for the Load, Customers and Revenue exhibit is prepared according to the Board s Filing Requirements for Electricity Distribution Rate Applications (July 7, 0). 4 6 7 8. HISTORICAL LOADS Historical total system load (actual and weather-normalized) for Toronto Hydro is illustrated in Figure below. 7,00.0 7,000.0 6,00.0 6,000.0,00.0,000.0 4,00.0 4,000.0 Annual Historic Purchased Energy, GWh 00 004 00 006 007 008 009 00 0 0 0 /C Actuals Weather Normalized 9 Figure : Historical Purchased Energy 0 4 Since 007, there has been a significant decrease in total energy consumption. Essentially flat growth over the 004-006 period has been replaced by declining loads over the 007-0 period. While it is difficult to precisely attribute this decline to any particular event, Toronto Hydro believes that the effect of conservation activities both program driven and naturally occurring - continue to have a significant impact on the overall load change. Furthermore, in late 008 and 009, economic conditions also
EB04-06 Corrected: 04 Sep Page of contributed to the load decline. Table shows a summary of the total historical normalized annual loads and growth. 4 Table : Historical Annual Load Year Total Normalized GWh Growth GWh Percentage Change (%) 00 6,9.7 004 6,69. 0.% 00 6,64.7-8 -0.% 006 6,767. 6 0.% 007 6,. -444 -.7% 008 6,60.9-6 -0.6% 009,7.8-88 -.% 00,607. 4 0.% 0,49.0-88 -0.7% 0,69. 0 0.9% 0,. -46 -.7% /C 6 7 8 9 0. LOAD FORECAST METHODOLOGY Toronto Hydro s load forecast methodology consists of the three-step process which explicitly takes into account historic and forecast CDM impacts. First, the actual historical cumulative CDM impacts are added back to the system purchased energy. Second, the load (gross of CDM) is forecasted based on multifactor regression techniques. Third, the cumulative forecast CDM impacts are deducted from the gross load forecast to derive to the load forecast (net of CDM). 4 6 Energy forecasts are developed for each rate class separately. Peak demand at the rate class level is based on historical relationships between energy and demand. Total system load is summed from the individual rate class loads. The forecast of customers by rate class is determined using time-series econometric methodologies. Revenues are
EB04-06 ORIGINAL Page 4 of determined by applying the proposed distribution rates to the rate class billing determinants for the forecast period. 4. kwh LOAD FORECAST 6 7 8 9 0.. Multivariate Regression Model The process of developing a model of energy usage involves estimating multifactor models using different input variables to determine the best fit. Different models were fit based on a priori assumptions about which input variables impact energy use. Using stepwise regression techniques, numerous explanatory variables were tested with the ultimate model being determined based on model statistics and judgement. 4 6 7 8 Models are developed for each rate class separately. This methodology allows for greater detail in modelling loads, and allows for the different interactions to be modelled independently. All of the regression models use monthly kwh per day as the dependent variable, and monthly values of independent variables from July, 00 through to the latest actual values (December 0) to determine the monthly regression coefficients. 9 0 4 The main drivers of the energy consumption over time are weather, energy conservation activities both program and natural related, as well as calendar, economic and demographic conditions. While load impacts related to the CDM program activities are explicitly taken into account prior to and after the modelling (see section below on CDM forecast), the remainder of the effects are captured through the multivariate regression model. 6 7 8 The primary driver of consumption within years remains weather. Weather impacts on load are apparent in both the winter heating season, and in the summer cooling season.
EB04-06 ORIGINAL Page of 4 6 7 8 9 0 For that reason, both Heating Degree Days ( HDD a measure of coldness in winter) and Cooling Degree Days ( CDD a measure of summer heat) are captured in the multifactor regression model. In previous rate filings, Toronto Hydro has indicated that the standard definition of HDD, which uses 8 degrees Celsius as the point at which loads start to be impacted by temperature, was not as effective as a measure which uses 0 degrees Celsius as the balance point for the HDD measure. Figure below shows the relationship between temperatures and loads for the period of July 00-December 0. It is clear that the relationship between heating loads and temperature changes at 0 degrees Celsius. Toronto Hydro uses this 0 degrees Celsius balance point for construction of its HDD measure. THELS purchased energy vs average temperature,700 Monthly purchased energy, GWh,600,00,400,00,00,00,000,900,800 - -0-8 -6-4 - 0 4 6 8 0 4 6 8 0 4 6 8 Average monthly temperature, C Figure : Purchased Energy vs Average Temperature 4 Dew point temperature is another type of weather factor, included as an explanatory variable for the GS <0 kw, GS 0-999 kw, GS 000-4999 kw, and Large Use customer classes. This variable captures the impact of humidity on consumption and
EB04-06 ORIGINAL Page 6 of shows the positive impact of temperature on loads during summer months and negative impact during winter months. 4 6 7 8 9 0 Demographic, economic conditions and natural related conservation activities are captured within the model by customer, population, Toronto unemployment rate and time trend variables. The Toronto unemployment rate reflects the level of economic fluctuations, and was found to be statistically significant in the GS <0 kw, GS 0-999 kw and GS 000-4999 kw class models. Population and customer variables capture overall levels of demographic fluctuations, and were found to be statistically significant in the Residential, GS <0 kw, GS 0-999 kw, GS 000-4999 kw and Large Use class models. 4 6 7 The time trend variables used in the models are intended to capture trends which are not otherwise explained by the other driver variables. The Residential model uses a simple time trend variable which captures the observed downward trend in consumption over the historical period. Since the models are based on consumption with CDM loads added back to loads, CDM activities alone cannot explain this trend. 8 9 0 4 6 7 8 For the GS<0kW and Large Use customer classes, a clear change in trend has occurred. For these two classes, Toronto Hydro has incorporated a linear spline time trend. Consumption for these two classes displays a clear change in trend between the 00-09 period and the 00- period, and is captured by this type of time trend. Another factor determining energy use in the monthly model can be classified as calendar factors. For example, the number of business days in a month will impact total monthly load. To capture the different number of days in the calendar months the modelling of purchased energy was performed on a per-day basis. To reflect different numbers of business days in the month and, consequently, different number of peak
EB04-06 ORIGINAL Page 7 of hours, business day percentage was used in those class models. A dummy variable was also included to reflect the impact of the 00 August blackout on energy use in that month. 4 6 7 8 9 0,,, Appendix A- contains the historical and forecast load and input variable details. The model statistics for each class model are shown in,,, Appendix A-. From the regression models, the forecast of energy usage is determined by applying the model coefficients to forecasts of the input variables. 4 6 7 8 The forecast for heating, cooling degree-days, and dew-point temperature inputs is based on a ten-year historical average of HDD, CDD and Dew. A 0-year average was chosen over the 0-year average based on analysis of the annual HDD and CDD data that shows a definite trend in HDD and CDD (see Figure below). Using an average over the longer time period would therefore be less reflective of the most recent data and an inferior forecast of HDD and CDD. Toronto Pearson International Airport station was used as the climatological measurement point for establishing monthly HDD and CDD.
EB04-06 ORIGINAL Page 8 of,00 800.0,000 6.0 HDD 0,00,000 40.0 7.0 CDD 00 00.0 0-7.0 994 99 996 997 998 999 000 00 00 00 004 00 006 007 008 009 00 0 0 0 HDD0 CDD Linear ( HDD0 ) Linear (CDD) Figure : Historic CDD and HDD 4 The forecast of the City of Toronto unemployment rate and population was derived based on the Conference Board of Canada forecast of the Toronto CMA unemployment rate and population using a pair regression model. 6 7 The following table summarizes the variables included in each of the rate class energy models.
EB04-06 ORIGINAL Page 9 of Table : Regression Variables by Rate Class Residential Competitive Sector Multi-unit Residential GS<0 kw GS 0-999 kw GS,000-4,999 kw Large Use Street lighting Unmetered Load HDD0 per day HDD0 per day HDD0 per day HDD0 per day HDD0 per day CDD per day CDD per day CDD per day CDD per day CDD per day Toronto City Population Time Trend Blackout dummy Normalized Average Use per Customer Dew Point Temperature Number of GS<0 kw customers Toronto Unemployment Rate Dew Point Temperature Business Days Percentage Toronto Unemployment Rate Dew Point Temperature Business Days Percentage Toronto Unemployment Rate Dew Point Temperature Business Days Percentage Number of LU customers Average use per device Simple extrapolation technique Intercept term Time Trend Number of GS 0-999 kw customers Number of GS,000-4,999 kw customers Time Trend Blackout dummy Blackout dummy Blackout dummy Blackout dummy Intercept term Intercept term Intercept term Intercept term
EB04-06 ORIGINAL Page 0 of 4 6.. Normalized Average use per Customer ( NAC ) Model The load forecast for Competitive Sector Multi-unit Residential ( CSMUR ) was determined using the NAC as the most suitable model for this relatively new rate class. Historically, CSMUR customers were part of Residential rate class, however, as directed by the Ontario Energy Board in EB-00-04, Toronto Hydro established a separate rate class with rates implemented as of June, 0. 7 8 9 Similarly to the other rate classes, the forecast for CSMUR consumption explicitly incorporates CDM volumes. 0 4 The Normalized Average use per customer for CSMUR rate class is based on hourly load profile sample data from 0 and is weather corrected over a ten-year historical average of HDD and CDD. This average use per customer is then multiplied by the forecast of CSMUR customers to arrive at the forecast consumption for this class. 6 7 8 9 0 4 6 7 8 4. CLASS DEMAND FORECAST The forecast of monthly peak demand by customer class, which is used to determine revenue for those customers billed on a demand basis, is established using historical relationships between energy and demand. The demand forecast is explicitly adjusted to reflect the impacts from the cumulative estimated CDM activities and subsequently, converted based on the billing factors to the peak demand forecast (net of CDM). The cumulative CDM demand forecast consists of incremental CDM forecast as well as persistence from the CDM demand savings. The demand savings for the Demand Response ( DR ) programs were excluded from the total CDM demand savings. Toronto Hydro believes that the peak demand savings from the DR programs are not necessarily coincident with customer s individual peak demand for the demand reduction occurrence (see table with the 04-09 cumulative CDM kw forecast under the section below).
EB04-06 Corrected: 04 Sep Page of. CDM FORECAST Consistent with the Board s CDM Guideline EB-0-000, Toronto Hydro confirms that it has explicitly included the impacts of CDM into its load forecast. 4 6 7 The cumulative CDM forecast deducted from the gross load (step of the three-step process described previously) includes the CDM savings for programs delivered in each year plus the persistence of these programs through subsequent years. 8 9 0 4 6 7 8 9 0 4 6 7 8 The forecasted CDM savings for the 0 to 09 period were developed based on the assumption that there will be a continuation of conservation programs throughout the rate filing period as announced in the Conservation First Framework released on March, 04 by the Ministry of Energy. In the absence of the framework being developed in detail, the projected conservation achievements are based on a number of assumptions that partly rely on Toronto Hydro s experience and progress toward the current provincial targets, and partly on the anticipated target assigned for the 0 to 00 conservation planning period. With respect to the timing of CDM savings in this forecast, there are significant uncertainties due to the fact that there is very little information regarding the landscape of conservation offerings, the level of funding, and target and contribution calculations. However, it is known that the new phase of programming will prioritize customer energy savings, moving away from the peak demand focus of the former saveonenergy strategies. This is most obviously reflected by the fact that the provincial target carries only an energy total, rather than energy and demand requirements. Choosing this as the most reliable starting point, Toronto Hydro forecasts achieving the required energy savings assuming it will be responsible for achieving approximately % of the provincial total of 7 TWh, or. TWh by the end of 00. /C
EB04-06 Corrected: 04 Sep Page of 4 6 In terms of allocating the Conservation First target alignment, the effects of new program build-up and then eventual market saturation determined the basic assignment of annual savings. From historical program experience, monthly project application patterns and realized monthly energy savings were calculated and extrapolated for the forecast. Furthermore, conservation and efficiency measure persistence has been applied, which is shown to limit the increase of the cumulative conservation totals year after year. 7 8 9 0 4 6 7 8 9 0 4 The result of the factors mentioned above produces a CDM energy reduction forecast, with the anticipated end result being a significant step towards realizing the Conservation First target of nearly. TWh by 00. It is anticipated that Toronto Hydro will be equipped with the resources to achieve this level of achievement, as is expected with the current suite of programming and the saveonenergy targets that are on track and nearing a close. Historical and estimated CDM savings used in the load forecast are gross numbers and hence, include free riders. Toronto Hydro believes that gross CDM savings are the correct values to apply in to the load forecast used to determine billing units. With respect to future lost revenue adjustment mechanism variance account ( LRAMVA ) however, it is Toronto Hydro s understanding that the CDM applied in this forecast will be the basis for the LRAMVA and that the LRAMVA balance will reflect the difference between estimated and actual CDM savings on a net basis. Tables 4 and represent the summaries of the cumulative forecast CDM consumption and demand impacts by class used for establishing the load forecast (net of CDM). /C
EB04-06 Corrected: 04 Sep Page of Table 4: Cumulative Forecast CDM Consumption Impacts, MWh (Gross) Year Residential CSMUR GS <0 kw GS 0-999 kw GS 000-4999 kw Large Use Total 04 49,88,849 9,894 48,70,80 7,806,0,960 0 47,96,0 7,87 8,0 7,78 4,70,66,806 06 4,74,679 60,6 68,47 86,8 7,40,84, 07,60,9 40, 8,07 06,0 44,800,08,68 08 74,89,747 44,9 90,884,09,9,7,8 09 8,40 4,09 49,0,069,040 48,080 60,0,46,0 /C Table : Cumulative Forecast CDM Demand Impacts, MW (Gross) Year GS 0-999 kw GS 000-4999 kw Large Use Total 04 794.8 4.7 4.8,69.4 0 968.9 46.64 40.9,880. 06,7. 477.49 46.67,078.4 07,7.4 09.9 49.6,9. 08,6...8,8.04 09,7.6 60.0 7.68,8. /C 4 6 Table 6 includes 04-09 total gross forecast CDM consumption and demand impacts per year with no prior persistence. Table 7 includes 04-09 total gross forecast CDM demand impacts per year with no prior persistence for those customers billed on a demand basis.
EB04-06 Corrected: 04 Sep Page 4 of Table 6: 04-09 Total Gross Forecast CDM Consumption Impact, MWh 04 0 06 07 08 09 04 CDM Forecast 9,0 7,44 4,889 9,698,78 00,78 0 CDM Forecast 99,69 46,00 4,46 7,94 9, 06 CDM Forecast 0,946 98,80 9,77 88,9 07 CDM Forecast 47,8 6,0 9,874 08 CDM Forecast 4,04 48,60 09 CDM Forecast 7,66 Total 9,0 7,07 9, 909,868,,90,4,770 /C Table 7: 04-09 Total Gross Forecast CDM Demand Impact, MW 04 0 06 07 08 09 04 CDM Forecast 0..80 0.4 4.9.87 7.67 0 CDM Forecast.6.97 7.8 07.7 9.4 06 CDM Forecast 64.7 47.77 4.0.40 07 CDM Forecast 0.4 4.0 49.6 08 CDM Forecast 9.00 40.7 09 CDM Forecast 7.7 Total 0. 406.6 76.88,09.08,00.70,844.9 /C 4 6 7 8 9 0 6. CUSTOMER FORECAST The forecast of new customers for all classes is primarily based on extrapolation models for each rate class. Customer additions in the company s operating area have been fairly flat over recent history, with the exception of the customers from the newly classified CSMUR rate class (implemented on June, 0), whose rate of growth has been increasing as a result of Toronto Hydro s suite metering activities. Historically, CSMUR customers were included in the Residential rate class. With the establishment of the new CSMUR class, historical Residential customers that fall under a current definition of CSMUR class were identified, excluded from the Residential class, and forecasted independently. The detailed forecast of customers by rate class is found in, Tab,, Appendix C-.
EB04-06 ORIGINAL Page of 7. ACCURACY OF LOAD FORECAST AND VARIANCE ANALYSES Table 8 summarizes the variances between actual loads and the last Board-approved loads (filed in Toronto Hydro s EB-00-04 0 rate filing). 4 Table 8: Forecast vs. Actual Purchased Energy Board- Load Actual Load Weather Normalized Actual Forecast GWh GWh Variance GWh Variance 00,74.,69..0%,60.0 0.9% 0,8.6,8.8.%,409. 0.% 6 7 8 9 0 Year to year variances in historical loads and customers reflect the impacts of weather, economic conditions, CDM, and normal customer growth. Some year to year variance arises from re-classification of customers. Loads and customers reported for the Residential class are impacted in 0 by the creation of the new Competitive Sector Multi-Unit Residential class, which was formerly part of the Residential class. 4 For the forecast periods, year to year variances in loads and customers reflect the impact of model driver variables and CDM assumptions. In addition, some re-classification is anticipated for the General Service and Large Use classes in 04. 6 7 Tables showing year-over-year and actual vs. Board-approved loads and customers can be found in,, Schedules, Appendices B- and C-.
Table : Model Input Data Toronto Hydro Electric System Limited EB 04 06 Appendix A Corrected: 04 Sep Page of 4 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Col. 9 Col. 0 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Col. 9 Col. 0 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Month Residential Competitive Sector Multi Unit Residential (CSMUR) Purchased Energy per day, kwh (by customer class) GS<0 kw GS 0 999 kw GS 000 4999 kw Large Use Street lighting Unmetered Scattered Load Cumulative CDM impacts per day, kwh Residential CSMUR GS<0 kw GS 0 999 kw HDD0 per day CDD8 per day Toronto Population ('000) GS 000 4999 kw Large Use Residenti al 4 Jul 00 9,04,876 8,649,8,76, 6,7,79 8,80,70 6,949 66,00 0.0 6.,07 0.6 7.0 8.8 66,908 0,76 484 46 Aug 00 8,, 8,8,94 9,604,6,7,90 8,46,09 46,77 9,47 0.0 4.6,07 0. 67.7 0.4 66,87 0,86 480 46 6 Sep 00 6,4,70 7,687,79 8,98,6 4,984,7 7,89,84 69,6 8, 0.0.9,07 0.6 66.7 9.0 66,86 0,69 48 46 7 Oct 00 4,,86 6,947,084,96,64,64,06 7,7,60 06,06,740.0 0.,07 4 4 0 4.8 7.0 8. 66,89 0,669 48 46 8 Nov 00,9,00 7,, 7,6,66,90,998 7,04, 6, 8, 7. 0.0,0 0 0. 70.0 7. 66,88 0,680 48 46 9 Dec 00 7,64, 7,,46 8,96,964,78,67 7,7,4 7,8 6,4.0 0.0,040 6 6 0.6 64. 7.9 66,94 0,708 486 46 0 Jan 00 8,,9 7,96,78 0,09,90 4,99,96 7,98,4 40,49 6,77 8. 0.0,0 7 7 0.6 7.0 9. 66,987 0,7 487 46 Feb 00 7,946,87 8,,90 0,77,9 4,9,0 7,6,76 7,97 6, 7.0 0.0,08 8 8 0. 7.4 8.0 67,9 0,786 487 46 Mar 00 6,06,690 7,6,9 8,08,88 4,0,408 7,96,898 9,070 9,786 0.8 0.0,06 9 9 0. 67.7 7.7 67, 0,794 48 46 Apr 00 4,6,87 7,,777 6,707,97,80,89 7,49,67 88,09 60,68. 0.,04 0 0 0.8 70.0 8. 67,040 0,809 487 46 4 May 00,7,4 6,6,8 4,87,76,6,484 7,07,78 6,6,86 0. 0.0,04 0 6. 67.7 9.4 67,6 0,88 49 46 Jun 00 4,77,99 7,06,9 7,040,86 4,9,6 7,68,6,69 8,46 0.0.8,09 0.9 70.0 8.7 66,98 0,84 489 46 6 Jul 00 6,98,890 7,87,787 9,4,8,8,6 7,77,09,9 6, 0.0.8,04 0 4.7 7.0 0.6 67,046 0,848 49 46 7 Aug 00,7,06 7,4,6 7,79,98 4,449,68 7,48,74 8,90 9,766 0.0 4.,067 4 4 6.0 64. 9.8 67,040 0,80 49 46 8 Sep 00 4,0,78 6,90,0 6,8,74 4,74,78 7,48, 79,868 7,60 0.0 0.8,07 0.7 70.0 9.4 66,964 0,8 49 46 9 Oct 00,98,8 6,77,7,744,47,778,46 6,9,996 6,8 9,766. 0.0,09 6 6 0 4.7 7.0 7.9 67,08 0,89 49 46 0 Nov 00,09,66 6,999,78 6,9,0,9,69 7,09,8 8,778 6,94.4 0.0,07 7 7 0.6 66.7 8. 66,89 0,874 496 46 Dec 00 6,844,4 7,7,89 7,677,89,794,88 6,9,8 400, 6,70 0. 0.0,0 8 8 0 4.0 67.7 6.9 67,064 0,908 497 47 Jan 004 7,978,69 7,904,747 0,86,80,4, 8,77,88 40,04 67, 9.4 0.0,04 9 9 0. 67.7 8.4 66,97 0,99 497 47 Feb 004 7,70,90 7,700,44 9,66,9 4,66,89 7,,84 6, 6,6.8 0.0,08 0 0 0 8. 69.0 7.6 67,046 0,97 497 47 4 Mar 004,69,667 7,6, 7,98,4 4,44,0 7,68,86 4,9 6,66 7.9 0.0,0 0. 74. 8.7 67,00 0,986 499 47 Apr 004 4,06,79 6,860,879 6,007,84,99,97 7,4, 84,77 60,004.9 0.0,06 0 0.6 70.0 7. 66,90,007 498 47 6 May 004,90,60 6,697,9,467,4,6,94 6,88,79 6,779 8,600 0.6 0.,06 0 7.9 64. 9. 66,87,08 498 47 7 Jun 004,68,848 6,996,090 6,877,869 4,7,47 7,0,466 0,98 6,946 0.0.,08 4 4 0. 7. 8. 66,789,08 494 47 8 Jul 004 4,78,7 7,40,0 8,0,,00,76 7,789,4 47,688 64,679 0.0.8,06 0. 67.7 0.0 66,7,04 49 47 9 Aug 004 4,9,7 7,,804 7,8,86 4,779,0 7,49,884,9 60,7 0.0.9,076 6 6 0 4. 67.7 9. 66,7,076 494 47 0 Sep 004,9,06 7,074,7 7,7,76,4,67 7,9,00 87,88 6,7 0.0.4,08 7 7 0.8 70.0 8. 66,68,04 494 47 Oct 004,466,69 6,96,7,678,84,94,46 6,960,70,74 9,8 0.8 0.0,066 8 8 0 6.0 64. 8.6 66,496,097 49 47 Nov 004 4,74,8 6,9,86 6,94,98 4,049,87 7,04,74 80,877 6,489 4.7 0.0,09 9 9 0 0. 7. 7.7 66,8,9 498 47 Dec 004 6,96,969 7,498,07 9,00,69 4,74,60 7,87,8 9,686 67,78.8 0.0,060 0 0 0 6. 67.7 7.9 66,0,46 498 47 4 Jan 00 7,644,488 7,884,0,067,90 4,884,09 7,0,4 4,09 6,047 6.8 0.0,070 0 0.7 64. 7.8 66,464,67 0 47 Feb 00 6,986,994 7,790,04 9,96,06 4,769,846 7,4,68 80,0 9,98 4.0 0.0,08 0 7. 7.4 8.9 66,68,84 0 47 6 Mar 00,704,996 7,44,0 8,9,78 4,4,49 7,9,9 7,9 7,7.6 0.0,088 0 6. 7.0 8.4 66,60,98 04 47 7 Apr 00,76,84 6,766,8 6,9,6,76,7 6,90,7 96,87,9.8 0.0,07 4 4 0 0.6 70.0 8. 68,964,46 48 8 May 00,79,9 6,8,0,7,4,7,84 6,8,4,600 49,04 0.9 0.0,07 0 4. 67.7 8. 66,48,8 06 47 9 Jun 00 7,,69 7,80,80 0,8,06 6,6,447 8,4,88 9,904,947 0.0 4.9,09 6 6 0.4 7. 8.6 66,668,4 07 47 40 Jul 00 8,89,649 8,66,,78,64 6,,80 7,77,70 49,964 8,88 0.0 6.,0 7 7 0 6.0 64. 9. 66,74, 07 47 4 Aug 00 7,48,77 7,764,78 0,49,66 6,074,4 7,8,64 4,4,64 0.0 4., 8 8 0.8 7.0 8.9 66,807,4 09 47 4 Sep 00 4,7,84 7,084,6 8,04,770,4,44 7,74,68 80,008,7 0.0.7, 9 9 0.0 70.0 7. 66,88, 0 47 4 Oct 00,6,49 6,,60 6,087,00 4,69,46 6,868, 0,97,9. 0.,098 40 40 0 7. 64. 7. 66,9,67 4 47 44 Nov 00 4,87,6 6,90,069 7,4,9 4,466,00 7,0,94 80,0 49,78.4 0.0,09 4 4 0. 7. 6. 67,066,86 47 4 Dec 00 6,64,476 7,47,90 9,7,09 4,96,00 7,0,79 90,67 6,98. 0.0,097 4 4 0 6. 64. 7. 67,47,498 7 47 46 Jan 006 6,7,60 7,,899 8,878,874 4,408,784 6,987,66 407,6,07 97 9.8 0.0,09 4 4 0.8 67.7 7.6 67,09,49 9 47 /C 47 Feb 006 6,84,64 7,4,777 9,98,80,09,66 7,,46 84,49 6,94,76.6 0.0,0 44 44 0 7.7 7.4 7.9 67,8,8 04 46 /C 48 Mar 006,46,88 7,66,8 8,07,88 4,68,47 7,6,6 4,,07,678 8.7 0.0,0 4 4 0. 74. 7. 67,4,8 7 47 /C 49 Apr 006,9,6 6,47,96,4,60,,0 6,80,677 9,9 0,,6.6 0.0,7 46 46 0 0.7 6. 7.0 67,08,7 9 47 /C 0 May 006,94,4 6,470,7,888,7 4,86,44 7,84,46 7,48 0,8,697 0. 0.8,04 47 47 0 7.9 7.0 7.4 67,00,77 46 /C Jun 006,49,974 7,089,496 8,6,90,667,78 7,60,9 9,7 64,4,6 0.0., 48 48 0.6 7. 7. 67,004,97 48 /C Jul 006 7,9, 7,80,08,08,0 6,76, 8,6,7 0,990 7,8,07 0.0.4,9 49 49 0 7.4 64. 8.4 67,009,89 0 48 /C Aug 006,9,776 7,,97 9,47,040,969,7 7,769,9 46,0 67,0 60,0 0.0.,0 0 0 0 4.6 7.0 9.0 67,089,47 49 /C 4 Sep 006,47,994 6,48,984,986,4 4,7,6 7,0,9 7,4 8,60 7,7 0. 0.4,0 0.6 66.7 8.0 67,09,40 9 49 /C Oct 006,46,709 6,406,677 6,87, 4,,78 6,99, 7,4 6,87 77,940.4 0.0,07 0 4.8 67.7 7. 67,0,44 49 /C 6 Nov 006 4,88,4 6,77,40 7,48,9 4,48,704 7,7,7 8,4 6,0 84,8 4.8 0.0,04 0. 7. 8. 67,068,46 49 /C 7 Dec 006,70,68 4 6,86,88 7,90,7,8,7 6,798,7 86,0 6,8 7,74 8. 0.0,0 4 4 0.4 6..9 67,07,444 6 49 /C 8 Jan 007 6,684,00 4,94 7,7,9 9,6,098 4,77,49 6,87,00 4,0 6,099 77,68 64 668 6 6.9 0.0,099 0 6.6 7.0 7. 66,90,46 09 49 /C 9 Feb 007 7,8,8,07 7,777,090,97,997,446,009 7,66, 89,67 66,76 0,0,8,76,4,49 8.4 0.0,8 6 6 0.4 7.4 7.7 66,9,4 9 49 /C 60 Mar 007,646,66 4,697 7,07,66 8,79,9 4,40,44 7,070,94,6 6, 90,40 4,88, 4,887 4,798 9.8 0.0,4 7 7 0.6 7.0 8. 66,8,0 7 48 /C 6 Apr 007,90,99,4 6,6,04 6,879,7,84,8 6,946,74 99,6 6, 9,78,704,89,707,68 4.9 0.0,4 8 8 0 0.0 66.7 7. 66,84,476 7 49 /C 6 May 007,097,78,4 6,4,897 6,0,0 4,,89 6,79,0 4,6 6,69 96,7,84 4,0,846,776 0. 0.7,0 9 9 0.8 7.0 8. 66,68,469 08 48 /C Time Trend GS<0 kw & Large Use Blackout Dummy DewPoint Temperature Business Days Percent Toronto Unemployment Rate GS<0 kw Customer Numbers GS 0 999 kw GS 000 4999 kw Large Use
Toronto Hydro Electric System Limited EB 04 06 Appendix A Corrected: 04 Sep Page of 4 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Col. 9 Col. 0 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Col. 9 Col. 0 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Month Residential Competitive Sector Multi Unit Residential (CSMUR) Purchased Energy per day, kwh (by customer class) GS<0 kw GS 0 999 kw GS 000 4999 kw Large Use Street lighting Unmetered Scattered Load Residential CSMUR GS<0 kw Cumulative CDM impacts per day, kwh GS 0 999 kw HDD0 per day CDD8 per day Toronto Population ('000) GS 000 4999 kw Large Use Residenti al Time Trend GS<0 kw & Large Use Blackout Dummy DewPoint Temperature Business Days Percent Toronto Unemployment Rate GS<0 kw Customer Numbers 6 Jun 007,0,998,67 7,8,6 9,64,8,74,060 7,6,78 6,6 64,44 6,9,76 4,678,7,48 0.0., 60 60 0. 70.0 8. 66,67,440 7 49 /C 64 Jul 007,78,49,4 7,48,4 9,,90,078,4 6,90,0 4,8 9,8 9,76 7,9 8,400 7, 6,90 0.0.4, 6 6 0 4.0 67.7 9.9 66,486,497 49 /C 6 Aug 007 6,00,4,444 7,64,70 0,48,7 6,09,800 7,86,46 0,774 64,84 07,0,09,,08,46 0.0 4.,0 6 6 0 4. 7.0 8.9 66,86,7 9 49 /C 66 Sep 007,97,98 7,9 6,60,46 7,6,0 4,78,0 7,,9 79,044 9,47 679, 4,0 6,8 4,68,867 0.0.6, 6 6 0.6 6. 7. 66,88,6 9 49 /C 67 Oct 007,400,600,86 6,6,09 6,9, 4,99,77 6,70,69,8 9,80 70,8 6,7 66,0 6,796 60,670 0. 0.6,9 64 64 0 9. 7.0 7.4 66,99,0 8 49 /C 68 Nov 007 4,96,769 6,049 6,76,474 7,98,4 4,4,840 7,07,98 8,67 6,7 90,87 67,47 7,04 67, 66, 7.4 0.0, 6 6 0.4 7. 7.7 66,4,86 9 49 /C 69 Dec 007 6,46,47 7,87 7,09,79 9,0,8 4,44,46 6,99,9 89,8 64,76 609,946 44,07 4,64 44,678 4,04. 0.0, 66 66 0.8 6. 6.6 66,4,90 49 /C 70 Jan 008 6,669,64 7,89 7,09,07 9,946,486 4,8,64 7,0,668 4,876 6,8 66,864 6,798 7,98 6,86 60,4. 0.0, 67 67 0 6. 7.0 7. 66,04,74 7 49 /C 7 Feb 008 6,4,408 9,99 7,9, 0,869,860 4,60,984 7,,4 84,40 64,0 679,04 7,988 84,80 7,4 7,47. 0.0,07 68 68 0 9. 69.0 6. 66,0,86 8 48 /C 7 Mar 008,9,86 8,800 6,860,669 9,07,6 4,7, 7,4,4 8, 6,4 6,66 6,8 7,987 67,49 64,44.7 0.0,0 69 69 0 7.0 64. 7. 66,09,99 9 48 /C 7 Apr 008,4,6 0,489 6,46,98 6,47,06,747,990 6,94, 9,774 9,698,64 86,6 9,4 87,446 8,8.6 0.0,0 70 70 0 0.7 7. 6. 66,,977 9 48 /C 74 May 008,678,640 0,846,9,,0,844,6,9 6,466,08 4,9 7,0,7 8,44 90,7 86,60 8,0 0.4 0.,7 7 7 0.9 67.7 7.7 66,094,06 0 49 /C 7 Jun 008 4,409,89,447 6,9,68 8,4,0 4,9,788 7,67,88,9 6,9 79,880,6 4,69,6, 0.0.4,0 7 7 0.6 70.0 8. 66,,066 0 49 /C 76 Jul 008,46,40 4,09 7,64,768 0,78,8,06,4 7,48,96 48,4 6,898 88,749 88,48 00,67 96,644 9,4 0.0.6,0 7 7 0 4.8 7.0 9. 66,86,06 7 49 /C 77 Aug 008 4,08,60 6,4 6,687,887 8,9,4 4,4, 6,9,7 9,6 6,74 8, 88,8 00,98 97,6 9,84 0.0., 74 74 0.6 64. 8.7 66,6,077 8 49 /C 78 Sep 008,9,6 0,08 6,46,406 7,49,8 4,7, 6,84,760 86,60 6,94 6, 0,674,6,74 06,066 0.0 0.9,7 7 7 0. 70.0 7.6 66,9,0 7 48 /C 79 Oct 008,4,460 0,479,88,99 6,09,808,664,78 6,47, 9,48 6,7 8, 0,076,040,70,448.0 0.0,0 76 76 0.4 7.0 7.4 6,867,09 6 48 /C 80 Nov 008 4,644, 6,76 6,47,070 7,84,677,80,77 6,6,0 86,0 6,06 6,847, 0,487 9,9 6,88 7. 0.0,7 77 77 0. 66.7 7. 66,084,8 7 47 /C 8 Dec 008 6,7,0 40,079 6,67,60 9,,089,49,0 6,4,49 80,80 9,64 08,99,40 6,6 60,0,6. 0.0,6 78 78 0 7. 67.7 6.9 6,97,6 47 /C 8 Jan 009 7,00,97 47,09 7,97,666,67,98 4,4,97 6,660,68 47, 66,009 09,6 7,8 64,86 6,099 8,0 8.8 0.0, 79 79 0.9 67.7 8. 6,700,47 6 47 /C 8 Feb 009 6,8,9 6,7 6,86,980 9,978,0,84,699 6,74, 8,7 69,699 7,44 8 87,07 98,69 97,407 9,70.7 0.0,7 80 80 0 8. 67.9 8.9 66,,8 6 47 /C 84 Mar 009 4,74,877 8, 6,0, 8,464,88,84,44 6,44,460 6,76 6,0 8,04 4 6,0 7,0 7,0 68,90 9. 0.0,9 8 8 0 6.4 7.0 9.4 66,40,89 4 47 /C 8 Apr 009,6,6 6,00 6,0,8 6,4,699,9, 6,9,44 0,0 6, 60,797,484 8,08 40,98 47,994. 0.0,48 8 8 0.4 70.0 8.8 6,846,6 4 47 /C 86 May 009,9, 6,408,70,0,,040,784,6 6,06,66 6,904 8,7 4,87 4 0,44 6,488 9,8 48,9 0.4 0.,4 8 8 0 4.6 64. 0.7 6,798,08 47 /C 87 Jun 009,90,96 6,7 6,04,6 7,7,6,84,97 6,84,04,6 64,4 648,79 4,878 49,44 7,89 80,7 0.0.,9 84 84 0. 7.. 66,074, 47 /C 88 Jul 009,79,78 68,047 6,047,049 7,,,6,667 6,9,78 4,648 8,77 7,9 446 89,60 404,60 4,90 448,807 0.0.4, 8 8 0.4 67.7.6 6,84,87 47 /C 89 Aug 009 4,8,64 69,9 6,49,7 9,687, 4,70,4 6,94,078,87 6,49 74,80 486 97,4 4,789 4,067 464,40 0.0.9,7 86 86 0. 64..6 66,047,9 0 47 /C 90 Sep 009,6,764 77,94,86,4 7,74,47,90,64 6,78,894 8,097,004 7,8 9 4,84 40,48 4,07 490,6 0.0 0.7,7 87 87 0.0 70.0 9.8 66,00,7 0 47 /C 9 Oct 009,8,064 76,76,684,484 6,464,9,,6 6,47,0,09 4,86 8,98 64,86 7,987 8,769 0,77.0 0.0,4 88 88 0 4. 67.7 9. 6,87,6 06 47 /C 9 Nov 009,689,07 8,0,774,87 6,864,60,74,6 6,69,86 88,98,4 9,888 6 7, 79,0 9,0,0 4. 0.0,9 89 89 0.8 70.0 0. 6,8,84 0 47 /C 9 Dec 009 6,87, 8,88 6,7,8 9,49,,407,6 6,74,49 9,44 6,9 60,979 8 7, 68,74 84,8 86,4.4 0.0,9 90 90 0.9 67.7 8.9 6,88,444 09 47 /C 94 Jan 00 6,94,9 97,086 6,,6 0,70,76,76,88 6,46,844 48,96 6,8 666,086 8 46,6 467,47 48,77 484,674. 0.0,4 9 90 0 8.4 64. 9.8 6,607,97 07 47 /C 9 Feb 00 6,,,48 6,498,49 0,698,8,774,06 6,40,897 86,994 60,7 74,876 9,78 6,86 44,97 4,898.4 0.0,8 9 90 0 7. 67.9 9. 66,06,74 47 /C 96 Mar 00,79, 0,46,87,49 7,9,94,8, 6,6,068 7,966 7,6 676,0 877 474,888 486,04 0,64 04,.7 0.0,7 9 90 0.4 74. 9. 66,6,70 0 47 /C 97 Apr 00,97,00,9,49,0 6,40,67,7,,77,68 0,06,09,0 449 4,07 49, 66,0 97,4.0 0.0,7 94 90 0 0.8 70.0 0. 6,99,86 0 47 /C 98 May 00,044,68 7,8,647,99 7,4,46,486,67 6,8,9 4,68 48,88,07 49 4,846 4, 9,89 9,84 0.6., 9 90 0 8.6 64..6 6,68,89 47 /C 99 Jun 00 4,46,44 4,6,88,774 8,6,970 4,4,478 6,49, 6,789 46,96 84,69,9 60,660 60,6 689,06 7,990 0.0.0,4 96 90 0.9 7. 0. 6,799,87 09 47 /C 00 Jul 00 7,98,7 47,77 6,77,9,48,969,8,78 6,870,06,09 46,894 80,996,8 6,47 640,680 678,87 746,9 0.0.,6 97 90 0 7.4 67.7 0. 66,09,906 09 46 /C 0 Aug 00 6,,76,7 6,48,48,6,847,077,0 6,774,897 0,6 4,69 74,, 68,477 68,6 697,46 768,694 0.0 4.,6 98 90 0 7. 67.7.0 6,89,96 07 46 /C 0 Sep 00,947,969 70,469,8,908 7,7,07,748,09 6,80,96 87,4 9,740 7,7,9 699,444 70,40 76,840 88, 0.0.,8 99 90 0. 70.0 9.9 6,794,978 06 46 /C 0 Oct 00,96,66 68,4,4,0 6,9,874,08,06,8,4,76 4,068 9,00 9 79,066 88,4 07, 47,9. 0.0,7 00 90 0.4 64. 9.4 66,04,980 0 46 /C 04 Nov 00,777,80 78,64,64,86 7,907,96,40,6 6,,6 88,90 40,79 0,66 608 98,40 08,08 8,97 7,4.6 0.0,6 0 90 0 0.4 7. 9. 6,976,0 04 46 /C 0 Dec 00 6,8,84 77,87 6,0,64 0,78,06,,897 6,47,64 9,78 9,0 6,6,48 60,9 6,9 69,8 644,49.8 0.0,48 0 90 0 7. 67.7 8. 66,67,68 00 0 /C 06 Jan 0 6,87,9 80,664 6,8,7,48,76,49,704 6,8,88 48,4 9,86 64,99,8 6,989 67,879 64,96 64,0 7.0 0.0,48 0 90 0 0. 64. 9.4 6,996,66 498 0 /C 07 Feb 0 6,8,099 0,79 6,4,7,4,4,664,0 6,646,4 88,767 7,6 68,,4 68,68 700,749 7,64 74,0.4 0.0,4 04 90 0 9. 67.9 8. 6,94,4 498 0 /C 08 Mar 0 4,77,06 87,88 6,04,67 9,69,8,79,690 6,90,80 60,48,860 6,,444 66,66 66,08 64,640 6,80 0. 0.0,4 0 90 0. 74. 9.6 6,94,46 0 0 /C 09 Apr 0,8,90 04,97,87,6 7,99,96,7,894 6,4,44 0,774,469,98 78 7,67 40,868 4,0 68,099.6 0.0, 06 90 0 0.9 66.7 9. 6,86,98 0 0 /C 0 May 0,89,0,,64,6 6,60,896,88,790 6,6,894 4, 8,094 07,708 84,687 4,90,847 6, 0. 0.4,9 07 90 0 9. 67.7 0.0 66,4,79 0 0 /C Jun 0,74,99,0,76,09 8,7,9,60,7 7,006,47 4,769 7,69 79,8,46 80,7 90,400 868,40 894,7 0.0.7,9 08 90 0. 7. 9. 66,68,84 0 0 /C Jul 0 8,,99 0,700 6,6,88,49,98 4,469,94 7,9,67,86 7,864 787,74,78 84,09 9,0 87,40 87,49 0.0 6.4,48 09 90 0.8 64. 0.4 66,7,84 0 0 /C Aug 0,,86 4,69 6,86,48,,790 4,09,498 7,04,4 46,9 8,097 786,89,644 89, 94,07 89,9 86,44 0.0.9,67 0 90 0. 7.0 0. 66,900,84 499 0 /C 4 Sep 0,6,84 8,7,67, 8,408,0,,97 6,77,6 8,0,40 86,4,8 84,786 99,498 89,886 889,806 0.0.,60 90 0.0 70.0 8. 67,07,79 498 /C Oct 0,6,97 60,9,407,98 6,76,9,6,6 6,68, 4,0 6,46,0,86 4,87 40,00 7,690,64.6 0.,49 90 0 6.7 64. 8.7 67,00,70 49 /C 6 Nov 0,40,48 7,87,766,400 7,74,0,8,0,646,80 90,8 7,47 46,07,47 70,74 449,7 84,06 7,76.9 0.0,68 90 0.4 7. 8.7 67,7,6 496 /C 7 Dec 0,08, 7,074,969,677 9,087,4,676,968,78,7 40,9 9,78 74,6,440 79,868,006,067 86,48 777,094 9. 0.0,60 4 90 0.7 64. 8. 67,6,87 498 /C 8 Jan 0,8,9 79,0 6,08,77 9,70,4,88,49 6,44,99 4,6,978 7,84,474 79,,04,06 87,4 777,49.7 0.0,6 90 0.4 67.7 0. 67,460,7 497 /C 9 Feb 0 4,796,840 07,9 6,,97 9,67,0,89,46 6,,4 9,70,97 77,80,7 8,47,09,8 874,04 80,77 0. 0.0,6 6 90 0 4.7 69.0 8.7 67,6,9 498 /C 0 Mar 0,96,994 9,66,84,849 7,4,0,798, 6,40,8 60, 7,866 78,7,6 808,47,049,04 8,08 777,70 4.7 0.0,6 7 90 0 0. 7.0 8.6 67,8, 498 /C Apr 0,99,84,40,68,89 6,8,488,099,009 6,,660,08 7,76 6,684,86 40,770 9, 40,6 88,889.4 0.0,60 8 90 0. 66.7 9.9 67,8,07 497 /C May 0,7,97 04,440,9,8 6,86,,867,064 6,08,090 8,09,8,8,88 96,089,6 98,8 76,44 0.0.,68 9 90 0 8.7 7.0 0. 67,06,6 497 /C Jun 0,7, 8,78 6,64,748 9,007,9 4,7,6 6,79,77 6,99,4 898,476 4,697,00,064,9,796,009,678 9,6 0.0.4,64 0 90 0. 70.0 0.0 67,40,9 496 /C GS 0 999 kw GS 000 4999 kw Large Use
Toronto Hydro Electric System Limited EB 04 06 Appendix A Corrected: 04 Sep Page of 4 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Col. 9 Col. 0 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Col. 9 Col. 0 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Month Residential Competitive Sector Multi Unit Residential (CSMUR) Purchased Energy per day, kwh (by customer class) GS<0 kw GS 0 999 kw GS 000 4999 kw Large Use Street lighting Unmetered Scattered Load Residential CSMUR GS<0 kw Cumulative CDM impacts per day, kwh GS 0 999 kw HDD0 per day CDD8 per day Toronto Population ('000) GS 000 4999 kw Large Use Residenti al Time Trend GS<0 kw & Large Use Blackout Dummy DewPoint Temperature Business Days Percent Toronto Unemployment Rate GS<0 kw Customer Numbers 4 Jul 0 8,,906,904 7,06,09,40,688,049,4 7,,9,9 0,79 866,9 4,87 978,8,0, 97,9 97,707 0.0 6.4,7 90 0. 67.7.0 67,40,9 496 /C Aug 0,69,0,67 6,0,4 9,99,809 4,7,846 7,9,8 0,899,9 89,7 4,69 967,94,98,007 96, 90,4 0.0.6,6 90 0. 7.0.0 67,,7 49 /C 6 Sep 0,969,8 7,66,770,9 7,48,48,684,97 6,7,0 87,76 6,49 88,6 4,8 884,8,,90 87,649 86, 0.0.,64 90 0 0.7 6. 9.6 67,66,8 49 /C 7 Oct 0,90,6 0,06,4,890,96,4,49,47 6,4, 4,,648 48,70,9,9 49,09 46,060,08.6 0.0, 4 90 0 6. 7.0 9. 67,90,84 494 /C 8 Nov 0,74,74 0,4,98,46 7,6,966,,88 6,4,8 87,9 8,44 6,786,040 64,,94 6,08 0,87 6.6 0.0,0 90 0.4 7. 8. 67,986,0 497 /C 9 Dec 0 4,899,0 47,76 6,068,480 7,9,8,896,4 6,4,9 90,7,694 78,60 4,406 76,4,0,80 70,00 69,984 8.4 0.0, 6 90 0.7 6. 8.4 67,970, 04 /C 0 Jan 0,740,7,07 6,4,4 9,40,70,84,080 6,90,78 47,00 7,907 740,84 4,447 760,74,,99 7,8 660,49. 0.0, 7 90 0 6.0 67.7 9. 67,994,9 08 /C Feb 0,668,88 400,7 6,4,87 0,8,0,99,909 6,4,7 88,4 6,68 8,7 4,984 849,97,4,76 80,04 7, 4.6 0.0,4 8 90 0 7.7 67.9 8.8 68,08,6 07 /C Mar 0 4,4, 76,76 6,044, 7,74,888,68,7 6,0,79 66,0 8,9 744,906 4,4 77,66,4,79 77,748 66,4 9.9 0.0,4 9 90 0. 67.7 8. 68,09,06 0 /C Apr 0,76,88 9,87,79,4 6,9,98,8,0 6,9,0 09,00,86 7,9,9 88,97 80,744 64,8,9 4. 0.0,7 0 90 0 0. 66.7 8.6 68,06,99 /C 4 May 0,977,698 87,96,87,47,6,9,46,77 6,4,76 4,699 0,09 6,6,0 79,9 70,89 4,788,46 0. 0.7,44 90 0 7.6 7.0 8.7 68,7,074 /C Jun 0,489,99 404,74,866,940 7,80,7 4,0, 6,,60 8,0,9 9,66,79 97,98,474,96 904,666 89,47 0.0.0,7 90 0.9 70.0 8. 68,,88 6 /C 6 Jul 0 6,08,8 98,0 6,84,8 0,9,698 4,96,6 6,6,00,4,94 90,849,678 9,7,4,4 877,6 79,9 0.0 4.4,4 90 0 6. 67.7 9. 68,40,94 6 /C 7 Aug 0 4,0,87 404,870 6,4,4 8,8,74 4,664,08 6,04,8,48,767 899,090,7 960,69,474,69 88,0 794,99 0.0.0,4 4 90 0 4. 7.0 8. 68,48,9 7 /C 8 Sep 0,0,489 4,,78,7 6,690,786,99,6 6,88,7 90,79,04 90, 6,0,000,97,46,84 96,74 8,66 0.0 0.9,48 90 0. 6. 8.7 68,66,9 7 /C 9 Oct 0,04, 4,7,4,6,80,76,76,7 6,99,4 4,67,46 68,8,40 99,7 6,69 6,89 6,.6 0.0,46 6 90 0 6. 7.0 9.4 68,66,890 9 /C 40 Nov 0,99,799 47,88 6,7,788 7,6,69,74,9 6,9,8 9, 6,97 8,86,84 44, 67,7 8,88 40,0 8. 0.0,66 7 90 0.6 7. 8. 68,69,904 /C 4 Dec 0,789,4 47,804 6,449,09 8,88,788,46,66 6,6,7 9,98 0,490 779,66,0 866,67,84,7 77,4 68,688 4. 0.0,67 8 90 0 7.7 6. 9. 68,70,94 /C 4 Jan 04 776,,0 869,677,9,746 774,00 68,6 4.6 0.0,68 9 90 0 8. 7.0 9.0 68,78,9 0 /C 4 Feb 04 860,89,9 968,766,6,79 860,8 79,6 4. 0.0,69 40 90 0 8. 67.9 9.0 68,68,9 6 /C 44 Mar 04 78,677,480 88,98,4,94 784,06 690, 9.0 0.0,70 4 90 0 4.6 67.7 8.9 68,7,9 6 /C 4 Apr 04 90,647,77 44,77 7,86 9,47 46,64. 0.0,7 4 90 0 0.0 66.7 8.9 68,799,98 49 48 /C 46 May 04 79,809,7 46, 7,6 8,99 7,47 0.4 0.6,7 4 90 0 6.8 67.7 8.9 68,84,969 49 48 /C 47 Jun 04 967,9 7,069,,8,84,96 98,6 860,89 0.0.4,7 44 90 0.8 70.0 8.8 68,89,97 49 48 /C 48 Jul 04 97,7 6,90,09,044,804, 949,77 89,9 0.0 4.,74 4 90 0.6 7.0 8.8 68,97,944 49 48 /C 49 Aug 04 9,9 6,97,098,7,8,80 94,47 8,68 0.0.4,7 46 90 0.0 64. 8.7 68,98,9 49 48 /C 0 Sep 04 9,47 7,77,4,7,90,760 99, 86,497 0.0.,76 47 90 0.0 70.0 8.7 69,09,90 440 48 /C Oct 04 77,,96 4,6 76,64 94,066 4,87. 0.,77 48 90 0 6.0 7.0 8.6 69,040,9 440 48 /C Nov 04 94,44,9 48,949 8,07 44, 8,90.8 0.0,77 49 90 0 0. 66.7 8.6 69,0,90 440 48 /C Dec 04 80, 6,89,00,7,7,49 8,90 7,.8 0.0,78 0 90 0.4 67.7 8. 69,06,96 440 48 /C 4 Jan 0 79,9 6, 990,,708,9 88,66 77,86 4.6 0.0,79 90 0 8. 67.7 8.4 69,074,980 440 48 /C Feb 0 878,667 7,8,00,90,904,84 90,87 79,8 4. 0.0,80 90 0 8. 67.9 8. 69,08,99 440 48 /C 6 Mar 0 796,04 6,680,00,77,74,76 846,8 7,08 9.0 0.0,8 90 0 4.6 7.0 8. 69,097,00 440 48 /C 7 Apr 0 97,648,70 0,747 879,99 4,77 6,9. 0.0,8 4 90 0 0.0 66.7 8. 69,08,04 440 49 /C 8 May 0 86,47, 49,9 864,074 4,07,48 0.4 0.6,8 90 0 6.8 64. 8. 69,9,09 440 49 /C 9 Jun 0 979,7 8,4,7,8,,76,0,40 89,7 0.0.4,84 6 90 0.8 7. 8. 69,,04 440 49 /C 60 Jul 0 940, 8,9,6,07,68,09,008,97 8,67 0.0 4.,8 7 90 0.6 7.0 8. 69,4,069 44 49 /C 6 Aug 0 96,48 8,9,0,68,8,60,0,094 8,7 0.0.4,86 8 90 0.0 64. 8. 69,,084 44 49 /C 6 Sep 0 89,9 8,78,6,8,70,7,04,868 877,788 0.0.,87 9 90 0.0 70.0 8.0 69,64,099 44 49 /C 6 Oct 0 4,97,486 0,780 907,89 44, 48,09. 0.,87 60 90 0 6.0 67.7 8.0 69,76,4 44 49 /C 64 Nov 0 69,00,707 9,49 96,788 44,79 6,099.8 0.0,88 6 90 0 0. 70.0 8.0 69,87,9 44 49 /C 6 Dec 0 7,64 7,78,09,00,08,406 89,96 79,460.8 0.0,89 6 90 0.4 67.7 7.9 69,98,44 44 49 /C 66 Jan 06 749,7 7,80,079,896,00,940 874,90 7,06 4.6 0.0,90 6 90 0 8. 64. 7.9 69,0,9 44 49 /C 67 Feb 06 80,6 8,44,9,67,64,90 97,6 77,777 4. 0.0,9 64 90 0 8. 69.0 7.9 69,,74 44 49 /C 68 Mar 06 76,6 8,009,096,968,0,48 88,644 7, 9.0 0.0,9 6 90 0 4.6 67.7 7.9 69,,89 44 49 /C 69 Apr 06 78,4 4,040,4,06,67 44, 64,6. 0.0,9 66 90 0 0.0 70.0 7.9 69,4,0 44 49 /C 70 May 06 68,7,967 9,96,08,6 4,0 4,804 0.4 0.6,94 67 90 0 6.8 67.7 7.9 69,,8 44 49 /C 7 Jun 06 99,79 0,,8,744,60,74,0,47 899,670 0.0.4,9 68 90 0.8 7. 7.9 69,66, 44 49 /C 7 Jul 06 906,906 0,0,4,76,60,90,06,7 86,0 0.0 4.,96 69 90 0.6 64. 7.8 69,77,48 44 49 /C 7 Aug 06 900,70 0,0,47,448,78,,06,98 86,8 0.0.4,97 70 90 0.0 7.0 7.8 69,89,6 44 49 /C 74 Sep 06 9,96 0,0,94,70,680,678,098,894 890,6 0.0.,98 7 90 0.0 70.0 7.8 69,00,78 44 49 /C 7 Oct 06 66,0 4,07,68,07,4 46,696,46. 0.,98 7 90 0 6.0 64. 7.8 69,,9 44 49 /C 76 Nov 06 8,70 4,469 86,7,7,788 48,9 69,07.8 0.0,99 7 90 0 0. 7. 7.7 69,,08 44 49 /C 77 Dec 06 786,09 9,40,9,849,84,67 94,79 7,44.8 0.0,00 74 90 0.4 64. 7.7 69,4, 44 49 /C 78 Jan 07 786,664 9,99,8,00,9,907 99,4 746,74 4.6 0.0,0 7 90 0 8. 67.7 7.7 69,4,8 44 49 /C 79 Feb 07 87,7 0,48,,076,666,0,04,894 87,86 4. 0.0,0 76 90 0 8. 67.9 7.6 69,6, 44 49 /C 80 Mar 07 794,9 9,600,9,0,44,74 90,770 7,90 9.0 0.0,0 77 90 0 4.6 74. 7.6 69,68,67 44 49 /C 8 Apr 07 97,77 4,87 6,89,,07 476,947 78,479. 0.0,04 78 90 0 0.0 60.0 7.6 69,79,8 44 49 /C 8 May 07 87,0 4,74 609,76,0,6 46,44 68,498 0.4 0.6,0 79 90 0 6.8 7.0 7.6 69,90,97 44 0 /C 8 Jun 07 988,88,04,66,044,0,40,9,0 99,4 0.0.4,06 80 90 0.8 7. 7.6 69,40,4 44 0 /C 84 Jul 07 94,884,9,499,9,07,49,7,69 880,477 0.0 4.,07 8 90 0.6 64. 7. 69,4,47 44 0 /C GS 0 999 kw GS 000 4999 kw Large Use
Toronto Hydro Electric System Limited EB 04 06 Appendix A Corrected: 04 Sep Page 4 of 4 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Col. 9 Col. 0 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Col. 9 Col. 0 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Month Residential Competitive Sector Multi Unit Residential (CSMUR) Purchased Energy per day, kwh (by customer class) GS<0 kw GS 0 999 kw GS 000 4999 kw Large Use Street lighting Unmetered Scattered Load Residential CSMUR GS<0 kw Cumulative CDM impacts per day, kwh GS 0 999 kw HDD0 per day CDD8 per day Toronto Population ('000) GS 000 4999 kw Large Use Residenti al Time Trend GS<0 kw & Large Use Blackout Dummy DewPoint Temperature Business Days Percent Toronto Unemployment Rate GS<0 kw Customer Numbers 8 Aug 07 947,984,00,07,44,049,40,0,4 88,47 0.0.4,08 8 90 0.0 7.0 7. 69,44,44 44 0 /C 86 Sep 07 974,868,06,66,77,7,908,7,6 9, 0.0.,08 8 90 0.0 66.7 7. 69,4,47 44 0 /C 87 Oct 07 87,006 4,99 64,8,68,007 466, 6,60. 0.,09 84 90 0 6.0 67.7 7. 69,447,47 44 0 /C 88 Nov 07 40,86,9 66,84,48,6 49,6 80,866.8 0.0,0 8 90 0 0. 7. 7. 69,48,487 44 0 /C 89 Dec 07 80,,06,76,8,8,6,04,8 777,.8 0.0, 86 90 0.4 6. 7. 69,469,0 44 0 /C 90 Jan 08 8,84,07,7,88,89,9,009,69 77,467 4.6 0.0, 87 90 0 8. 7.0 7. 69,48,7 44 0 /C 9 Feb 08 94,0,8,6,840,49,0,0,84 8,496 4. 0.0, 88 90 0 8. 67.9 7. 69,49, 444 0 /C 9 Mar 08 84,749,88,9,66,878,788,00,8 777,900 9.0 0.0,4 89 90 0 4.6 67.7 7. 69,0,46 444 0 /C 9 Apr 08 4,0,68 699,74,448,668,0 9,67. 0.0, 90 90 0 0.0 66.7 7. 69,4,6 444 0 /C 94 May 08 4,60,67 68,07,48,49 499,86 8,0 0.4 0.6,6 9 90 0 6.8 7.0 7. 69,6,76 444 0 /C 9 Jun 08,0,6 4,,748,98,64,76,77, 97,69 0.0.4,7 9 90 0.8 70.0 7. 69,7,9 444 0 /C 96 Jul 08,0,680 4,00,69,66,46,,0, 9,4 0.0 4.,8 9 90 0.6 67.7 7. 69,48,606 444 0 /C 97 Aug 08,07,677 4,096,697,89,66,76,,469 9,49 0.0.4,8 94 90 0.0 7.0 7. 69,60,6 444 0 /C 98 Sep 08,06,04 4,67,76,8,707,68,77,844 964,468 0.0.,9 9 90 0.0 6. 7. 69,7,66 444 0 /C 99 Oct 08 4,47,80 700,4,477,4 07,806 8,0. 0.,0 96 90 0 6.0 7.0 7. 69,8,6 444 0 /C 00 Nov 08 44,94 6,86 77,,6,04,489 40,40.8 0.0, 97 90 0 0. 7. 7. 69,9,666 444 0 /C 0 Dec 08 90,7,86,,40,40,984,096,88 88,089.8 0.0, 98 90 0.4 6. 7. 69,60,68 44 0 /C 0 Jan 09 88,47,9,00,968,7,740,074,60 794,86 4.6 0.0, 99 90 0 8. 7.0 7. 69,66,696 44 0 /C 0 Feb 09 97,89 4,6,666,0,86,9,9,0 880,9 4. 0.0,4 00 90 0 8. 67.9 7.4 69,67,70 44 0 /C 04 Mar 09 884,80,06,,766,68,4,08,49 797,70 9.0 0.0, 0 90 0 4.6 67.7 7.4 69,69,7 44 0 /C 0 Apr 09 44,84 6,8 79,804,640,74 4,40 400,49. 0.0,6 0 90 0 0.0 66.7 7.4 69,60,740 44 0 /C 06 May 09 49,886 6,44 74,0,60,90 7,0 89,67 0.4 0.6,7 0 90 0 6.8 7.0 7.4 69,66,7 44 0 /C 07 Jun 09,087,04 6,49,888,4 4,094,70,8,880 984,96 0.0.4,8 04 90 0.8 66.7 7. 69,67,770 44 /C 08 Jul 09,09,66 6,074,8,74,986,770,94,670 949,90 0.0 4.,9 0 90 0.6 7.0 7. 69,684,78 44 /C 09 Aug 09,048,40 6,7,87,067 4,00,76,94,87 947,64 0.0.4,9 06 90 0.0 67.7 7. 69,69,800 44 /C 0 Sep 09,07,694 6,79,897,60 4,47,99,,4 97,8 0.0.,0 07 90 0.0 66.7 7. 69,706,8 44 /C Oct 09 44,4 6,680 7,0,648,74 8,79 8,600. 0., 08 90 0 6.0 7.0 7. 69,78,80 44 /C Nov 09 44,48 7,0 789,0,7,,08 40,07.8 0.0, 09 90 0 0. 70.0 7. 69,79,84 446 /C Dec 09 900,76 4,8,6,66,67,46,9, 84,6.8 0.0, 0 90 0.4 64. 7. 69,740,860 446 /C GS 0 999 kw GS 000 4999 kw Large Use
EB-04-06 Appendix A- Corrected: 04 Sep Page of Residential Model Dependent Variable: RES_DAY Method: Least Squares Date: 08//4 Time: 0:4 Sample: 00M07 0M Included observations: 8 White Heteroskedasticity Consistent Standard Errors & Covariance Variable Coefficient Std. Error t Statistic Prob. HDD0_DAY 0,97 7,880 8.40 0.000 CDD8_DAY 988, 8,08.04 0.000 POP,06,66.6 0.48 TREND_JUL00 8,776,40.6 0.000 BLACKOUT,7,8 07,94.7 0.000 C 6,8,46,47,7.6 0. /C R squared 9.9% Mean dependent var,9,740.6 Adjusted R squared 9.7% S.D. dependent var,7,8.6 S.E. of regression 44,6.9 Akaike info criterion 8.84 Sum squared resid 4,904,9,06,89 Schwarz criterion 8.97 Log likelihood,984. Hannan Quinn criter. 8.90 F statistic 409. Durbin Watson stat.6 Prob(F statistic) 0.0000
EB-04-06 Appendix A- Corrected: 04 Sep Page of GS<0 kw Model Dependent Variable: LESS0_DAY Method: Least Squares Date: 08//4 Time: 0:9 Sample: 00M07 0M Included observations: 8 White Heteroskedasticity Consistent Standard Errors & Covariance Variable Coefficient Std. Error t Statistic Prob. HDD0_DAY,06 7,478. 0.000 CDD8_DAY 68,64 9,496.78 0.000 DEW 9,000 6,7 4. 0.000 TREND_JUL00_ 8,89 47 8.7 0.000 UNEMPL_RATE,4,98.6 0.0 CUST_NUMBERS 7.8 0.000 BLACKOUT 40,9,79. 0.000 C,77,0,478,08.7 0.4 /C R squared 9.4% Mean dependent var 6,90,66. Adjusted R squared 9.0% S.D. dependent var 66,0.94 S.E. of regression 6,6.8 Akaike info criterion 6.89 Sum squared resid,47,708,67,740 Schwarz criterion 7.06 Log likelihood,847.6 Hannan Quinn criter. 6.96 F statistic 6. Durbin Watson stat.9 Prob(F statistic) 0.0000
EB-04-06 Appendix A- Corrected: 04 Sep Page of GS 0 999 kw Model Dependent Variable: GS0_DAY Method: Least Squares Date: 08//4 Time: 0:44 Sample: 00M07 0M Included observations: 8 White Heteroskedasticity Consistent Standard Errors & Covariance Variable Coefficient Std. Error t Statistic Prob. HDD0_DAY 44,77,88 0.8 0.000 CDD8_DAY 948,400 4,76.8 0.000 BUS_DAYS_PERCENT,40,0.78 0.006 DEW,744 8,98 6.49 0.000 CUST_NUMBERS 969 8 6.79 0.000 UNEMPL_RATE 68,8 6,76.86 0.06 BLACKOUT,878,77 4,97.04 0.000 C,99,6,00,7.90 0.000 /C R squared 9.% Mean dependent var 8,646,86.8 Adjusted R squared 9.% S.D. dependent var,979,44.79 S.E. of regression 4,66.8 Akaike info criterion 8.84 Sum squared resid 4,,60,66,84 Schwarz criterion 9.0 Log likelihood,98. Hannan Quinn criter. 8.9 F statistic 9.0 Durbin Watson stat.0 Prob(F statistic) 0.0000
EB-04-06 Appendix A- Corrected: 04 Sep Page 4 of GS 000 4999 kw Model Dependent Variable: GS40_DAY Method: Least Squares Date: 08//4 Time: 0: Sample: 00M07 0M Included observations: 8 White Heteroskedasticity Consistent Standard Errors & Covariance Variable Coefficient Std. Error t Statistic Prob. HDD0_DAY 87,90 4,7.77 0.000 CDD8_DAY 0,9,048 8.7 0.000 BUS_DAYS_PERCENT 4,76 8,99 6.9 0.000 DEW 7,789,9 9. 0.000 CUST_NUMBERS 0,08,46 4. 0.000 UNEMPL_RATE 4,0 9,08 7.44 0.000 BLACKOUT 886,8 8,964 0.68 0.000 C,0,66,,84.7 0.000 /C R squared 87.7% Mean dependent var 4,4,799.09 Adjusted R squared 87.0% S.D. dependent var 8,86. S.E. of regression 00,96. Akaike info criterion 8. Sum squared resid,77,7,487,66 Schwarz criterion 8.9 Log likelihood,9. Hannan Quinn criter. 8.9 F statistic.4 Durbin Watson stat.6 Prob(F statistic) 0.0000
EB-04-06 Appendix A- Corrected: 04 Sep Page of Large Use Model Dependent Variable: LU_DAY Method: Least Squares Date: 08//4 Time: 0: Sample: 00M07 0M Included observations: 8 White Heteroskedasticity Consistent Standard Errors & Covariance Variable Coefficient Std. Error t Statistic Prob. HDD0_DAY 8,49 0,90 7.8 0.000 CDD8_DAY 4,786,967.4 0.000 BUS_DAYS_PERCENT 9,744 7,0.8 0.006 DEW,94 9,48. 0.000 CUST 40,4,06.06 0.00 TREND_JUL00_ 6,60 7 9.09 0.000 BLACKOUT 47,6 67,68 6.99 0.000 C,488,89 84,06 4. 0.000 /C R squared 7.% Mean dependent var 7,70,9.4 Adjusted R squared 74.% S.D. dependent var 478,7.0 S.E. of regression 4,87.8 Akaike info criterion 7.69 Sum squared resid 7,666,4,47,9 Schwarz criterion 7.86 Log likelihood,90.9 Hannan Quinn criter. 7.76 F statistic 7. Durbin Watson stat.8 Prob(F statistic) 0.0000
Table : Loads by Class Toronto Hydro Electric System Limited EB 04 06 Appendix B Corrected: 04 Sep Page of Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Col. 9 Col. 0 Col. Col. Col. Col. 4 Col. Col. 6 Col. 7 Col. 8 Col. 9 009 Board 00 Board 0 Board 0 Board 0 Board 04 Board 04 Bridge 009 Actual 00 Actual 0 Actual 0 Actual 0 Actual Year 0 Test Year 06 Test Year 07 Test Year 08 Test Year 09 Test Year Residential kwh,87,07,866,0,8,46,08,08,66,06,86,7 4,986,768,67,7,679,4 n/a,4,7,96 n/a,007,496,09 n/a 4,9,89, 4,909,898,4 4,90,60,49 4,8,68, 4,807,77,78 4,774,97,60 /C 4 kva n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a /C Competitive Sector Multi Unit Residential (CSMUR) kwh n/a n/a n/a n/a n/a n/a n/a n/a n/a 8,,909 n/a 7,86,499,6,8,6,09 89,87,9 4,960,77 6,94,76 /C 6 kva n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a /C 7 GS <0 kw kwh,4,94,999,80,476,74,9,476,0,09,766,048,9,8,076,08,497,86 n/a,4,68,049 n/a,7,,48 n/a,4,640,,8,40,6,0,996,0,08,84,4,06,60,06,986,96, /C 8 kva n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a /C 9 GS 0 999 kw kwh 9,786,6,4 9,844,680,70 0,4,40, 0,9,,07 0,6,74, 0,7,86,06 n/a 9,978,9,876 n/a 9,84,8,68 n/a 9,8,4,00 9,848,64,894 9,88,7,4 9,74,9,849 9,67,97,970 9,66,00, /C 0 kva,06,77,674,6 6,,77 6,77,77 6,9,9 6,844,7 n/a 6,409,77 n/a,980,090 n/a 6,74,6 6,9,86 6,4,4 6,,008 6,8,9 6,40,6 /C GS 000 4999 kw kwh,040,0,4 4,786,9,8 4,880,64,7 4,89,7,46 4,66,98,6 4,670,66,68 n/a 4,794,68,898 n/a 4,90,7,69 n/a 4,68,9,494 4,64,,7 4,669,074,09 4,6,40,9 4,640,88,6 4,64,979,494 /C kva,6,464 0,9,09,4,88 0,974,687 0,87,9 0,6,988 n/a 0,88,66 n/a,00,609 n/a 0,744,06 0,67,87 0,77,086 0,688,40 0,69,77 0,690,4 /C Large Use kwh,70,84,9,4,90,9,78,,,6,689,66,76,778,,40,746,74 n/a,67,07,6 n/a,7,06,90 n/a,46,880,,8,86,74,4,7,907,9,64,449,,0,0,9,60,68 /C 4 kva,60,90,8,884 4,974,40,68,0 4,99,7,44,844 n/a,67,06 n/a,40,906 n/a,70,8,0,00,0,68,8,8,79,8,7, /C Street Lighting kwh 09,74,97,00,096 09,98,944,749,99 0,6,06,04,004 n/a,94,97 n/a,64,8 n/a,90,87 4,09,99 4,69,00 4,47, 4,66,8 4,8,49 /C 6 kva 7,6,00,8,99,0,48 n/a,74 n/a,0 n/a,96 4,479 4,984,489,998 6,0 /C 7 Unmetered Scattered Load kwh 7,40,00 6,4,96,4,0,07,4 6,,8 4,79,00 n/a 4,4,7 n/a 4,,4 n/a 4,,4 4,,4 4,4,04 4,,4 4,,4 4,,4 /C 8 kva n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a /C 9 Total kwh,496,888,777 4,49,79,089 4,86,,48 4,7,66,664 4,4,64,088 4,70,4,48 n/a 4,64,9,88 n/a 4,44,04,046 n/a 4,,77,6 4,8,79, 4,6,6,79,98,09,60,84,9,00,760,6,68 /C 0 kva 4,67,68 4,06,408 4,949, 4,8,76 4,88,067 4,,08 n/a 4,84,90 n/a 4,80,809 n/a 4,7,69 4,697,06 4,806,84 4,6,8 4,84,47 4,9, /C Notes. Loads are after losses 4. CSMUR rate class implementation date Jun 0, 0. Prior years were included in Residential class.