Fleming-Mason Energy Cooperative 2006 Load Forecast Prepared by: East Kentucky Power Cooperative, Inc. Forecasting and Market Analysis July 2006

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3 Table of Contents Introduction and Executive Summary 5 Narrative16 Key Assumptions22 Methodology and Results30 –Residential Forecast35 –Seasonal40 –Public Buildings42 –Small Commercial44 –Large Commercial46 –Other Forecast48 –Peak Day Weather Scenarios51 RUS Form Page Number

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5 Introduction Executive Summary Fleming-Mason Energy Cooperative (Fleming-Mason Energy) located in Flemingsburg, Kentucky, is an electric distribution cooperative that serves members in eight counties. This load forecast report contains Fleming-Mason Energy’s long-range forecast of energy and peak demand. Fleming-Mason Energy and its power supplier, East Kentucky Power Cooperative (EKPC), worked jointly to prepare the load forecast. Factors considered in preparing the forecast include the national and local economy, population and housing trends, service area industrial development, electric price, household income, weather, and appliance efficiency changes. EKPC prepared a preliminary load forecast, which was reviewed by Fleming-Mason Energy for reasonability. Final projections reflect a rigorous analysis of historical data combined with the experience and judgment of the manager and staff of Fleming-Mason Energy. Key assumptions are reported beginning on page 22.

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7 Executive Summary (continued) The load forecast is prepared biannually as part of the overall planning cycle at EKPC and Fleming-Mason Energy. Cooperation helps to ensure that the forecast meets both parties’ needs. Fleming- Mason Energy uses the forecast in developing two-year work plans, long-range work plans, and financial forecasts. EKPC uses the forecast in areas of marketing analysis, transmission planning, generation planning, demand-side planning, and financial forecasting. The complete load forecast for Fleming-Mason Energy is reported in Table 1-1. Residential and commercial sales, total purchases, winter and summer peak demands, and load factor are presented for the years 1990 through 2025.

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11 Executive Summary (continued) Overall Results Total sales are projected to grow by 2.3 percent a year for the period , compared to 2.1 percent which was projected in the 2004 load forecast for the period Results shown in Table 1-2 and Figure 1-1. Winter and summer peak demands for the same period indicate annual growth of 2.7 and 2.2 percent, respectively. Annual peaks shown in Figure 1-2. Load factor decreases slightly to approximately 57% for the forecast period. See Figure 1-3.

12 Executive Summary Overall Results (continued)

13 Figure 1-1

14 Figure 1-2 Peak Demand Forecast Winter and Summer

15 Figure 1-3 Annual System Load Factor

16 Narrative Territory The service area of Fleming-Mason Energy is located in the northeastern Kentucky counties of Bath, Bracken, Fleming, Lewis, Mason, Nicholas, Robertson, and Rowan. The electric service boundaries dividing these counties were established by the Kentucky Public Service Commission several years ago. This is a primarily agricultural area with the major interest in tobacco, dairy, and beef cattle. The wooded eastern section has a small sawmill industry. The three largest communities are Maysville (population 10,500), Morehead (population 5,900) and Flemingsburg (population 3,100). All three are in the electrical service territory of Kentucky Utilities, as are the county seat communities of the other counties except Vanceburg in Lewis County, which has a municipal power system.

17 Narrative (continued) Counties Served Fleming-Mason Energy provides service to members in 8 counties. Figure 1-4

18 Narrative (continued) Local Economy and Customer Growth The service area has developed slowly and trying to predict the future identifies some of the causes. The farming economy has been flat for the past several years, but there are indicators which may allow for some optimism in the beef cattle and dairy areas, but tobacco faces critical consequences from congressional and health related groups. Industrial development has been increasing along the AA Highway corridor (Mason County primarily, but potential in Lewis County exists as well). Inland Container Corporation is operating with more than 50 MW of combined steam and electric load and may expand in the not too distant future. Dravo Lime may increase its 15 MW usage because of increased demand for scrubber lime, as a result of the Clean Air Act of 1992.

19 Narrative (continued) Local Economy and Customer Growth The Mason County industrial park has continued to expand. Recently, Mitsubishi and Green Tokai completed expansion projects and the industrial authority is actively seeking additional companies to locate in this area. The Morehead area, especially the Highway 801 corridor, has significant growth potential. We feel that economic growth, both short-term and long-term, in this particular area, may be significant. There may be greater natural gas availability in the Morehead, Flemingsburg, and Maysville areas. This could have a negative effect if use becomes widespread.

20 Narrative (continued) Local Economy and Customer Growth Other potential projects that may have an impact on future load growth include the completion of Highway 11 that links Flemingsburg and Mount Sterling. The new road will open up potential housing development in Bath County and southern Fleming County. Also, the relocation and expansion of Highway 32 in Rowan County could support additional growth along this road. The construction on Highway 32 is nearly complete and the development in this area is about to begin. Recently, Walmart purchased a parcel of land in this area and other businesses are actively looking for adjacent properties. The commercial growth in this area will be significant in the coming years.

21 Narrative (continued) Fleming-Mason Energy Members Demographic Information There is an average of 2.32 people per household. 49% of all homes are headed by someone age 55 or greater. Approximately 30% of homes have farm operations, with beef cattle and tobacco most popular. 30% of all homes served are less than 10 years old.

22 Key Assumptions Power Cost and Rates EKPC’s wholesale power cost forecast used in this load forecast comes from the following report: “Twenty-Year Financial Forecast, Equity Development Plan, ”, dated January 2006.

23 Key Assumptions (continued) Economic

24 Key Assumptions (continued) Share of Regional Homes Served Figure 1-5 Fleming-Mason Energy’s market share will increase for the forecast period.

25 Key Assumptions (continued) Household Income Members’ Greatest Sources Figure 1-6

26 Key Assumptions (continued) Appliance Saturations. Room air conditioner saturation is declining due to customers choosing central air conditioning systems.. Appliance efficiency trends are accounted for in the model. The data is collected from Energy Information Administration, (EIA). See Figure 1-7.

27 Key Assumptions (continued) Saturation Rates - Non HVAC Appliances Microwave Oven98% Electric Range94% Dishwasher49% Freezer61% Clothes Dryer96% Personal Computer57%

28 Key Assumptions (continued) Figure 1-7 Source: Energy Information Administration (EIA) Efficiency Trend Update, 2005 All of the projections are very similar to what was used in the 2004 Load Forecast. However, the 2004 Load Forecast assumption was just below 8 by 2024 whereas this update shows the trend continuing above 8.

29 Key Assumptions (continued) Weather Weather data is from the Covington weather station. Normal weather, a 30-year average of historical temperatures, is assumed for the forecast years.

30 Methodology and Results Introduction This section briefly describes the methodology used to develop the load forecast and presents results in tabular and graphical form for residential and commercial classifications. Table 1-3 through Table 1-5 shows historical data for Fleming-Mason Energy as reported on RUS Form 736 and RUS Form 5. A preliminary forecast is prepared during the first quarter depending on when Fleming-Mason Energy experiences its winter peak. The first step is modeling the regional economy. Population, income, and employment are among the areas analyzed. The regional model results are used in combination with the historical billing information, appliance saturation data, appliance efficiency data, and weather data to develop the long range forecast.

31 Table 1-3

32 Table 1-4

33 Table 1-5

34 Methodology and Results (continued) The preliminary forecast was presented to Fleming-Mason Energy staff, and reviewed by the Rural Utilities Services (RUS) Field Representative. Changes were made to the forecast as needed based on new information, such as new large loads or subdivisions. In some instances, other assumptions were changed based on insights from Fleming-Mason Energy staff. Input from EKPC and Fleming-Mason Energy results in the best possible forecast.

35 Methodology and Results (continued) Residential Forecast Residential customers are analyzed by means of regression analysis with resulting coefficients used to prepare customer projections. Regressions for residential customers are typically a function of regional economic and demographic variables. Two variables that are very significant are the numbers of households by county in each member system's economic region and the percent of total households served by the member system. Table 1-6 and Figure 1-8 report Fleming-Mason Energy’s customer forecast. The residential energy sales were projected using a statistically adjusted end-use (SAE) approach. This method of modeling incorporates end-use forecasts and can be used to allocate the monthly and annual forecasts into end-use components. This method, like end-use modeling, requires detailed information about appliance saturation, appliance use, appliance efficiencies, household characteristics, weather characteristics, and demographic and economic information. The SAE approach segments the average household use into heating, cooling, and water heating end-use components. See Figure 1-9. This model accounts for appliance efficiency improvements. Table 1-6 reports Fleming-Mason Energy’s energy forecast.

36 Table 1-6

37 Figure 1-8 Annual Change in Residential Customers

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39 Figure 1-9

40 Methodology and Results (continued) Seasonal Forecast Seasonal sales are projected using two equations, a customer equation and an energy equation. Both are determined through regression analysis and utilize inputs relating to the economy, electric price, and the residential customer forecast. Projections are reported in Table 1-7.

41 Table 1-7

42 Methodology and Results (continued) Public Building Forecast Public building sales are projected using two equations, a customer equation and an energy equation. Both are determined through regression analysis and utilize inputs relating to the economy, electric price, and the residential customer forecast. Projections are reported in Table 1-8.

43 Table 1-8

44 Methodology and Results (continued) Small Commercial Forecast Small commercial sales are projected using two equations, a customer equation and a small commercial sales equation. Both are determined through regression analysis and utilize inputs relating to the economy, electric price, and the residential customer forecast. Small commercial projections are reported in Table 1-9.

45 Table 1-9

46 Methodology and Results (continued) Large Commercial Forecast Large commercial customers are those with loads 1 MW or greater. Fleming-Mason Energy currently has 5 customers in this class and is projected to increase to 8 customers by Large commercial results are reported in Table 1-10.

47 Table 1-10

48 Methodology and Results (continued) Other Forecast Fleming-Mason Energy serves street light accounts which are classified in the ‘Other’ category. This class is modeled separately. Results are reported in Table 1-11.

49 Table 1-11

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51 Methodology and Results (continued) Peak Day Weather Scenarios Extreme temperatures can dramatically influence Fleming-Mason Energy’s peak demands. Table 1-12 and Figure 1-10 reports the impact of extreme weather on system demands.

52 Table 1-12

53 Figure 1-10

54 RUS Form 341

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