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Load Forecast

2015–2025

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2015-2025 LOAD FORECAST

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2015-2025 LOAD FORECAST

TABLE OF CONTENTS

Page

1.0 SUMMARY ... 3

2.0 INTRODUCTION ... 7

3.0 RESIDENTIAL SALES FORECAST ... 8

3.1 End Use Model ... 9

3.2 Residential Forecast Results ... 14

4.0 GENERAL SERVICE AND STREET LIGHTING SALES FORECAST ... 15

4.1 Econometric Model ... 17

4.2 General Service Forecast Results ... 17

4.3 Street Lighting Forecast Results ... 18

5.0 INDUSTRIAL SALES FORECAST ... 19

5.1 Pulp and Paper Industry... 21

5.2 Mining and Smelting Industry ... 22

5.3 LIREPP ... 23

5.4 Industrial Forecast Models ... 23

5.5 Industrial Forecast Results ... 24

6.0 PEAK DEMAND FORECAST ... 28

6.1 Econometric Model ... 29

6.2 Peak Demand Forecast Results ... 29

7.0 SUMMARY OF MAJOR FORECAST ADJUSTMENTS ... 30

7.1 Natural Gas ... 30

7.2 Reduce and Shift Demand (RASD) ... 31

7.3 System Losses ... 32

8.0 OVERALL FORECAST RESULTS ... 33

8.1 Annual Requirements ... 34

8.2 Monthly Energy Supply ... 35

8.3 Monthly Peak Hour and Non-Coincident Demands ... 36

9.0 FORECAST VARIATION ... 37

9.1 Sensitivities ... 37

9.2 Historical Forecast Variations ... 40

10.0 METHODOLOGY UPDATES ... 44

10.1 2007 Load Forecasting Audit ... 44

10.2 Residential Customer Forecast and Household size ... 45

APPENDICES... 48

Appendix 1 – Key Assumptions ... 49

Appendix 2 – Load Forecasting Audit Status Report ... 55

Appendix 3 – General Service Econometric Model ... 60

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2015-2025 LOAD FORECAST

LIST OF TABLES

Page

Table 1: Forecast Summary ... 3

Table 2: Total Energy Sales By Sector ... 6

Table 3: 2013/14 Major Appliance Assumptions ... 11

Table 4: Residential Energy Sales ... 15

Table 5: General Service Energy Sales ... 18

Table 6: Street Light Sales ... 19

Table 7: Industrial Energy Requirements ... 25

Table 8: Industrial Transmission Energy Sales ... 27

Table 9: Industrial Distribution Energy Sales ... 28

Table 10: Peak Demand Requirements ... 30

Table 11: RASD Program Savings by Sector ... 32

Table 12: Annual Energy, Demand and Load Factor Forecast ... 35

Table 13: In-Province Monthly Energy Requirements ... 36

Table 14: In-Province Monthly Peak Hour Demand ... 36

Table 15: Sensitivities of Major Forecast Inputs ... 38

Table 16: Forecast Variance ... 41

Table 17: Non-Industrial Forecast Variance ... 41

Table 18: Forecast Bias Statistics ... 44

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2015-2025 LOAD FORECAST

LIST OF FIGURES

Page

Figure 1: Annual Energy Requirements ... 4

Figure 2: Annual Demand Forecast ... 5

Figure 3: Energy Sales by Customer Classification ... 8

Figure 4: Average End Use Breakdown of Residential Electricity Sales ... 9

Figure 5: 2013/14 Average Appliance Load per Customer ... 12

Figure 6: 2010 – 2013 Primary Heating System Conversions ... 13

Figure 7: Residential Sales Per Household ... 14

Figure 8: General Service Sales ... 16

Figure 9: Industrial Transmission Sales ... 20

Figure 10: Industrial Distribution Sales ... 21

Figure 11: Pulp and Paper Sales History ... 22

Figure 12: High & Low Energy Requirement Forecast Scenarios ... 39

Figure 13: High & Low Peak-Hour Demand Forecast Scenarios ... 40

Figure 14: Non-industrial Actual and Weather Adjusted Actual Variance ... 42

Figure 15: Industrial Forecast Variance With and Without Bowater/UPM Adjustments ... 43

Figure 16: NB Population History and Foercast ... 46

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This report documents the forecast of the electricity requirements of the in-province customers of NB Power Corporation for the ten-year period from fiscal year 2015/16 to 2024/25.

A load forecast is prepared based on a cause and effect analysis of past loads and trends. The analysis uses data gathered through customer surveys along with assessments of economic, demographic, technological and other factors that will affect the utilization of electrical energy. Appendix 1 summarizes the key assumptions used in this forecast In addition to the forecast requirements of each sales classification and total energy supply by month, this document includes a forecast of the annual and monthly peak hour demands (the total amount of energy required in a one-hour period). Finally, the

document includes the forecasted annual system load factor (the average demand as a percentage of the peak hour demand). A management discussion of the forecast is included that compares the forecast to history and highlights the reasons for abnormalities.

The forecast results are used for the financial, facilities and supply planning activities of NB Power, specifically:

 To provide energy sales and requirements to NB Power’s short and long term business plans;

 To support the Transmission System Operator's province-wide forecasting and assessment activities;

 To provide NB Power’s generation and transmission planning groups with a forecast of in-province requirements;

 To provide NB Power’s strategic planning group with long-term energy & demand requirements.

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Energy requirements and the peak hour demand are affected by weather conditions; most significantly by temperature. The energy forecast is based on 30-year rolling average temperatures (1984/85-2013/14). The annual demand forecast is based on the historical peak demands which occur at an average temperature of -24 degrees Celcius.

Actual experience is likely to differ from the forecast and such differences are usually larger in later years. Variations from the forecast can affect future financial and facilities requirements. This document includes a discussion of potential sources of variance and sensitivities, as well as a discussion of forecast accuracy over various timeframes.

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Electrical energy required to meet the in-province load is forecasted to increase from 14,001 GWh in fiscal year 2014/15 to 14,839 GWh in fiscal year 2024/25 as shown in Table 1. During the same period, the maximum one-hour peak demand is forecasted to decrease from 3,000 MW to 2,880 MW.

Energy Forecast

The annual energy forecast and historical requirements are shown in Figure 1. Overall, energy is forecast to grow at an average of 0.6 per cent per year during the 2014/15 to 2024/25 period, compared to annual growth of 0.2 per cent over the last 20 years.

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In 2004/05, in-province energy requirements crested and began a period of decline due to a number of industrial operations being closed in the pulp & paper, chemical and mining sectors. Numerous industrial distribution customers in the forestry sector also closed or reduced operational levels as market conditions for their products deteriorated. In the 11 years prior (1994/95 to 2004/05), the annual average growth rate was 1.3 per cent. A decrease in load from 2013/14 to 2014/15 is the result of the closure of Brunswick Mine and the Penobsquis mine at Potash of Saskatchewan.

Annual forecasted growth is not uniform over the forecast period and growth in the early years of the forecast deviates from long-term historical growth. Higher growth in

2016/17 – 2018/19 is being driven by the addition of new load at large industrial customers (Sisson Mine, Oxford Blueberries, and the new Picadilly mine at Potash of

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Peak Hour Demand Forecast

The annual peak hour demand forecast and historical peaks are shown in Figure 2. The peak hour demand averaged annual growth of 0.4 per cent over the last 20 years. However, it is forecasted to decrease at an average rate of -0.6 per cent per year during the forecast period. This decrease is the result of RASD programs focusing on both energy efficiency and demand response. Without any RASD programs, growth in peak demand would have been +0.7 per cent per year.

Energy Sales

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Annual sales growth is forecasted to be the strongest in the industrial sector over the forecast period. The ramp-up of the new potash mine, opening of Sisson Mine and overall growth of small industrial customers are driving sales increases to industry. Growth in the residential sector is mainly due to an increase in the number of customers, driven by decreasing average household size (persons per household). The general service sector experiences natural growth as a result of consistent GDP growth.

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For forecasting purposes, NB Power’s in-province electrical requirements are divided into three main groups: residential, general service, and industrial. The grouping reflects similarity in end uses of electricity; that is, the electrical requirements of customers in each group are similar and the customers within each group are to some extent homogenous. As a result electricity requirements within each group are affected by similar factors.

The residential classification includes year-round and seasonal households, churches, and farms. The general service classification comprises mostly commercial and institutional establishments. The industrial classification is for customers involved in the extraction of raw materials or the manufacturing and processing of goods.

The residential, general service and industrial forecasts are then separated into seven customer classifications: residential, general service, street lighting, industrial

distribution, industrial transmission and wholesale (includes the sales to the preceding classifications by the municipal utilities in the cities of Saint John and Edmundston).

Forecasts by customer classification are required for facilities and financial planning. The relative proportions of in-province energy sales in fiscal year 2013/14 to each of the seven customer classifications are shown in Figure 3.

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The following sections outline the methodology, inputs and results of the annual energy sales forecast. Also included is the monthly distribution of the annual energy

requirements and the associated peak hour demands.

In fiscal year 2013/14, residential customers accounted for 45 per cent of the total in-province electrical energy sales (40 per cent directly by NB Power and five per cent by Wholesale utilities). The residential classification is made up mostly of year-round domestic (household) customers. It also includes some non-domestic customers such as farms and churches, which account for less than three per cent of the total residential energy requirements. Seasonal customers, accounting for approximately one per cent of

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Growth in the residential forecast is driven by the addition of new customers. Approximately 75 per cent of new residential customers are the result of decreasing average household size while the remaining 25 per cent are the result of provincial adult population growth. Increasing annual household usage is offset by naturally occurring energy efficiency and RASD programs.

Average household energy is comprised of electric space heating, water heating and other uses. In 2013/14 the average household energy was comprised of 46 per cent electric space heating, 18 per cent water heating and 36 per cent other uses. The breakdown of usage by end use is shown in Figure 4.

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appliance trends and efficiency standards. To account for such trends, the forecast for the total residential class is based upon an end use model that requires identification of the various applications of electricity. These applications include space heating, water heating and other household appliances. The penetration (saturation) level and the average use for each household application provide the basis for average use per customer. The number of customers is based on an analysis of population trends. The model can be simply stated as:

Energy = Year round Customers • Average Use per Customer where:

Average Use per Customer =

(Appliance • Average Use)

In 2013/14, there were 340,400 year-round residential customers in New Brunswick. Of those, 301,500 were served directly by NB Power and 40,000 were served by the

municipal utilities in the cities of Saint John and Edmundston. The adult population of New Brunswick is forecast to increase by 6,700 adults over the forecast period, which represents a 0.1 per cent average annual increase. In New Brunswick, the average number of persons per household is expected to decline from 2.21 in 2013/14 to 2.14 in 2024/25. These factors result in an increase of 15,300 new year-round customers over the forecast period.

Details on the customer forecast can be found in Section 10.2.

An appliance efficiency model was used to estimate the changes in per unit consumption for major household appliances: refrigerators, freezers, dishwashers, clothes washers and

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expected average use for each appliance. All new appliances are assumed to meet existing energy efficiency standards so as older appliances are replaced, the energy efficiency of stock appliances will increase over time.

The saturation levels of major household electrical appliances were based on data from Natural Resources Canada and compared to results from NB Power’s 2013 Energy Planning Survey of residential customers for verification. The 2013/14 appliance data is shown in Table 3.

The average major appliance usage per customer per year is 2,695 kWh. The breakdown of each end use on a per customer basis is shown in Figure 5.

Refrigerators 120% 569 Electric Ranges 97% 697 Freezers 72% 418 Dishwashers 55% 122 Clothes Washers 91% 88 Cothes Dryers 89% 990 Annual Energy Consumption (kWh/unit) Saturation rate (units/houeshold)

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The penetration of electric heat has increased slightly from 62 per cent in 2008 to 63 per cent in 2013 based on the latest Energy Planning Survey results. This is because most new homes have opted for electric space heating (75 per cent) and electric water heating (90 per cent). Existing homes have also shown a trend of moving toward electric space heating. Between 2010 and 2013, approximately 6 per cent of customers converted their primary home heating system. Of those, an estimated 22 per cent more moved away from fossil fuels (oil, natural gas and propane) than converted to fossil fuels. Electricity saw a net gain of approximately 23 per cent of the conversions, while other heating fuels (including wood, pellets, solar, etc.) were largely unchanged. See Figure 6 below.

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The penetration of electric water heating has also increased slightly from 92 per cent in 2008 to 93 per cent in 2013. Similar to space heating, more customers are opting to heat their water with electricity rather than using fossil fuels.

As indicated previously, the annual energy requirement of the year-round customers is a function of the stock of electrical appliances and the extent to which these appliances are used. The annual energy required for space heating was derived by analyzing data from the previous year's sales along with historical and forecasted heating degree day effects. Average energy required for water heating was based on historical trending of kilowatt-hours per person. For the remaining appliances, estimates of average annual usage were based on an appliance efficiency model.

Since 1994/95, the average use for year-round residential customers has decreased from 17,000 kWh/year to 16,600 kWh/year in 2013/14; an average annual decrease of 0.1 per cent. As shown in Figure 7, usage per customer peaked in 2005/06 at 17,700 kWh/year. Since then the average has declined by approximately 0.5 per cent per year. This

decrease was the result of declining household size and improvements in construction standards. Also, the establishment of Efficiency NB in November 2005 granted

To Electricity 54% To Oil/Gas 12% To Wood/Other 34% From Electricity 31% From Oil/Gas 33% From Wood/Other 35%

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customers access to programs to help them save energy. With continued investment in RASD programs by NB Power, this trend is expected to continue through the forecast period. Further detail on RASD programs can be found in Section 7.2.

The total New Brunswick residential electrical energy requirements are forecasted to increase from 5,740 GWh in fiscal year 2014/15 to 5,888 GWh in fiscal year 2024/25. The net increase in the forecast period is 148 GWh, an annual average increase of 0.3per cent. Year over year growth is higher in early years of the forecast as a result of

population growth trends. Table 4 summarizes the history and forecast of residential sales. Past large year over year variations were the result of weather variances.

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In the 2013/14 fiscal year, general service energy requirements accounted for 21 per cent of the total in-province energy sales (17 per cent directly by NB Power and four per cent by Wholesale utilities). Street lighting sales, which include unmetered services, accounted for less than one per cent of total provincial sales.

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Sales to the general service classification include commercial (retail/wholesale, hotel/motel/restaurants, offices) and institutional customers (hospitals, schools, universities). At the end of March 2014 there were 25,500 general service customers served by Disco and an additional 5,000 served by the wholesale utilities.

The proportions of total general service sales to each of the major customer groups are shown in Figure 8.

Approximately 70 per cent of general service sales are commercial in nature and, therefore, considered to be directly related to the level of provincial economic activity. The remaining 30 per cent of general service sales are to the institutional sector, which is indirectly related to the economic activity in the province. As the economic activity of the province increases, the activity in the institutional sector is also likely to increase.

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General Service sales in New Brunswick reflect the level of commercial activity and are closely related to the provincial gross domestic product (GDP). In addition, weather affects the level of sales. The general service model relates changes in the level of sales to changes in the provincial GDP, the real price of electricity, the number of heating degree days, the previous year's sales, and the previous year’s number of heating degree days. An average annual real GDP growth of 1.8 per cent is forecasted between 2015/16 and 2024/25. This GDP growth forecast was based on the June 2014 Province of New Brunswick Economic Outlook and consultation with staff at the Department of Finance on the prospects of the provincial economy. Based on NB Power’s current RASD plan, energy savings are assumed to increase during the course of the forecast, suppressing growth through all years. Normal heating degree days were based on a weighted average provincial total for the 30-year period 1984/85 to 2013/14. Detailed model parameters and the “fit” of the model, which illustrates confidence in the performance of the model, are included in Appendix 3.

Table 5 summarizes the history and forecast of general service sales. The total New Brunswick general service energy requirements are forecasted to increase from 2,929 GWh in fiscal year 2014/15 to 3,053 GWh in fiscal year 2024/25. The net increase in the forecast period is 124 GWh. Growth is steady through the forecast period, based on consistent GDP growth and no change in real prices. The wholesale portion of the general service sales is expected to increase by 28 GWh over the forecast. The general service forecast also accounts for decreases from RASD programs which are estimated to reduce sales by 4 GWh in 2015/16, increasing to 85 GWh in 2024/25. Note that the RASD program energy efficiency estimates are based solely on future programs and would be in addition to any past or current energy efficiency savings.

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Street lighting sales include unmetered energy sold for street and area lighting plus other services such as sign lighting and traffic signals. The key factors affecting growth in street light and unmetered installations are new residential and commercial development. The total New Brunswick street light energy requirements are forecast to decrease from

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emitting diode (LED) technology is driving energy decreases. Table 6 summarizes history and forecast of street lighting sales.

In 2014/15 New Brunswick's industrial customers will consume about 33 per cent of the total in-province electrical energy. Industrial sales are divided into two groups: industrial

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transmission sales (to customers who are served at transmission voltages of 69 kV and above) and industrial distribution sales (to customers who are served at distribution voltages of 25 kV or less). Sales as part of the Large Industrial Renewable Energy Purchase Program (LIREPP sales) are included within the industrial transmission sales unless noted otherwise. There are 36 customers served at transmission voltages, which constitute the majority of industrial sales. The portions of total industrial transmission sales for fiscal year 2013/14 to each of the main industry groups are shown in Figure 9.

The industrial transmission forecast includes the opening of a major mining operation and food processing plant within the forecast period.

NB Power serves some 1,700 industrial customers at distribution voltages, while the wholesale utilities serve approximately another 70. Together they account for 16 per

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Power's industrial distribution sales to the main industry groups for fiscal year 2013/14 are shown in Figure 10.

An overview of the key industrial groups and the factors that can affect their future electrical requirements is presented in the following sections.

In fiscal year 2003/04 and the years prior, sales to the pulp and paper industrial

customers were approximately 3,600 GWh compared to 2,200 GWh in 2013/14. This 1,400 GWh per year sales reduction resulted from the permanent closure of two paper mills, Bowater Maritimes and UPM Kymmene due to world market and economic conditions. The effect of this closure on sales is shown in Figure 11.

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Since 2008/09, the pulp and paper industry in New Brunswick has stabilized and additional mill closures are not assumed to occur during the forecast period.

The electricity intensive mineral industries in New Brunswick are related to base metal and potash mines. The base metal industry includes mining and concentration of ore bodies with zinc, copper, lead and silver. Lead concentrate is refined into lead ingots in Belledune. The potash mines in southeast New Brunswick export essentially their entire product. Production is tied to the worldwide demand for potash, used in fertilizer. The size and quality of the ore body limit the useful life of mines. Brunswick Mine closed operations in 2013, while Potash of Saskatchewan has recently shut down their existing

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is expected to begin mining Molybdenum and Tungsten at Sisson Mine in the western part of the province.

Under the Large Industrial Renewable Energy Purchase Program (LIREPP), NB Power purchases electricity from qualifying large industrial customers who have renewable electricity generating facilities located in New Brunswick. LIREPP renewable energy purchases contribute to meeting the overall renewable energy target set out in the Electricity from Renewable Regulation – Electricity Act. The revenue from renewable energy sales assist these qualifying customers in reducing their net electricity costs and thereby increase their competitiveness in the global market. The LIREPP program results in an increase in sales and revenue and a corresponding increase in generation

requirements and cost. LIREPP sales differ from typical sales, as they are generated exclusively at customer owned facilities. The energy for LIREPP is produced at the site of the end use and therefore does not impact NB Power’s capacity planning requirements, system losses or transmission system requirements. Because of this, LIREPP sales are sometimes reported separately from the system energy requirements.

The relationship between increases in the total electricity requirements and provincial gross domestic product (GDP) for goods producing industries is used to forecast

industrial load growth. Industrial transmission and industrial distribution forecasts are modeled separately. The industrial distribution forecast is based on historical trends and GDP growth. The firm and non-firm industrial transmission forecast is modeled on a customer-by-customer basis with adjustments for unallocated growth and RASD

programs. Unallocated growth is added beginning in 2018/19 and is based on historical trends and GDP growth.

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Each industrial transmission customer was modeled individually based on historical load levels and input from NB Power’s account management team. The forecast was adjusted to include known industrial transmission closures and expansions. Typically, new large industrial projects or expansions are made known a few years ahead of opening.

Therefore, unallocated growth begins in 2018/19 to capture unannounced new customers or expansions that would otherwise not be included. Customer-owned generation was also modeled in aggregate based on historical data.

An econometric model was used to forecast the total industrial distribution electric energy sales. The model was based on a forecast of provincial goods producing gross domestic product and its historical relationship with industrial distribution sales. During the forecast period, average 1.8 per cent increases in GDP increase the industrial

distribution sales by 84 GWh (1.1% per cent per year) after the effects of RASD programs. Statistical details on the industrial distribution econometric model are provided in Appendix 4. The forecasted industrial distribution electrical requirements are split between the amount to be supplied by NB Power and municipalities based on a historical factor.

The industrial distribution forecast is based on growth in the overall GDP in New Brunswick. The key input assumption is an average annual GDP growth of 1.8 per cent over the forecast period. The industrial transmission forecast is based on individual customer forecasts and the key input assumptions are announced new customers and expansions.

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Energy efficiency savings are expected as a result of NB Power’s RASD industrial programs. Energy savings applied to the industrial transmission sector total 31 GWh annually in 2024/25. Energy savings applied to the industrial distribution sector total 7 GWh annually in 2024/25.

The history and forecast of the total industrial electricity requirements are shown in Table 7.

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Industrial Transmission

Sales to transmission voltage customers are shown in Table 8. Non-firm sales to

transmission customers make-up part of the overall energy requirement; some customers can accommodate infrequent and short duration interruptions so they take a portion of their energy needs as non-firm supply. While non-firm energy and demand are included in the forecast of in-province requirements, they are excluded for capacity planning purposes.

The forecast decreased in 2013/14 and 2014/15 as a result of the closures of operations at Brunswick Mining. The increases in 2015/16 to 2018/19 result from the Potash of Saskatchewan expansion, and the addition of two new customers: Sisson Mine and Oxford Blueberries. Gradual decreases in later years result from NB Power’s RASD programs.

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Industrial Distribution

Sales to distribution voltage industrial customers (by both NB Power or municipal utilities) are shown in Table 9. The net increase over the forecast period is 84 GWh. Economic and market conditions have resulted in significant reductions in industrial distribution sales since 2004/05. A number of sawmills and forestry-related customers have closed or reduced operations. Historical industrial distribution sales tend to be cyclical in nature. Industrial distribution sales are expected to grow but sales are not

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NB Power is a winter peaking system driven by electric space heating in homes and businesses, with the peak normally occurring in late January or early February. In addition to the total annual energy, the maximum energy requirement in a one-hour

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Peak demand is driven by weather. The system peak typically occurs on one of the coldest days of the year, on average at -24oC. Weather sensitive loads are subject to the same weather patterns, and therefore peak at roughly the same time. Industrial loads tend to be less affected by weather and remain relatively flat. The econometric model relates peak demand to the level of monthly industrial and non-industrial sales and changes in temperature.

The average monthly load levels within industrial and non-industrial classes prior to the effects of any RASD programs are used, along with monthly normal peak temperatures to forecast the unadjusted peak demand to 2024/25. Based on NB Power’s current RASD plan, peak demands are then adjusted for both energy efficiency and demand response programs. Normal peak temperatures days were based on a weighted average provincial total for the 30-year period 1984/85 to 2013/14. Detailed model parameters and the “fit” of the model, which illustrates confidence in the performance of the model, are included in Appendix 5.

Table 10 summarizes the history and forecast of system peak demand. The total New Brunswick system peak demand requirements are forecasted to decrease from 3,000 MW in fiscal year 2014/15 to 2,880 MW in fiscal year 2024/25. The net decrease in the forecast period is 120 MW. Through the forecast period, peak demand requirements slowly decline, and decrease more sharply in the final three years due to more aggressive RASD programs. The LIREPP portion of the peak demand is expected to remain

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Prior to 2013 the load forecast was adjusted to account for residential customers converting to natural gas from electricity. Based on the results of the Energy Planning

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converting from oil to electricity is more than enough to cancel out those converting from electricity to natural gas. Refer to Figure 6 in Section 3.1.3.

Natural Gas has been widely available in many urban areas of New Brunswick since 2001. The effect natural gas has on the general service sector is therefore captured within the general service forecasting model, making an adjustment unnecessary. Natural gas is not expected to reduce industrial sales.

An important part of the load forecasting process is recognizing the effects of conservation, energy efficiency and load demand management, also referred to as demand-side management. Demand management is any attempt to change or influence the demand placed upon the system by the customer. It encompasses a broad range of techniques from the direct control of customer equipment to educating customers about conserving electricity.

Reducing Demand

Energy efficiency and conservation is an integral part of NB Power’s Reduce and Shift Demand (RASD) program. This part of the program provides benefit to the participating customers through direct savings on their power bills. It also provides benefit to NB Power through immediate fuel cost savings and through lower capital requirements in the long term by reducing the need for new supply in the future. This provides indirect benefit to all customers by ensuring low and stable rates.

Shifting Demand

These programs will be enabled through smart grid technology that will allow the systematic control of participating customers’ load. The shifting of demand will benefit the utility by improving the utilization and operation of generating plants and through an overall improved efficiency in operating the transmission and distribution systems. This

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Forecast Impacts

Estimates of NB Power’s RASD program were factored into the forecasts for the residential, general service, street lights and industrial sectors. These estimates were based on a 10-year projection of NB Power’s Triennial RASD Plan. Program savings lower the forecast by 820 GWh and 390 MW in 2024/25. Table 11 shows the energy efficiency estimates by sector and peak demand savings for the system. Efficiency NB’s program savings are also included.

The forecast also includes estimates of energy efficiency measures that consumers are expected to naturally implement. The impact of improving construction standards in the residential sector is expected to increase the thermal shell efficiency of homes in the province, reducing average heating requirements by 0.25 per cent per year. This equates to 77 GWh and 20 MW in the final year of the forecast.

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the customers at standard service voltages. There are losses associated with each of these stages. The amount of losses is a function of the load levels, technical characteristics of the transmission and distribution system and the distance between the generation sources and the customers.

The basis of forecast energy losses on the transmission system is the Open Access

Transmission Tariff loss factor, currently 3.3 per cent. Loss factors or percentages were multiplied by the amount of energy delivered over the system to meet NB Power’s total energy requirements. LIREPP generation and sales occur behind the customer’s

transformer and are therefore not subject to transmission losses.

Distribution losses were forecasted based on an analysis of the energy supplied over the distribution system compared to the billed distribution sales. Energy losses on the distribution system are estimated at 4.0 per cent of the total distribution sales over the forecast period. In addition, a substation transformer loss factor of 0.6 per cent is applied to all distribution level sales.

The total energy supply requirements for NB Power are the combined total of the sales to the seven customer classifications plus transmission and distribution losses related to those sales.

In order for the forecast to be used effectively in the overall utility planning process, it is necessary to spread the total sales and associated system losses over the year. The monthly spread is particularly useful for planning system operations and estimating the peak hour and non-coincident demands. The following sections outline the overall forecast results of annual requirements by customer class, monthly energy supply and peak hour and non-coincident demands.

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Results of the residential, general service and industrial forecasts are allocated to the sales classifications and, where applicable, further allocated to the municipal wholesale utilities in the cities of Saint John and Edmundston. Table 12 summarizes the forecast sales to each of the seven customer classifications: Residential, General Service, Street Lighting, Industrial Distribution, Industrial Transmission, Wholesale and LIREPP. Also included in the table are the forecasted transmission and distribution losses, total energy supply (total sales plus losses), associated peak hour and non-coincident demands, and annual load factors. LIREPP sales are shown separately as many applications of the data require it to be removed.

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The annual energy sales are apportioned by month for revenue and cash flow projections. The monthly spread of the annual forecast requirements is also required for scheduling the operation and maintenance of facilities. The monthly energy supply forecast is shown in Table 13.

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The peak hour demand for each month of the forecast is shown in Table 14. As with the monthly energy spread, the peak demands were estimated separately for the base energy and any adjustments (e.g. RASD) on a month by month basis. The base

energy-associated demand was combined with the adjustment-energy-associated demand to give the final peak demand for each month.

LIREPP sales are not always required for system planning or generation adequacy purposes. Therefore, the monthly energy and demand without LIREPP are included in

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not necessarily occurring at the same time interval. It is used to calculate transmission tariff and ancillary service costs.

The actual electric energy requirements and peak demands are expected to differ from the forecast. Furthermore, these differences are usually larger in the later years of the

forecast. In view of the significance of the forecast for facility and financial planning, it is useful to understand the causes and effects of significant variances.

In a specific year, the actual sales can be significantly affected by fluctuations in weather and the operational levels of large industrial customers. The forecast is based on past results that have been adjusted for such temporary fluctuations.

In the long-term, the forecast will differ from actual experience as the factors that

contribute to load do not materialize as forecasted or do not have the expected impact. For example, provincial economic growth may exceed (or fall short of) the forecast value or the impact of economic growth on electricity requirements may in the future be less (or more) than expected. Also, the take-up of energy efficiency programs may be more (or less) rapid than expected, and displace more (or less) existing load than forecast. Weather adjustments to historical energy supply are made based on a 30-year average of the heating degree-days in each month. Adjustments to historical peak demand are made based on the difference between average temperature in the eight hours leading up to the peak hour and -24ºC (the average temperature for peak demands since 1985).

Major factors that impact the actual requirements for electricity and sensitivities to variations in forecast inputs have been estimated and are presented in Table 15.

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Figures 12 and 13 illustrate the combined impact a number of these sensitivities could have on the forecast. The high and low cases include the following assumptions.

 new/closure of 50 MW industrial customer

 RASD program savings +/- 25 per cent of estimate

 Change of +/- 0.5 per cent to GDP growth rate in all years of the forecast

The likelihood of all three sensitivities occurring at the same time is unknown, but they were chosen to provide a reasonable upper and lower limit to the forecast. Forecasted annual average growth is 0.6 per cent, compared to 1.1 per cent and 0.1 per cent in the high and low cases respectfully.

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The impact of heating-degree variations from the 30 year average has not been included in the high and low cases, but can be significant in a particular year. Historical

variations greater than 300 GWh have occurred due to abnormally warm or cold temperatures in a fiscal year. The impact of the average temperature at the time of the peak demand has also not been included. In the years 2002/03 to 2004/05, the energy requirements were very similar yet the peak demands were very different. The peak demand during fiscal year 2003/04 occurred at a temperature of -30 degrees Celsius, while the adjacent years were very close to expected at -24 and -25 degrees Celsius. This five degree difference resulted in a 200 MW increase in peak demand. In 2006/07 the demand drops back down again due to both a drop in the energy requirements as well as a warmer peak temperature (-18 degrees Celsius).

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A comparison of forecasts prepared from 1990/91 to 2012/13 with actual results is shown in Table 16. This table compares the forecast of energy supply and peak hour demand to actual and weather adjusted results. The short-term forecasts tend to be fairly close, with most of the variance in any year being related to a combination of weather and industrial operating conditions. As the forecast period increases, the accuracy of the forecast decreases. On a weather adjusted basis, the energy forecasts tend to be accurate within about one per cent per forecast year. Demand forecasts tend to be slightly less accurate than energy. Though not shown, there are more occurrences in which the forecast is higher than actual. This reflects the increased likelihood of industrial closures than a new unplanned industrial load and warmer than normal temperatures in recent years.

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Since 2005 poor world market conditions have caused several large industrial customers to close operations. Past forecasts were based on continued operation of existing

customers resulting in higher than expected variances. The variances in Table 17 show the accuracy of the energy requirements less industrial sales, which give a better

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Budget forecasts are based on forecasting one year in advance. Because of the

significance of the budget, the accuracy and bias (if any) of the one-year forecast is of particular importance. Figure 14 shows the non-industrial energy sales forecasted one year in advance.

Weather adjusting the actual improves the accuracy of the one year forecast greatly from 2.7 per cent to 1.0 per cent.

For industrial energy sales, a similar analysis shows a five year period (2005/06 through 2009/10) where forecast had large varinaces in excess of five per cent. The years 2005/06 to 2008/09 forecast errors were the result of two major industrial closures: Bowater Maritimes and UPM Kymmene. Both the forecast and actual were adjusted to exclude the Bowater and UPM loads, and the variance plotted in Figure 15.

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The variance in 2009/10 was caused by the significant downturn in the economy leading to production shutdowns and closures of other pulp and paper customers and mining operations.

Forecast accuracy is measured in two ways. The first goal of forecasting in any individual year is to minimize forecast error (or variance), and on a longer term scale, forecast bias should be minimized. Bias is the tendency of a forecast to vary in one direction more than the other. It is important to test that the energy forecasts are not systematically (though inadvertently) being over or under forecast. The simplest way to test this for bias is to analyze the forecast error in each year with a one sample t-test. The t-test is a statistical tool used to measure whether a sample’s mean is different from a specific value, in this case zero.

̅

Where: ̅ = the sample mean (in this case the average forecast error) 0 = the null hypothesis (in this case, zero)

 = the sample standard deviation

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If the magnitude of t is greater than 1.96, bias is said to be present within the sample. The statistics for the weather adjusted non-industrial and adjusted industrial load groups are shown in Table 18.

Because in neither instance the magnitude of t exceeds 1.96, bias is not present in the industrial nor non-industrial forecasts.

NB Power strives to continuously improve its forecasting models. In 2007, an

independent consultant GDS Associates reviewed NB Power’s Load Forecasting process. In the auditor’s opinion, the forecast meets industry standards and best practices. Nine recommendations were made to improve the results and transparency. A summary of the recommendations and their statuses is shown below in Table 19. A detailed update on each measure can be found in Appendix 2.

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In addition to the nine recommendations, the 2007 Load Forecast audit discussed other areas for possible improvement. Special mention was made of the residential customer forecast, outlining its importance and the need to review the methodology.

“The greatest impact on changes in residential sales is growth in the number of customers; therefore, considerable attention must be devoted to the development of a number of households forecast, which drives the customer forecast.”

-GDS Associates, Load Forecasting Audit. (2007) New Brunswick is facing significant demographic challenges, as the population ages and birth rate declines drawing more attention to the relationship between population and number of residential customers. Since 1994, the total population of NB is largely unchanged but there has been a shift in the demographics, as a larger percentage of the population is now over the age of 20. This trend is important, as the number of

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decline in general population in the next ten years, yet adult population increases in the same period as shown in Figure 16.

Household size is calculated as the average provincial population divided by the number of year round residential customers. Household size in the traditional sense is declining because of the declining youth population as well as cultural factors leading to more people choosing to live alone. Basing the household size on the number of adults only, isolates the two phenomena to give a better forecast.

The number of adults per household is declining slowly as shown in Figure 17. A logarithmic regression was used to represent the facts that household size is decreasing, yet decelerating and it cannot decrease indefinitely to zero, but will rather trend to a

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Where: t = the fiscal year ending (for 2015, t = 0)

and 1.532 is the long term, theoretical minimum for adults per household

The household size and population forecasts are combined to give the final year round customer forecast, as denoted below:

Year-round Customers = Adult Population • Adults per Household When this change was first implemented in 2012, the impact to the forecast was a decrease in customer growth of 16,000 customers over 10 years. The current forecast shows customer growth down slightly, as the population forecast has been reduced since the 2012 outlook.

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 The GDP forecast is based on the June 2014 Province of New Brunswick Economic Outlook and consultation with staff at the Department of Finance on the prospects of the Provincial economy.

 Low growth in 2014/15 is boosting 2015/16. Growth in 2016/17 and beyond is assumed at slightly reduced historical levels.

Fiscal Year Real GDP % Change 2015/16 2.1 % 2016/17 1.8 % 2017/18 1.8 % 2018/19 1.8 % 2019/20 1.8 % 2020/21 1.8 % 2021/22 1.8 % 2022/23 1.8 % 2023/24 1.8 % 2024/25 1.8 %

 Sensitivity: +0.1% change in annual GDP growth 2015/16: 0 GWh, 0 MW

2024/25: 40 GWh, 5 MW

 Continued operation of existing large industrial customers through the forecast period. Individual loads modeled based on historical sales levels and known information about customers’ future operational plans.

 No industrial shutdowns are included within the forecast period.  New customers and load additions include:

Potash of Saskatchewan second mine (sinking of shaft and compaction load until full production in 2017/18).

Sisson Mine assumed to be operational in 2016/17

Oxford Blueberries assumed to be operational in 2016/17

 Unallocated growth averaging 0.8%/year is assumed beginning in year 4 (2018/19).

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 (LIREPP) will increase sales to some Industrial Transmission customers currently self supplying. Separate forecasts are prepared with and without LIREPP sales.

 Continued participation of current customers assumed. No new customers are assumed to join the program.

 Canadian average rates are assumed to increase at 3%/year. NB Power rates are assumed to average 2%/year through the forecast period, resulting in a decrease in the LIREPP discount:

Fiscal Year LIREPP Discount % 2015/16 13.5% 2016/17 14.6% 2017/18 13.4% 2018/19 13.5% 2019/20 12.7% 2020/21 11.8% 2021/22 11.0% 2022/23 10.1% 2023/24 9.2% 2024/25 8.3%

 An econometric model was used to forecast industrial distribution sales. The model is based on a forecast of provincial goods producing gross domestic product and its historical relationship with industrial distribution sales.

 A 2.0 per cent average rate increase is assumed in July 2015, followed by and 2.0 per cent average rate increases (no real increase in the price of electricity) in remaining years.

 Consumer Price Index (CPI) assumed to be 1.8 per cent in 2015/16, and 2.0 per cent in all other years.

 Sensitivity: 1% increase in 2015/16 rates 2015/16: -24 GWh, -5 MW

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 Price elasticity is -0.42 per cent per +1.0 per cent change in the real price of residential electricity.

 Provincial population forecasted to decrease by approximately 8,500 people over the forecast period or -0.1% per year. This assumption is based on the province of New Brunswick’s population forecast.

 The adult population is forecasted to increase by approximately 4600 people over the forecast period. This assumption is based on the province of New Brunswick’s population forecast.

 An average of 1,500 new year-round customers are forecasted in the Province per year based on trending of historical growth and the adult population forecast.

 Household size declines on average 0.3 per cent (or 0.006 adults per household) each year over the forecast period.

Fiscal Year Population Forecast Adult (20+) Population Forecast Average Adults per Household Customer Forecast 2015/16 755,638 605,764 1.76 344,211 2016/17 755,263 607,439 1.75 346,393 2017/18 754,763 608,682 1.75 348,308 2018/19 754,137 609,834 1.74 350,149 2019/20 753,388 610,481 1.74 351,675 2020/21 752,509 611,093 1.73 353,159 2021/22 751,482 611,182 1.72 354,315 2022/23 750,293 611,037 1.72 355,309 2022/23 748,938 610,764 1.71 356,204 2023/24 747,415 610,355 1.71 356,994 Average

Growth -850 / year +460 / year

-0.006 / year

+1500 / year

 Sensitivity: 1,000 extra new year round customers in 2015/16 2015/16: 14 GWh, 5 MW

2024/25: 16 GWh, 5 MW

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 Based on 1984/85-2013/14, 30-year normal heating degree days (4,666 HDD/year).

 This is 8 HDD more than last year’s forecast due to rolling the 30 year average for normal heating degree days. The impact is equivalent to increasing the forecast by approximately 7 GWh.

 Natural gas has been in the New Brunswick market for ten years, sufficient time for the General Service econometric model to pick-up natural gas trends. As such, no adjustments have been made to account for natural gas displacing electric load in the General Service sector.

 Residential customers converting to natural gas are assumed to be offset by conversions from oil.

 Incremental energy efficiency savings on existing electricity sales resulting from NB Power‘s RASD programs to residential, commercial and industrial

customers have been included in addition to naturally occurring conservation historically modeled.

 Program saving estimates for Residential, General Service and Industrial are based on NB Power’s 10-year projection of NB Power’s Triennial RASD Plan.  Savings include the effects of Efficiency NB Programs

 LED Street Lighting Program to replace all street lights by 2016/17

 Breakdown of energy efficiency savings on existing electricity sales assumed in the forecast:

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 Consumption use of new major appliances has been decreased to reflect

appliance efficiency legislation and consumer buying habits. These ratings are based on Federal Government data published in December 2013 for appliances shipped in Canada. Energy savings from improved appliance efficiencies are included directly in the end-use model and are in addition to the saving estimates provided above.

 General Service reduced by 5 GWh by end of forecast to account for alternate energy sources that customers may install such as micro turbines, solar and wind.

 The impact of alternate energy / net metering in the Residential sector is not considered to be material in the forecast period. The current connected capacity of the 47 net-metered customers is approximately 310 kW.

 Transmission losses are assumed to be 3.3%.

 Distribution losses are assumed to be 4.0% of Distribution Sales based on history.

 Distribution substation losses assumed to be 0.56%

GWh MW

RASD Programs:

Residential Programs 366 210

General Service Programs 105 110

LED Street Lighting Program 28 0

Industrial Programs 271 40

Sales Sub-Total 769 360

Losses 49 30

Total 818 390

Naturally Occurring:

Residential (Thermal Shell Improvements) 81 20

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 Remain constant over the life of the forecast.

 Unit Energy Consumptions (UECs) of stock appliance updated based on 2011 Canadian data published by Natural Resources Canada. Canadian UECs assumed to reflect New Brunswick as federal legislation mandates efficiency standards for manufacturers.

 Number of stock appliances calibrated to reflect New Brunswick data based on 2011 Natural Resources Canada published data.

 Miscellaneous appliance (plug load) and lighting load is assumed to grow by 2.1% per year based on 2002 to 2011 growth using data from Natural Resources Canada.

 Sensitivity: 0.1% annual increase to the plug and lighting load growth 2015/16: 2 GWh, 0 MW

2024/25: 17 GWh, 5 MW

 All wholesale customers are assumed to continue on standard service supply.

 A total of 8,000 electric vehicles are forecast to be in New Brunswick by 2024/25. This represents a market share of approximately 1.6% of the approximately 500,000 registered vehicles in the province. Each electric vehicle is assumed to consume 3,000 kWh per year. This estimate is based on 15 kWh/100km travelled and assumes average mileage per vehicle of 20,000 kilometers each year. The total adjustment in 2024/25 is 18 GWh or 4 MW.

 Consistent with the methodology reviewed by the Public Utilities Board in 2007 with refinements to the model as a result of an independent audit and continuous modeling improvements.

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In July 2007, GDS associates issued a Load Forecasting Audit. It reviewed the entirety of the NB Power’s Load forecasting process and based on discussions with NB Power staff and an analysis of the information collected during the study, the following actions were recommended:

“A historical dataset (10 years or more) should be developed for use in analyzing historical residential energy sales and providing the basis on which forecasts are made. The dataset should include historical values for all model inputs. NB Power should utilize its load research data, to the extent possible, to support unit energy consumption (UEC) values contained in model.”

-GDS Associates, Load Forecasting Audit. (2007) 2/3 Complete

A dataset of the inputs for the General Service and Industrial forecast models already exists and is used in the model’s development.

The Development of a historical a dataset for all Residential model inputs can not be completed. There are virtually hundreds of model inputs. Historical data is not published or available internally for many of these inputs. A data set containing key inputs such as; number of customers, household size and known market penetration levels is being developed as part of the backcasting process described below.

“A backcasting process should be developed as a means of validating existing forecasting models. Backcasting is the process of estimating historical energy sales and peak demand using existing forecasting models. Backcasting provides a means for determining the relative accuracy of existing forecasting models by comparing actual and model estimated values. Trends in the model residuals may provide additional insight for improving the existing models. Results of the backcasting analysis will also provide load forecasting staff valuable information for defending the forecast.”

-GDS Associates, Load Forecasting Audit. (2007) 2/3 Complete

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A backcast or fit of the econometric General Service and Industrial models has been prepared and is included in the annual Load Forecast report. A preliminary investigation to backcast the residential end use model has been completed. Performing a backcast using all model inputs cannot be completed as historical data for all inputs is unknown. It is possible to backcast using the key inputs such as; number of customers, household size and known market penetration levels, but a 10 year historical dataset is currently not available. Data will continue to be collected. This work is expected to be completed by the end of 2015.

“NB Power should revise its normal weather criteria to base normal weather parameters (degree days or temperatures) on a rolling period rather than a fixed period. Data is readily available to compute normal weather parameters based on the most recent 15, 20, or 30 years. Historical data indicates a long-term warming trend; therefore, it is appropriate to update normal values annually rather than use fixed period normals, which may tend to overstate heating degree days over the forecast horizon.”

-GDS Associates, Load Forecasting Audit. (2007) Complete

Prior to 2011, NB Power used the 30-year (1971 to 2000) average heating-degree-days of 4,776 as per Environment Canada’s standard. Analysis indicated that moving to a

rolling 30-year average would lower the annual load forecast by approximately 65 GWh or 7.5 per cent of the weather sensitive sales (Residential, General Service and

Wholesale).

Surveys from 2005 and 2007 indicate that a 30-year period or more remains the most common period for which utilities normalize weather. There does appear to be a trend among utilities to move towards a shorter weather normalization period. The vast majority of utilities use a rolling average that is updated annually.

There is international agreement among World Meteorological Organization (WMO) members to define “normal weather” as a 30-year period for which Canada is a member. A 30-year base normalizes short-term changes in weather patterns while capturing long term trends.

NB Power first implemented a rolling 30-year average in the 2011 to 2021 Load Forecast, and continues to revise normal weather criteria annually.

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“In-house or off-site training should be provided to the single load forecasting staff person. The staff member has only one year experience and would benefit from training in the areas of statistics, econometric modeling, and general load forecasting processes.”

-GDS Associates, Load Forecasting Audit. (2007) Complete

The Senior Load Forecaster has attended Itron’s annual Forecasters Conference. Dozens of utility load forecasters from North America attend this three to four day conference. All presentations and discussions directly relate to the load forecasting function. Topics such as: statistical modeling, weather adjustments, demand forecasting, are discussed. Our current Senior Load Forecaster has recently completed two statistics courses: Time Series Analysis & Applications, and Regression Analysis at the University of New Brunswick.

“Load forecast staff should develop a peak demand forecasting model that

provides for the quantification of key influences. The current load factor approach does not directly provide a means for determining the impacts of extreme weather conditions on peak demand.”

-GDS Associates, Load Forecasting Audit. (2007) Complete

A new demand model using energy and temperature was completed and implemented in 2011. The model was reviewed by GDS Associates prior to implementation in the last Load Forecast.

“Reliable economic projections from credible sources are available from few sources; however, NB Power should collect, to the extent possible, economic forecasts from sources in addition to current sources. Given the unique

circumstance of flat population and continued residential customer growth, it is important that NB Power secure a projection of number of households from a reliable third party. Global Insights and Moody’s are two alternative sources of

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-GDS Associates, Load Forecasting Audit. (2007) Complete

NB Power has been unable to find a third party forecast for the number of households or average household size in New Brunswick. In the forecast, the number of households is not used, but average household size is used to forecast water heating use. Beginning in 2013, the customer forecast is now based on a combination of an average household size forecast and a third party population forecast. The household size is forecast using a regression that captures historical trends. The provincial population forecast is based on the province of New Brunswick’s latest projections, and updated annually.

As a course of normal business, NB Power consults with Provincial staff and reviews various published reports to support the economic assumptions used in the Load Forecast.

“Provide greater clarification in the electric load forecast report on the key influences on system requirements. Describe why the forecast is as projected. For instance, projected growth rates in the 2005- 2015 forecast are considerably higher in the last five years of the forecast period than in the initial five years, which should raise questions from evaluating parties. The reasons for such a pattern of growth should be clearly discussed in the report, preferably the

Executive Summary or Forward section. Present high and low range projections to demonstrate possible outcomes in addition to the base case forecast. Include a section in the report that summarizes all the key assumptions made regarding model inputs. Prepare an appendix that contains the statistical output for the econometric models. The monthly forecast variance analysis and reporting should be expanded to include peak demand in addition to energy sales.”

-GDS Associates, Load Forecasting Audit. (2007) Complete

The two most recent Load Forecast reports include a more detailed discussion on forecast results, as well as statistical model details and a high and low forecast. Monthly forecast variances for demand are now being incorporated into our month-end analysis.

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is that the near-term will track the immediate past more closely than long-term history. Short-term models based on 24 to 48 months of historical data may provide more accurate forecasts over the first 12 to 24 months of the forecast horizon than do annual models based on an extended history.”

-GDS Associates, Load Forecasting Audit. (2007) Outstanding

This recommendation will be completed after the backcasting exercise as information from this work may be helpful in determining the value of short-term forecasting models.

“As air conditioning market share increases, load forecasting staff should periodically analyze the impacts on energy sales and peak demand. Once the impacts are significant, the residential and commercial energy sales models should be updated to include an air conditioning parameter.”

-GDS Associates, Load Forecasting Audit. (2007) Complete, but ongoing

An initial review of load data, compared to temperature indicates only a small air

conditioning load exists in New Brunswick, mainly in July and August. Since NB Power is a winter peaking system, air conditioning load has no impact on the winter peak forecast.

The 2013 Energy Planning Survey results show that air conditioners, both window and central units, are gaining popularity. To quantify the air conditioning load, more

summer hourly load and temperature data is needed. This data continues to be collected each month. In addition, daily cooling-degree-days are now being tracked. A model implementing these factors is currently under development.

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Forecast Report for PeakDemand Model Details

Dynamic regression

Regression(4 regressors, 0 lagged errors)

Term Coefficient Std. Error t-Statistic Percentile

AvgNonInd 1.1706 0.058507 20.007 1

AvgInd 0.78661 0.093281 8.4327 1

Temperature -9.3677 1.2704 -7.3736 1

_CONST 416.81 78.326 5.3215 1

Within-Sample Statistics

Sample size 120 No. parameters 4 Mean 2084.02 Std. deviation 501.9 Adj. R-square 0.97 Durbin-Watson 2.22 Ljung-Box(18) 29.7 P=0.96 Forecast error 81.12

BIC 86.38 MAPE 3.11

MAD 63.57

Variable specification test battery

Term Test Value Percentile

AvgNonInd[-1] 0.654 0.581

AvgInd[-1] 0.11 0.26

Temperature[-1] 0.167 0.317

_TREND 2.478 0.885

Dynamics tests successful.

Dynamics test battery

Variable specification tests successful.

Forecast Data

Date 2.5 Lower Forecast Quarterly Annual 97.5 Upper

2014-Apr 1908.72 2065.03 2221.34 2014-May 1590.68 1746.99 1903.3 2014-Jun 1392.52 1548.83 5360.85 1705.14 2014-Jul 1278.11 1434.42 1590.73 2014-Aug 1292.21 1448.52 1604.83 2014-Sep 1452.72 1609.03 4491.96 1765.34 2014-Oct 1690.68 1846.99 2003.3 2014-Nov 2064.93 2221.24 2377.55 2014-Dec 2490.66 2646.97 6715.21 25042.03 2803.28 2015-Jan 2710.99 2867.3 3023.61 2015-Feb 2674.3 2830.61 2986.92 2015-Mar 2317.12 2473.43 8171.35 2629.74 Total 24739.37 Average 2061.61

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References

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