Data Description

Five general data and variable categories were used in the development of the Customer Segment forecasts; these categories are described below. The actual variables used in each customer segment regression model are defined along with each model.

a) Customer Segment Data

Historical monthly billing data were collected from Company records for each Division by customer class for the period January 2009 through March 2014, including demand, measured in therms or ccf; number of customers; and bundled revenue by rate class for each Division. This data was aggregated into the respective Customer Segments by combining customer classes with similar usage patterns. For example, the C&I Low Load Factor Customer Segment is comprised of C&I customers that are served under one of Northern’s high winter use rate schedules, whereas the C&I High Load Factor Customer Segment is comprised of C&I customers that are served under one of Northern’s low winter use rate schedules. The customer classes that comprise each Customer Segment for each Division are shown in the table below:

106

Regression analysis is concerned with relating a dependent (or response) variable with a set of independent (or predictor) variables; a common use of regression analysis is to allow for predictions of the dependent variable based on predicted values of the independent variables.

107

IV-52

Table IV-4: Customer Segment Definitions Class

ME

Class

NH Class Description Customer Segment

R-2 R-5,R-10 Residential Heating Residential Heating

R-1 R-6,R-11 Residential Non-Heating Residential Non-Heating

G-40 G-40 C&I Sales Low Annual Use, High Peak Period/ Winter Use

C&I Low Load Factor G-41 G-41 C&I Sales Medium Annual Use, High Peak Period/ Winter Use

G-42 G-42 C&I Sales High Annual Use, High Peak Period/ Winter Use T-40 T-40 C&I Transport Low Annual Use, High Peak Period/ Winter Use T-41 T-41 C&I Transport Medium Annual Use, High Peak Period/ Winter Use T-42 T-42 C&I Transport High Annual Use, High Peak Period/ Winter Use G-50 G-50 C&I Sales Low Annual Use, Low Peak Period/ Winter Use

C&I High Load Factor G-51 G-51 C&I Sales Medium Annual Use, Low Peak Period/ Winter Use

G-52 G-52 C&I Sales High Annual Use, Low Peak Period/ Winter Use T-50 T-50 C&I Transport Low Annual Use, Low Peak Period/ Winter Use T-51 T-51 C&I Transport Medium Annual Use, Low Peak Period/ Winter Use T-52 T-52 C&I Transport High Annual Use, Low Peak Period/ Winter Use

SPC SPC Special Contracts Special Contracts

b) Weather Variables

Historical daily effective degree day (“EDD”) data for the 30 year historical period of November 1, 1983 through March 31, 2014 was utilized by the Company for the Maine Division (measured at the Portland, Maine weather station) and for the New Hampshire Division (measured at the Portsmouth, New Hampshire weather station). Daily EDD data were calculated based on averages of 24 hours of temperature and wind speed data for each Gas Day, which begins and ends at 10 AM each day.108

Firm natural gas demand is heavily dependent on weather conditions, as measured by EDD, which vary on a daily, monthly, and annual basis. Customer segment demand is measured on a billing month basis whereby approximately equal numbers of Northern’s customer meters are read every

108

The Company used the average temperature and wind speeds to produce daily EDD for each Gas Day for Division according to the following formula:

If avg. temperature < 65, EDD = (65 – avg. temperature) * (1 + (avg. wind speed / 100)) If avg. temperature > 65, EDD = 0

IV-53

working day of the month. As a result, most of the consumption recorded in the first billing cycles of a billing month relates to consumption that occurred in the prior calendar month, and most of the consumption recorded in the last billing cycles of a billing month relates to consumption that occurred in the same calendar month. Thus, consumption in each billing month is affected by EDD observed in both the same month and the prior month. A billing month EDD variable was developed to align the pattern of observed daily EDD to the billing cycle pattern each month. The methodology used to calculate billing cycle monthly EDD data is illustrated in the “Calculation of Billing Cycle EDD Variable” section of Appendix 1.

Historical billing cycle monthly EDD values for the period January 2009 through March 2014 were calculated and used to measure the effect of temperature on natural gas use in the Customer Segment use per customer regression models.109 Historical EDD values were also used to develop normal year and design year EDD patterns, as well as design day EDD levels, for each Division. The normal year and design year EDD were applied to the customer segment models to estimate normal year and design year demand. These EDD patterns are described further and presented in the Normal Year Throughput and Design Year Throughput sections that follow.

c) Economic and Demographic Variables

Economic activity and demographic data to be used in the regression analysis were acquired from IHS Global Insight, Inc. (“Global Insight”). Global Insight provided separate data series for the Maine and New Hampshire Divisions. Historical data was obtained for the period of January 2009 through March 2014 (the “historical period”) and forecast data was provided from April 2014 through October 2040. The data include fuel prices, employment, income, population, and housing statistics specific to counties that Northern serves in Maine and New Hampshire, as well as state level data for Maine and New Hampshire. The Maine Division variables are derived from data for the Lewiston- Auburn and Portland-South Portland metropolitan areas since these areas correspond most closely to Northern’s Maine service territory. The New Hampshire Division variables were derived from data for Rockingham County and Strafford County since these counties most closely correspond to Northern’s New Hampshire service territory. Table IV-5 summarizes the Global Insight economic and demographic data evaluated while developing the Customer Segment models.

109

IV-54

Table IV-5: Global Insight Variables Lewiston-Auburn and Portland- South Portland metropolitan areas for Maine Division: Rockingham and Strafford Counties for New Hampshire Division:

Total Population (Thousands) Households (Thousands) Housing Stock (Units) Housing Starts, Total Private

Employment, Total Non-farm (Thousands) Employment, Non-manufacturing (Thousands)

Employment, Total Service Providing Private Employment (Thousands) Employment, Manufacturing (Thousands)

State of Maine and State of New Hampshire:

Average Retail Price of Natural Gas, Residential ($/MMBtu) Average Retail Price of Natural Gas, Commercial, ($/MMBtu) Average Retail Price of Natural Gas, Industrial, ($/MMBtu)

d) Natural Gas Price Variable

Because economic theory suggests that price is likely to influence demand, natural gas price variables specific to each Customer Segment were developed for the use per customer models. Historical natural gas prices for each Customer Segment and each Division were derived from Company billing data. Forecasted prices, also specific to each Customer Segment and each Division, were developed using price forecasts prepared by Global Insight, together with the Company historical data. The methodology used to develop the natural gas price variables is described in “Calculation of Natural Gas Price Variables” in Appendix 1.

e) Other Variables

The following adjustments were made, and additional variables were developed, for use in the Customer Segment models:

 Monthly indicator or trend variables were created to account for any systematic changes in the number of customers or use per customer that were a function of time.

 Dummy variables (or indicator variables) were created to represent time-related events. These time-related dummy variables equal 1 when that specific time-related event occurs, and equal 0 at other times.

 Interactive variables were created by multiplying dummy variables and selected independent variables to determine if the relationships between the dependent variable and the selected independent variables changed as a result of time-related events.

IV-55

 Variables with time lags were created from several of the data series to test whether the impact of that variable on the number of customers or use per customer was not immediate, but instead is delayed.