Efland Direct Testimony - Indiana · VERIFIED DIRECT TESTIMONY OF AMY EFLAND 1 Q1. Please state...

Petitioner’s Exhibit No. 16 Northern Indiana Public Service Company LLC

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VERIFIED DIRECT TESTIMONY OF AMY EFLAND

Q1. Please state your name, business address and title. 1

A1. My name is Amy Efland. My business address is 290 W. Nationwide Blvd., 2

Columbus, Ohio 43215. I am currently the Manager of Demand 3

Forecasting for NiSource Corporate Services Company. 4

Q2. On whose behalf are you submitting this direct testimony? 5

A2. I am submitting this testimony on behalf of Northern Indiana Public Service 6

Company LLC (“NIPSCO”). 7

Q3. Please describe your educational and employment background. 8

A3. I attended Earlham College where I earned a Bachelor of Arts Degree in 9

Economics and Miami University where I earned a Master of Arts Degree 10

in Economics. From 1997 to 2002, I worked as a forecast analyst for Cinergy, 11

assisting with the production of the gas and electric long‐term forecasts of 12

customers, energy consumption and peak demand for the Cinergy (Public 13

Service Indiana, Union Light, Heat & Power, and Cincinnati Gas & Electric) 14

territories. I was promoted to Lead Analyst in 2002, a position I held until 15

45159

loldhamNew Stamp


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I left Cinergy in 2005. From 2005 to 2006, I worked as a Senior Forecasting 1

Analyst with Limited Brands/Victoria’s Secret Direct. I provided analysis 2

and recommendations surrounding circulation levels of catalogues and 3

assisted with catalogue messaging relating to marketing offers. From 2006 4

to 2008, I worked as a Senior Marketing Analyst for JP Morgan Chase where 5

I was responsible for the development of test designs for consumer and 6

business banking marketing programs. I joined NiSource Inc. in 2008 as a 7

Senior Forecast Analyst. In 2014, my title was changed to Lead Forecasting 8

Analyst reflecting the same responsibilities I held while a Senior Forecast 9

Analyst. I accepted my current position of Manager Demand Forecasting 10

on November 1, 2017. 11

Q4. What are your responsibilities as Manager Demand Forecasting? 12

A4. As Manager of Forecasting, my principal responsibilities include the 13

development of the short‐range and long‐range forecasts of customers, 14

energy consumption and peak demand for all NiSource distribution 15

companies including NIPSCO. I also manage other business‐related 16

analyses and forecasts. 17


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Q5. Have you previously testified before this or any other regulatory 1

commission? 2

A5. Yes. I testified before the Indiana Utility Regulatory Commission 3

(“Commission”) in NIPSCO’s gas rate case in Cause No. 44988. I also 4

testified related to weather normalization and customer usage trends before 5

the Pennsylvania Public Utility Commission in Docket Nos. R‐2009‐6

2149262, R‐2010‐2215623, R‐2012‐2321748, R‐2014‐2406274, R‐2015‐2468056, 7

R‐2016‐2529660, the Kentucky Public Service Commission in Case No. 2009‐8

00141, and before the Massachusetts Department of Public Utilities in 9

support of the 2017 forecast and supply plan for Columbia Gas of 10

Massachusetts in D.P.U. 17‐166. 11

Q6. Are you sponsoring any attachments to your testimony in this Cause? 12

A6. Yes. I am sponsoring Attachment 16‐A and Attachment 16‐B, both of which 13

were prepared by me or under my direction and supervision. 14

Q7. What is the purpose of your testimony? 15

A7. The purpose of my testimony is to explain how the historic test year 16

kilowatt hour (“kWh”) consumption was adjusted to reflect the kWh 17

consumption that would have occurred had weather been normal and why 18


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this is the appropriate level to use for ratemaking. I also explain the base 1

load temperature‐sensitive load normalization procedure. Finally, I 2

explain the forecast method used to derive the Forward Test Year (period 3

beginning January 1, 2019 and ending December 31, 2019) customers and 4

volume and propose an adjustment to align the forecast with the definition 5

of normal weather proposed for ratemaking purposes. 6

Weather Normalization 7

Q8. Is NIPSCO proposing an adjustment to reflect normal weather? 8

A8. Yes. 9

Q9. Why is an adjustment to electric utility revenues to normalize weather 10

appropriate? 11

A9. Electric rates include charges that are tied to kWh consumption. These 12

charges are developed by dividing required revenue by units of 13

consumption from the test year period. Because these charges are 14

dependent on energy consumption, variations in weather would affect the 15

costs allocated to each unit of energy. For example, calculating these 16

charges based on a test year with abnormally low consumption would 17

result in a higher allocation of costs to each unit of energy consumed; in 18


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subsequent years when normal levels of consumption are experienced, the 1

rates established would lead to an over‐collection of revenues. 2

Q10. Please describe NIPSCO’s weather normalization procedure. 3

A10. NIPSCO used the base load/temperature‐sensitive load normalization 4

procedure approved by the Commission in its 44688 Order. This procedure 5

identifies a level of energy units per customer that is not dependent on 6

weather (base load) and subtracts that from total energy units per customer 7

to derive temperature‐sensitive energy units per customer. The 8

temperature‐sensitive energy units per customer were then multiplied by 9

the ratio of normal cooling degree days (“CDD”) to actual CDD to obtain 10

normal temperature‐sensitive energy units per customer. The normal 11

temperature‐sensitive energy units per customer is then added to the base 12

load energy units per customer to arrive at the normal total energy units 13

per customer, which is then multiplied by the number of customers to yield 14

the normal units for the month. NIPSCO used three sources of data in its 15

analysis: (1) monthly billing data from its actual billing records, (2) NOAA1 16

National Weather Service Weather Stations data for actual temperatures 17

1 National Oceanic and Atmospheric Administration


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used to calculate CDD, and (3) the 30‐year average of temperature data 1

from 1988‐2017 using NOAA’s National Weather Service temperatures. 2

The weather normalization procedure assumed there was no temperature‐3

sensitive load in the month of May; therefore, the daily volumes recorded 4

in the month of May divided by the number of customers represented the 5

customer daily base load volume. NIPSCO used the following weather 6

normalization procedure for the Cooling Season months of June, July, 7

August and September: 8

1. The base load per customer was calculated for each month by 9

multiplying the customer daily base load by the number of days in 10

each billing month. 11

2. The temperature‐sensitive load per customer for each month was 12

calculated by subtracting the base load per customer from the total 13

load per customer. 14

3. The normal temperature‐sensitive load per customer was calculated 15

by multiplying the temperature‐sensitive load per customer by the 16

ratio of normal CDD to actual CDD. 17


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4. The normal load per customer was calculated by adding the base 1

load per customer to the normal temperature‐sensitive load per 2

customer. 3

5. The normal volume was then calculated by multiplying the normal 4

load per customer by the customer count. 5

Q11. What is CDD? 6

A11. CDD is a unit used to relate a day’s temperature to the energy demands of 7

air conditioning. CDD are calculated by subtracting the balance point 8

temperature, 65, from a day’s average temperature. For example, if the 9

day’s high temperature and low temperature ranged from 90°F to 70°F, and 10

the average temperature was 80°F, CDD is calculated by subtracting 65 11

from 80, which results in 15 CDD for that day. 12

Q12. What is the definition of a Balance Point Temperature? 13

A12. Balance Point Temperature is the average daily temperature used as a 14

reference level for calculating CDD. 15

Q13. Has the normalization procedure been updated since the 44688 Rate 16

Case? 17


Page 8 A13. Yes. NIPSCO has updated its definition of normal weather. In 2015, the 1

Company defined normal weather as the 30‐year average ending in 2014. 2

In this filing, NIPSCO has defined normal weather as the 30‐year average 3

ending in 2017, which is an update to the normal CDDs calculated by 4

NOAA’s National Weather Service, which uses data for the 30‐year period 5

ended 2010. Given its 10‐year cycle for updating a large set of climate 6

variables, NOAA’s National Weather Service will next issue new normal 7

CDDs for the period 1991‐2020. In the 44688 Rate Case, normal weather 8

was defined as the 30‐year average ending 2014. 9

Q14. Which weather stations were used to determine the weather throughout 10

NIPSCO’s service territory? 11

A14. Weather stations in Valparaiso, South Bend and Fort Wayne were weighted 12

by the number of residential customers assigned to those stations to 13

represent the NIPSCO service territory. 14

Q15. What are the volumetric results of NIPSCO’s weather normalization? 15

A15. With actual CDD lower than normal by 6%, the test year was cooler than 16

normal, and usage for the adjusted rates was adjusted up by 0.8% to reflect 17

normal weather. NIPSCO Witness Westerhausen uses the weather 18


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adjusted volumes to calculate pro‐forma expense and revenue levels. The 1

weather data used to develop the CDD for the adjustment is summarized 2

in Table 3. 3

Table 3 4

5 6

Q16. How can CDD be used to compare the current Cooling Season to a past 7

Cooling Season? 8

A16. CDD are accumulated daily so that a month’s CDD is the sum of the daily 9

CDD. A high level of CDD indicates many days with high temperatures 10

and is associated with a high level of kWh consumption. The level of CDD 11

can be compared from month‐to‐month or summer‐to‐summer to indicate 12

the relative amount of hot weather. The 702 CDD during the Cooling 13

Season of the test year is lower than the average of 747 CDD during the 14

Cooling Season for the 30‐year period 1988 to 2017. 15

Weighted Sum of CDD for NIPSCOCDD by Weather Station Valparaiso South Bend Fort Wayne Weighted Sum

Actual Month Valparaiso South Bend Fort Wayne 63% 19% 18%Jun 152 106 125 96 20 23 138Jul 267 196 233 168 37 42 247Aug 217 168 197 137 32 35 204Sep 126 89 94 79 17 17 113

Season 762 559 649 480 106 117 702

Normal MonthJun 118 118 127 74 22 23 120Jul 237 238 252 149 45 45 240Aug 230 235 235 145 45 42 232Sep 154 160 151 97 30 27 155

Season 739 751 765 466 143 138 747

Actual versus Normal Difference -6%


Page 10 Q17. Please discuss electric consumption due to lower than average CDD 1

during the test year. 2

A17. In simple terms, the data indicates that during the Cooling Season, either 3

the days were cooler than average or there were more cool days than 4

average resulting in customers operating their air conditioners less than 5

normal and thereby consuming less electric energy than normal. I can 6

conclude, based on this data, that the amount of electric energy consumed 7

during the test year is abnormally low and that the test year does not reflect 8

a representative level for ratemaking purposes. 9

Q18. Does the data show that kWh consumption varies with CDD? 10

A18. Yes, it does. The relationship between CDD and kWh consumption per 11

customer for the Cooling Season during the period 2008 to 2017 for the 12

residential class is depicted in the following graph: 13


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1

The graph shows a strong correlation between CDD and electric energy 2

consumed. Additionally, the graph also shows that the 702 CDD and the 3

kWh consumed during the Cooling Season of the test year are low when 4

compared to the average of 747 CDD. The 2017 Cooling Season is the 14th 5

lowest Cooling Season for the 30 years ended 2017. 6

Q19. What do you conclude from this data? 7

A19. A strong relationship between kWh consumption and CDD, combined with 8

CDD less than the level observed in 53 percent of the 30 years ended 2017, 9

leads to the conclusion that kWh consumption should be normalized for 10

weather so that it is stated at a level representative for ratemaking purposes. 11


Page 12 Results of Weather Normalization 1

Q20. What are the results of your weather normalization? 2

A20. The weather normalization adjustment reduces volume in two (2) and adds 3

volume in two (2) of the four (4) months in the Cooling Season. The net 4

effect is an increase of 59,151,141 kWh or 0.8% of the annual volume for the 5

adjusted rates. 6

Q21. Have you demonstrated the details of the weather normalization 7

procedure? 8

A21. Yes, the details are shown in Attachment 16‐A. 9

Forward Test Year 10

Q22. How were the projections for the Forward Test Year developed? 11

A22. The customer and energy projections utilized in the budget are developed 12

by NIPSCO’s Forecasting Group with input from Major Accounts, New 13

Business, and Financial Planning. The Forecasting Group is responsible for 14

most concepts, with New Business providing projections of residential and 15

commercial new customer additions. Major Accounts provides budget 16

projections for large industrial customers, while Financial Planning 17

supplies an outlook for all other small industrial customers utilized in the 18

budget. 19


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The methodology used to estimate both customer and usage is presented in 1

the summary below. Price information included in the models is from the 2

Company’s databases, and average saturation and efficiency data is from 3

Itron Inc., a national utility consulting firm. The economic variables and 4

deflator information are from IHS Global Insight, Inc., a data consultant, 5

and weather data is provided by Schneider Electric, a weather consulting 6

service. The energy usage models utilize both Heating Degree Days 7

(“HDDs”) and CDDs when estimating the Forward Test year level of usage. 8

Q23. What is HDD? 9

A23. HDD is a unit used to relate a day’s temperature to the energy demands of 10

primarily space heating and electric water heating. HDD are calculated by 11

subtracting the day’s average temperature from the balance point 12

temperature of 65. For example, if the day’s high temperature and low 13

temperature ranged from 20°F to 40°F, and the average temperature was 14

30°F, HDD is calculated by subtracting 30 from 65, which results in 35 HDD 15

for that day. 16

Q24. Please describe the forecast methodology used to develop the budget. 17

A24. The methodology is summarized as follows: 18


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Residential and Commercial Customers

Residential customer projections are developed utilizing the new 1

customer additions provided by NIPSCO’s New Business Team. 2

New Business identifies potential new projects, both rural extension 3

and non‐rural growth projects, monitors potential projects being 4

engineered, residential single family and multi‐family construction 5

permit applications, and outstanding electric service requests. 6

Subsequent years are developed based on the team’s knowledge of 7

the business climate, new construction activity and interviews with 8

active builders and developers. Customer attrition is estimated to be 9

at a typical historical level by the Forecasting Group. Total 10

residential customers are calculated by incorporating the new 11

customer outlook, existing customers and the historic attrition rate. 12

Commercial customers are estimated utilizing an econometric model 13

that incorporates local population and real gross county product. As 14

with residential, the New Business team provides new customer 15

projections that are incorporated into the total customer outlook. 16

Attrition is reflected at a historical rate. 17


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Residential and Commercial Usage

The residential use per customer model is estimated using an 1

econometric model incorporating the residential price of electricity, 2

appliance saturations and efficiencies as defined in an end use 3

variable supplied by Itron, Inc., real per capita income, HDD and 4

CDD. Total residential volume is calculated by multiplying the total 5

customer projections by the usage per customer outlook. 6

Commercial usage is estimated using a total energy consumption 7

model. Commercial total energy is a function of the commercial 8

customer count, employment, commercial electric price, HDD and 9

CDD. 10

Industrial Volume

The Industrial Energy Forecast Model projects the expected level of 11

industrial energy sales in NIPSCO’s service territory based on 12

individual discussions with its largest industrial customers, recent 13

historical industrial sales trends, and specific industry trends. 14

Accordingly, the industrial energy outlook models contains 15

individual projections for the major industrial account customers. 16


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The industrial sales projections also integrate a sales outlook for the 1

remaining industrial accounts (identified as Other Industrial). This 2

portion of the NIPSCO electric budget is based primarily on 3

historical data (billed volume) from the past six years with greater 4

consideration given to use for the most recent year. Annual and 5

monthly volumes were analyzed ‐ min, max, and averages were 6

calculated. Historical trends, if any, were identified and are reflected 7

in the projections. 8

Public Authority, Railroads, Company Use, Street Lighting, Losses

Public authority, railroads, Company use and losses are all based on 9

both current usage levels and anticipated future trends. The street 10

lighting model utilizes an econometric approach to model future 11

street lighting usage. 12

Peak

NIPSCO uses an econometric model to project future peak demand 13

on its system. The model incorporates residential, commercial, and 14

industrial energy levels, cooling degrees (summer) and heating 15

degrees (winter) at peak hour, and the level of relative humidity at 16

peak hour. The model also accounts for recent historical load factor 17


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levels and patterns associated with NIPSCO’s large industrial 1

customers. 2

Q25. What definition of weather normal is reflected in the 2018 and 2019 3

Budget? 4

A25. The budget was developed using the definition of normal for both CDD 5

and HDD of 35 years ended 2010. 6

Q26. What definition of weather normal is reflected in the forecast? 7

A26. The forecast was developed using the definition of normal for both CDD 8

and HDD of 30 years ended 2017. This is consistent with the definition used 9

in the normalization of the Historic Test Year. The Forward Test Year 10

forecast was provided to NIPSCO Witness Westerhausen as shown on 11

Attachment 16‐B and utilized as an input in Adjustment REV 1A‐19R as 12

shown on Petitioner’s Exhibit No. 4, Attachment 4‐C‐S2, REV 1A19R. 13

Q27. Please discuss the past performance of the forecast methodology used in 14

this case. 15

A27. Forecasts are updated annually with the most current data. An internal 16

review of the forecast performance occurs on a regular basis. Variances are 17

calculated and assessed in order to measure accuracy. For the last five 18


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years, the average annual one year weather normalized variance for the 1

residential model is 1.8%. For commercial, the average one year variance 2

of the forecast is 1.5%. The more volatile industrial forecast average 3

variance was 3.8%. 4

Q28. Does this conclude your prefiled direct testimony? 5

A28. Yes. 6

NIPSCO Normalization For 12 Months Ending December 2017

711 711 721 721 723 723 724 724 726 726Actual KWH 1 3 4 5 4 5 4 5 4 5 Total

1 314,286,192 291,522 115,863,076 790,478 86,180,525 179,617 44,770,194 13,539,786 29,768,903 20,965,252 626,635,545 2 258,362,673 239,244 108,686,385 979,962 82,002,086 190,479 43,261,612 12,805,220 29,216,270 19,115,410 554,859,341 3 231,939,486 227,394 101,898,881 1,087,854 79,603,287 177,703 42,400,381 12,134,969 29,089,958 19,753,626 518,313,539 4 223,091,374 207,397 102,055,297 1,249,131 80,095,767 171,119 44,295,207 11,967,893 30,191,925 19,122,206 512,447,316 5 193,919,857 180,923 93,685,327 948,055 76,573,396 178,017 42,574,956 11,665,266 28,966,407 19,908,198 468,600,402 6 287,925,637 249,645 118,035,707 1,088,684 93,210,205 217,855 51,070,197 14,210,663 35,300,053 22,151,782 623,460,428 7 366,786,920 316,635 127,242,033 1,067,344 96,116,467 196,153 51,454,406 14,316,579 37,679,053 24,368,668 719,544,258 8 360,715,160 298,234 130,066,513 1,183,715 96,520,908 176,483 49,890,630 13,406,691 34,991,469 23,236,592 710,486,395 9 297,142,847 264,054 122,997,463 1,303,110 95,590,892 234,770 52,885,373 15,023,615 37,023,316 22,630,690 645,096,130

10 263,136,683 264,575 112,184,610 1,416,033 87,819,707 219,637 49,309,677 14,396,710 33,356,579 21,195,526 583,299,737 11 229,294,401 283,330 107,599,450 1,211,605 79,539,582 175,106 45,875,177 14,056,016 30,218,666 19,367,898 527,621,231 12 254,476,105 267,420 106,893,400 1,146,303 76,167,005 168,472 43,396,805 12,472,831 28,284,126 18,455,124 541,727,591

Annual 3,281,077,335 3,090,373 1,347,208,142 13,472,274 1,029,419,827 2,285,411 561,184,615 159,996,239 384,086,725 250,270,972 7,032,091,913

Normal KWH1 314,286,192 291,522 115,863,076 790,478 86,180,525 179,617 44,770,194 13,539,786 29,768,903 20,965,252 626,635,545 2 258,362,673 239,244 108,686,385 979,962 82,002,086 190,479 43,261,612 12,805,220 29,216,270 19,115,410 554,859,341 3 231,939,486 227,394 101,898,881 1,087,854 79,603,287 177,703 42,400,381 12,134,969 29,089,958 19,753,626 518,313,539 4 223,091,374 207,397 102,055,297 1,249,131 80,095,767 171,119 44,295,207 11,967,893 30,191,925 19,122,206 512,447,316 5 193,919,857 180,923 93,685,327 948,055 76,573,396 178,017 42,574,956 11,665,266 28,966,407 19,908,198 468,600,402 6 279,169,434 243,939 116,592,740 1,088,684 92,489,710 217,855 50,865,527 14,088,701 34,487,883 22,099,403 611,343,8777 362,405,754 313,248 126,537,356 1,067,344 95,796,002 196,153 51,332,042 14,273,721 37,412,174 24,272,577 713,606,3708 381,982,382 312,887 134,295,178 1,183,715 98,492,973 176,483 50,530,745 13,545,977 35,805,424 23,639,332 739,965,0979 327,414,233 287,699 130,482,416 1,303,110 99,313,740 234,770 54,802,923 15,768,202 40,102,503 23,113,413 692,823,009

10 263,136,683 264,575 112,184,610 1,416,033 87,819,707 219,637 49,309,677 14,396,710 33,356,579 21,195,526 583,299,737 11 229,294,401 283,330 107,599,450 1,211,605 79,539,582 175,106 45,875,177 14,056,016 30,218,666 19,367,898 527,621,231 12 254,476,105 267,420 106,893,400 1,146,303 76,167,005 168,472 43,396,805 12,472,831 28,284,126 18,455,124 541,727,591

Annual 3,319,478,574 3,119,577 1,356,774,116 13,472,274 1,034,073,781 2,285,411 563,415,247 160,715,292 386,900,817 251,007,965 7,091,243,054

Difference1 - - - - - - - - - - - 2 - - - - - - - - - - - 3 - - - - - - - - - - - 4 - - - - - - - - - - - 5 - - - - - - - - - - - 6 (8,756,203) (5,706) (1,442,967) - (720,495) - (204,670) (121,962) (812,170) (52,379) (12,116,551) 7 (4,381,166) (3,387) (704,677) - (320,465) - (122,364) (42,858) (266,879) (96,091) (5,937,888) 8 21,267,222 14,653 4,228,665 - 1,972,065 - 640,115 139,286 813,955 402,740 29,478,702 9 30,271,386 23,645 7,484,953 - 3,722,848 - 1,917,550 744,587 3,079,187 482,723 47,726,879

10 - - - - - - - - - - - 11 - - - - - - - - - - - 12 - - - - - - - - - - -

Annual 38,401,240 29,204 9,565,974 0 4,653,954 0 2,230,632 719,053 2,814,092 736,993 59,151,141Annual % 1.2% 0.9% 0.7% 0.0% 0.5% 0.0% 0.4% 0.4% 0.7% 0.3% 0.8%Seasonal % 2.9% 2.6% 1.9% 0.0% 1.2% 0.0% 1.1% 1.3% 1.9% 0.8% 2.2%

Petitioner's Exhibit No. 16 Attachment 16-A

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NIPSCO Normalization Normal Weather 1988 2017 30 YearsRate 711

1Weather Adjustment Calculation

1 2 3 = 2/1 4 = 3/12 5 = 4 (May#) *12 6 = 3-5 7 = 5+(6*(11/10)) 8 = 1*7 9= 8-2 10 11 12

Actual Actual Actual Base Temperature Sensitive Normal Normal Weather CDD CDDCustomers KWH KWH/Cus KWH/Cus/Day KWH/Cus KWH/Cus KWH/Customer KWH Adjustment Actual Normal Days

2017 1 314,286,192 314,286,1922017 2 258,362,673 258,362,6732017 3 231,939,486 231,939,4862017 4 223,091,374 223,091,3742017 5 410,594 193,919,857 472 16.70 472 - 193,919,857 28.32017 6 410,789 287,925,637 701 21.77 537 163 680 279,169,434 -8,756,203 138 120 32.22017 7 409,320 366,786,920 896 28.86 518 378 885 362,405,754 -4,381,166 247 240 31.02017 8 410,134 360,715,160 880 29.27 502 378 931 381,982,382 21,267,222 204 232 30.02017 9 409,176 297,142,847 726 23.00 527 199 800 327,414,233 30,271,386 113 155 31.62017 10 263,136,683 263,136,6832017 11 229,294,401 229,294,4012017 12 254,476,105 254,476,105


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1 2 3 = 2/1 4 = 3/12 5 = 4 (May#) *12 6 = 3-5 7 = 5+(6*(11/10)) 8 = 1*7 9= 8-2 10 11 12

Actual Actual Actual Base Temperature Sensitiv Normal Normal Weather CDD CDDCustomers KWH KWH/Cus KWH/Cus/Day KWH/Cus KWH/Cus KWH/Customer KWH Adjustment Actual Normal Days

2017 1 291,522 291,5222017 2 239,244 239,2442017 3 227,394 227,3942017 4 207,397 207,3972017 5 269 180,923 673 23.78 673 - 180,923 28.32017 6 269 249,645 928 28.83 765 163 907 243,939 -5,706 138 120 32.22017 7 267 316,635 1,186 38.20 738 448 1,173 313,248 -3,387 247 240 31.02017 8 268 298,234 1,113 37.03 714 398 1,167 312,887 14,653 204 232 30.02017 9 267 264,054 989 31.32 751 238 1,078 287,699 23,645 113 155 31.62017 10 264,575 264,5752017 11 283,330 283,3302017 12 267,420 267,420


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1 2 3 = 2/1 4 = 3/12 5 = 4 (May#) *12 6 = 3-5 7 = 5+(6*(11/10)) 8 = 1*7 9= 8-2 10 11 12


2017 1 115,863,076 115,863,0762017 2 108,686,385 108,686,3852017 3 101,898,881 101,898,8812017 4 102,055,297 102,055,2972017 5 51,409 93,685,327 1,822 64.43 1,822 - 93,685,327 28.32017 6 51,580 118,035,707 2,288 71.09 2,074 214 2,260 116,592,740 -1,442,967 138 120 32.22017 7 51,181 127,242,033 2,486 80.07 2,000 486 2,472 126,537,356 -704,677 247 240 31.02017 8 51,273 130,066,513 2,537 84.42 1,936 601 2,619 134,295,178 4,228,665 204 232 30.02017 9 50,569 122,997,463 2,432 77.04 2,034 398 2,580 130,482,416 7,484,953 113 155 31.62017 10 112,184,610 112,184,6102017 11 107,599,450 107,599,4502017 12 106,893,400 106,893,400


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1 2 3 = 2/1 4 = 3/12 5 = 4 (May#) *12 6 = 3-5 7 = 5+(6*(11/10)) 8 = 1*7 9= 8-2 10 11 12


2017 1 86,180,525 86,180,5252017 2 82,002,086 82,002,0862017 3 79,603,287 79,603,2872017 4 80,095,767 80,095,7672017 5 3,214 76,573,396 23,825 842.30 23,825 - 76,573,396 28.32017 6 3,234 93,210,205 28,822 895.36 27,114 1,708 28,599 92,489,710 -720,495 138 120 32.22017 7 3,243 96,116,467 29,638 954.60 26,151 3,487 29,539 95,796,002 -320,465 247 240 31.02017 8 3,246 96,520,908 29,735 989.61 25,309 4,426 30,343 98,492,973 1,972,065 204 232 30.02017 9 3,218 95,590,892 29,705 940.88 26,592 3,113 30,862 99,313,740 3,722,848 113 155 31.62017 10 87,819,707 87,819,7072017 11 79,539,582 79,539,5822017 12 76,167,005 76,167,005


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1 2 3 = 2/1 4 = 3/12 5 = 4 (May#) *12 6 = 3-5 7 = 5+(6*(11/10)) 8 = 1*7 9= 8-2 10 11 12


2017 1 44,770,194 44,770,1942017 2 43,261,612 43,261,6122017 3 42,400,381 42,400,3812017 4 44,295,207 44,295,2072017 5 231 42,574,956 184,307 6515.91 184,307 - 42,574,956 28.32017 6 236 51,070,197 216,399 6722.46 209,750 6,649 215,532 50,865,527 -204,670 138 120 32.22017 7 233 51,454,406 220,834 7112.76 202,303 18,531 220,309 51,332,042 -122,364 247 240 31.02017 8 231 49,890,630 215,977 7187.82 195,788 20,189 218,748 50,530,745 640,115 204 232 30.02017 9 232 52,885,373 227,954 7220.27 205,717 22,238 236,219 54,802,923 1,917,550 113 155 31.62017 10 49,309,677 49,309,6772017 11 45,875,177 45,875,1772017 12 43,396,805 43,396,805


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1 2 3 = 2/1 4 = 3/12 5 = 4 (May#) *12 6 = 3-5 7 = 5+(6*(11/10)) 8 = 1*7 9= 8-2 10 11 12


2017 1 13,539,786 13,539,7862017 2 12,805,220 12,805,2202017 3 12,134,969 12,134,9692017 4 29 11,967,893 11,967,8932017 5 29 11,665,266 402,251 14220.98 402,251 - 11,665,266 28.32017 6 29 14,210,663 490,023 15222.60 457,780 32,243 485,817 14,088,701 -121,962 138 120 32.22017 7 29 14,316,579 493,675 15900.58 441,528 52,148 492,197 14,273,721 -42,858 247 240 31.02017 8 29 13,406,691 462,300 15385.57 427,307 34,993 467,103 13,545,977 139,286 204 232 30.02017 9 29 15,023,615 518,056 16409.00 448,977 69,079 543,731 15,768,202 744,587 113 155 31.62017 10 28 14,396,710 14,396,7102017 11 29 14,056,016 14,056,0162017 12 12,472,831 12,472,831


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1 2 3 = 2/1 4 = 3/12 5 = 4 (May#) *12 6 = 3-5 7 = 5+(6*(11/10)) 8 = 1*7 9= 8-2 10 11 12


2017 1 29,768,903 29,768,9032017 2 29,216,270 29,216,2702017 3 29,089,958 29,089,9582017 4 30,191,925 30,191,9252017 5 144 28,966,407 201,156 7111.56 201,156 - 28,966,407 28.32017 6 127 35,300,053 277,953 8634.64 228,925 49,029 271,558 34,487,883 -812,170 138 120 32.22017 7 128 37,679,053 294,368 9481.17 220,797 73,571 292,283 37,412,174 -266,879 247 240 31.02017 8 136 34,991,469 257,290 8562.75 213,686 43,605 263,275 35,805,424 813,955 204 232 30.02017 9 128 37,023,316 289,245 9161.60 224,522 64,722 313,301 40,102,503 3,079,187 113 155 31.62017 10 33,356,579 33,356,5792017 11 30,218,666 30,218,6662017 12 28,284,126 28,284,126


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1 2 3 = 2/1 4 = 3/12 5 = 4 (May#) *12 6 = 3-5 7 = 5+(6*(11/10)) 8 = 1*7 9= 8-2 10 11 12


2017 1 20,965,252 20,965,2522017 2 19,115,410 19,115,4102017 3 19,753,626 19,753,6262017 4 19,122,206 19,122,2062017 5 25 19,908,198 796,328 28153.01 796,328 - 19,908,198 28.32017 6 24 22,151,782 922,991 28672.79 906,259 16,732 920,808 22,099,403 -52,379 138 120 32.22017 7 24 24,368,668 1,015,361 32703.35 874,084 141,277 1,011,357 24,272,577 -96,091 247 240 31.02017 8 24 23,236,592 968,191 32221.90 845,931 122,260 984,972 23,639,332 402,740 204 232 30.02017 9 24 22,630,690 942,945 29867.05 888,831 54,115 963,059 23,113,413 482,723 113 155 31.62017 10 21,195,526 21,195,5262017 11 19,367,898 19,367,8982017 12 18,455,124 18,455,124


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NIPSCO Electric Rate Case Forecast - 30 Year NormalNIPSCO MWH

Year Residential Commercial2019 3,414,463 3,906,221

NIPSCO Electric Forecast - 35 Year Normal NIPSCO MWH

Year Residential Commercial2019 3,419,840 3,910,422

Adjustment - MWHYear Residential Commercial Total 2019 (5,377) (4,201) (9,577)

Petitioner's Exhibit No. 16 Attachment 16-B

Attachment 16-A.pdfSummary711-1711-3 721-4723-4724-4724-5726-4726-5

Efland Direct Testimony - Indiana · VERIFIED DIRECT TESTIMONY OF AMY EFLAND 1 Q1. Please state...

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