Load Forecast FY2020-FY2034

Load Forecast

FY2020-FY2034

Table of ContentsAbout Columbia Water & Light 3

Organization 3

Service Territory, Transmission and Distribution System 3

Power Supply 3

Methodology 3

Explanatory Variables 4

Model Selection 5

Energy Forecast 5

Monthly Energy Forecast 5

Accuracy of FY18 Energy Forecast 7

Non-Coincident Peak Forecast 8

Monthly Non-Coincident Peak Forecast 8

Effects of Weather on the Forecast 9

Accuracy of FY18 NCP Forecast 10

MISO Zone 5 Coincident Peak Forecast 11

MISO System Coincident Peak Forecast 12

Appendix 13

I. Data 13

II. Regression Results 19

A. Energy Forecasts 19

B. Demand Forecasts 25

III. 2018 Forecast 31

A. Energy Forecast 31

B. Demand Forecast 32

IV. 2017 Forecast 33

A. Energy Forecast 33

B. Demand Forecast 34

References 35

2

About Columbia Water & Light

OrganizationThe City of Columbia, Missouri (the “City”) was incorporated in 1826 and became a Constitutional Charter City in 1949. TheCity is located near the center of the state in Boone County, and is joined by interstate with Kansas City, Missouri, to the westand St. Louis, Missouri, to the east. The City’s utility is Columbia Water & Light (“CWLD”), which was formed in 1904. The Cityis home to the University of Missouri, Columbia College and Stephens College.

The City is organized under the laws of the State of Missouri and operates under a Constitutional Charter approved by thecitizenry in 1949. The City is governed according to a Council–Manager form of government. The Mayor and six councilmembers are elected by the citizens of Columbia for three years with staggered terms of service. The City Council appoints aCity Manager to implement its policies and direct operations of City departments, including CWLD.

All decisions concerning CWLD are made by the City Council. Recommendations are made to the Council by the Water andLight Advisory Board (the “Board”). The Board is a five member advisory board created by the City Charter. Board membersserve overlapping four year terms. The Board’s powers and duties are solely advisory. The Board performs duties according tothe City Charter and Code of Ordinances of the City of Columbia, Missouri, and reports its findings and recommendations atleast annually to the residents of Columbia and the City Council.

Service Territory, Transmission and Distribution SystemCWLD serves retail customers inside and outside the limits of the City. The CWLD electric service area is approximately 60square miles. CWLD serves over 50,800 retail electric customers and over 49,900 retail water customers.

CWLD’s transmission system is comprised of approximately 70 miles of 161 kV lines and 69 kV lines. CWLD’s transmissionsystem is interconnected to transmission facilities owned by Associated Electric Cooperative, Ameren, City of Fulton, and theUniversity of Missouri.

As of September 30, 2019, CWLD’s distribution system consisted of over 933 circuit miles of overhead and underground lines.The City maintains eight distribution substations.

Power SupplyThe City provides power and energy to its customers from a combination of owned generating resources and purchasedpower. CWLD owns and operates the Columbia Municipal Power Plant which has one natural gas boiler and one gas turbine.The plant has a net rated capacity of 47.5 MW and the last unit was placed in service in 1970. In addition, a retired 22 MWsolid-fuel boiler is being evaluated as a potential for conversion to biomass. The plant is used primarily for system support andcontributed approximately 0.4% of system energy during the previous year.

CWLD also owns 3 MW of landfill gas generation. This facility began operation in June 2009 with two 1 MW generators. In2013, a third 1 MW generator began operation. The facility was built to allow the addition of another 1 MW of generation asthe landfill gas supply develops, which is currently being evaluated. In 2010, CWLD purchased a 25% (36 MW) interest in the144 MW natural gas fired Columbia Energy Center peaking facility. Columbia purchased the remaining 75% of ColumbiaEnergy Center in May 2011.

The majority of CWLD’s energy is purchased from market participants under long-term contracts. The City has long termpurchase agreements in place with the City of Sikeston, Missouri, MJMEUC, Associated Electric Cooperative, Nextera andAmeresco.

MethodologyCWLD’s energy and system peak forecasts are constructed using a series of monthly econometric models based on 25 years ofweather and economic data. Since the factors that affect energy usage vary from month to month, the variables chosen foreach model differ. Before starting model construction, CWLD examined the relationship between the dependent variables(MWH for energy and MW for system peak) and each explanatory variable by looking at its correlation coefficient. Typically, if

3

the squared correlation coefficient was greater than 0.2 then the explanatory variable was considered for inclusion in themonthly model. Skewness and nonlinearity in the data were also considered and the appropriate transformations needed toimprove the overall fit of the model were applied. Once the potential variables for inclusion were identified, models werecreated using multiple linear regression analysis to examine various combinations of the relevant explanatory variables.

Explanatory VariablesThe following is a complete list and description of the variables that were considered and included in at least one final monthlymodel. Each variable is listed as they appear in the data and regression results provided in the appendix.

Weather Variables● AVG_MAX_TEMP: The average of the daily maximum temperature for each day of the month (University of

Missouri-Columbia, 2019).● AVG_MIN_TEMP: The average of the daily minimum temperature for each day of the month (University of

Missouri-Columbia, 2019).● AVG_TEMP: The average temperature over the course of the month (University of Missouri-Columbia, 2019).● CDD: Stands for cooling degree days, and is the maximum of the average temperature for each day minus 65 and 0

(that is, it is always greater than or equal to zero) summed over each day of the month (University ofMissouri-Columbia, 2019).

● HDD: Stands for heating degree days, and is the maximum of 65 minus the average temperature for each day and 0(that is, it is always greater than or equal to zero) summed over each day of the month (University ofMissouri-Columbia, 2019).

● MAXCDD: The largest value of cooling degree days on an individual day during the month (University ofMissouri-Columbia, 2019).

● MAXHDD: The largest value of heating degree days on an individual day during the month (University ofMissouri-Columbia, 2019).

● MAX_HEAT_INDEX: The maximum heat index recorded over the course of the month (University ofMissouri-Columbia, 2019).

● MAX_TEMP: The maximum temperature recorded over the course of the month (University of Missouri-Columbia,2019).

● MIN_TEMP: the minimum temperature recorded over the course of the month (University of Missouri-Columbia,2019).

Economic Variables● EL_CUSTOMERS: The average of the daily total number of electric customers over the course of the month. This

variable consistently had the strongest correlation with demand and energy sales, and was used in every final model.● MED_INCOME_CONS (City of Columbia, 2018): Stands for Median Income in Constant Dollars. This is calculated for

each month by taking the median household income for the month and multiplying it by 100/CPI, where CPI is theconsumer price index and is to account for inflation.

Other explanatory weather variables that were considered, but not ultimately used include: average heat index, maximumthree day moving average temperature, and maximum three day moving average heat index. None of these three variablesdisplayed a significant correlation with either energy or demand, and so were eliminated from consideration early in theprocess. Other economic variables considered were median income, population, and real GDP growth. While strongcorrelations were observed between the dependent variables and some of these economic variables, there was also strongmulticollinearity with the number customers. Since the number of customers showed consistently stronger correlations withthe dependent variables, customer data was preferred over these other variables.

Model SelectionCWLD refined each monthly model by employing both forwards and backwards processes. When comparing any two models,CWLD examined three main criteria to determine which model had the best overall fit:

● The model with the larger adjusted R2 was preferred over a smaller R2 value.● The model with the smaller was preferred over those with larger standard errors.

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● Models that had no variables with variation inflation factors (“vif”) of over 10 were preferred to those that did.

For the forward selection process, CWLD employed a technique known as step-wise regression. For this technique, an initialset of linear regression models are constructed for each explanatory variable, and the variable with the lowest p-value ischosen. Then, bivariate multiple regression models are constructed using the variable chosen in the first step and all of theother potential variables. The new variable with the lowest p-value is then added to the model, and we continue in this wayuntil there is no longer a variable remaining that has a p-value of less than 0.2.

For the backwards process, CWLD created a model with all of the potential variables identified as significant and then workedbackwards, eliminating the variable in each step with the highest p-value over 0.2, until all of the variables remaining have ap-value of less than 0.2. If at the end of this process there remains high vif values for some variables, CWLD continuedeliminating variables until all have a vif of less than 10. Finally, the models from the forwards and backwards processes werethen compared, and a final model is chosen using the criteria listed above.

Energy ForecastTo create the monthly energy forecasts, CWLD used the regression equations generated by each model. These equationsallowed CWLD to forecast each future year’s energy load based on what CWLD expects the explanatory variables to be in thoseyears. Each model contains at least one weather variable and the number of customers. To predict the future value of eachweather variable, CWLD used the average observed value for the variable for each month over the 25 year historical period.

To determine the future customer growth, CWLD examinedpast customer data and noted that the growth over the pastten years has followed a distinct linear trend. Thus, insteadof assuming that customer growth is by a fixed yearlypercentage, a linear model was constructed and it wasdetermined that CWLD’s customer base is expected to growby roughly 600 customers year over year. Prior to the 2018CWLD load forecast, a percentage was used and this causedthe yearly increase in load to accelerate in more distantyears due to compounding. The customer growth forecast isbased on ten years of monthly data, and re-assessed at thebeginning of each load forecasting cycle.

Monthly Energy ForecastThe energy forecast is shown in the three tables below. CWLD expects its total system load to be 1,251.5 ± 89.6 GWH inFY2020, growing by approximately 0.8% yearly until reaching 1,392.3 ± 100.0 GWH at the end of the forecast period in FY2034.The first table represents the mean value energy used by CWLD during each month, and is the value for which it is equallylikely that the actual total energy will be above versus below. The second and third tables display the lower and upper boundsof the 95% confidence interval for each mean value, which provides a range of values where there is a 95% probability that theactual load will be within.

Table 1: Mean Energy Forecast

R2 0.96 0.95 0.85 0.87 0.89 0.91 0.91 0.86 0.90 0.89 0.91 0.94 Fiscal

S.E. 2,421 2,588 3,655 2,897 3,685 3,952 3,867 4,672 3,864 3,079 2,654 2,878 Year

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec MWH's

2019 92,493 90,142106,00

0

5

2020112,96

3 98,089 95,412 87,094 97,329111,70

2129,248

127,096

103,966 93,267 90,890

107,035 1,251,534

2021114,12

8 99,012 96,230 87,746 98,054112,40

3130,160

127,983

104,683 94,041 91,638

108,071 1,261,593

2022115,29

3 99,936 97,048 88,398 98,780113,10

4131,071

128,871

105,400 94,816 92,386

109,106 1,271,652

2023116,45

8100,85

9 97,866 89,050 99,506113,80

5131,982

129,758

106,118 95,589 93,134

110,141 1,281,710

2024117,62

2101,78

3 98,684 89,702100,23

1114,50

7132,893

130,646

106,835 96,363 93,882

111,177 1,291,769

2025118,78

7102,70

6 99,502 90,355100,95

7115,20

8133,805

131,533

107,552 97,137 94,630

112,212 1,301,827

2026119,95

2103,63

0100,32

0 91,007101,68

3115,90

9134,716

132,420

108,270 97,911 95,378

113,247 1,311,885

2027121,11

6104,55

3101,13

8 91,659102,40

8116,61

0135,627

133,308

108,987 98,684 96,126

114,282 1,321,944

2028122,28

1105,47

7101,95

6 92,311103,13

4117,31

2136,539

134,195

109,704 99,457 96,874

115,318 1,332,002

2029123,44

6106,40

0102,77

3 92,963103,86

0118,01

3137,450

135,083

110,422

100,230 97,622

116,353 1,342,060

2030124,61

1107,32

4103,59

1 93,616104,58

6118,71

4138,361

135,970

111,139

101,004 98,370

117,388 1,352,117

2031125,77

5108,24

7104,40

9 94,268105,31

1119,41

5139,272

136,858

111,857

101,777 99,118

118,424 1,362,175

2032126,94

0109,17

1105,22

7 94,920106,03

7120,11

7140,184

137,745

112,574

102,549 99,866

119,459 1,372,233

2033128,10

5110,09

4106,04

5 95,572106,76

3120,81

8141,095

138,632

113,291

103,322

100,614

120,494 1,382,290

2034129,27

0111,01

8106,86

3 96,224107,48

8121,51

9142,006

139,520

114,009 1,392,347

Table 2: Energy Forecast 95% Confidence Interval Lower Bounds

Fiscal

Year


2019 85,661 84,265 99,628

2020107,604 92,340 87,247 80,669 89,134

102,705

120,716

116,698 95,310 86,396 84,978

100,627 1,161,977

2021108,738 93,228 88,017 81,282 89,810

103,327

121,580

117,521 95,966 87,129 85,690

101,623 1,171,469

2022109,869 94,113 88,784 81,893 90,483

103,944

122,440

118,342 96,619 87,859 86,399

102,616 1,180,930

2023110,999 94,997 89,548 82,502 91,153

104,558

123,298

119,159 97,269 88,588 87,107

103,608 1,190,358

2024112,128 95,878 90,310 83,109 91,820

105,167

124,154

119,972 97,916 89,314 87,813

104,598 1,199,756

2025113,254 96,758 91,069 83,713 92,485

105,772

125,006

120,782 98,560 90,038 88,517

105,586 1,209,124

2026114,379 97,636 91,825 84,316 93,147

106,374

125,856

121,589 99,200 90,759 89,219

106,572 1,218,463

2027115,503 98,512 92,579 84,917 93,806

106,972

126,704

122,392 99,837 91,479 89,920

107,557 1,227,773

2028116,624 99,386 93,331 85,515 94,463

107,566

127,549

123,192

100,472 92,197 90,619

108,539 1,237,055

2029117,745

100,259 94,080 86,112 95,118

108,157

128,391

123,989

101,103 92,912 91,316

109,520 1,246,309

2030118,863

101,130 94,827 86,707 95,770

108,745

129,231

124,783

101,732 93,626 92,012

110,498 1,255,537

2031119,981

101,999 95,572 87,300 96,420

109,329

130,069

125,575

102,358 94,338 92,706

111,476 1,264,739

2032121,096

102,867 96,314 87,892 97,068

109,910

130,905

126,363

102,982 95,048 93,399

112,451 1,273,916

2033122,211

103,733 97,055 88,481 97,713

110,489

131,738

127,149

103,602 95,756 94,090

113,425 1,283,068

6

2034123,324

104,597 97,793 89,069 98,356

111,064

132,569

127,931

104,221 1,292,196

Table 3: Energy Forecast 95% Confidence Interval Upper Bounds

Fiscal

Year


201999,325 96,020 112,37

2

2020118,32

2103,83

8103,57

793,518 105,52

3120,69

8133,80

0137,49

4112,62

2100,13

896,802 113,44

41,341,091

2021119,51

8104,79

7104,44

394,210 106,29

9121,47

9134,58

7138,44

5113,40

0100,95

497,587 114,51

91,351,716

2022120,71

6105,75

9105,31

294,903 107,07

7122,26

4135,37

9139,40

0114,18

1101,77

298,373 115,59

51,362,374

2023121,91

6106,72

2106,18

495,599 107,85

9123,05

3136,17

5140,35

8114,96

6102,59

199,161 116,67

41,373,062

2024123,11

7107,68

7107,05

896,296 108,64

3123,84

7136,97

5141,31

9115,75

4103,41

399,952 117,75

51,383,781

2025124,32

0108,65

5107,93

596,996 109,42

9124,64

4137,77

9142,28

4116,54

5104,23

6100,74

4118,83

71,394,530

2026125,52

4109,62

4108,81

497,698 110,21

9125,44

5138,58

6143,25

2117,34

0105,06

2101,53

7119,92

21,405,308

2027126,73

0110,59

5109,69

698,401 111,01

1126,24

9139,39

6144,22

4118,13

7105,88

9102,33

3121,00

81,416,114

2028127,93

8111,56

7110,58

099,107 111,80

5127,05

7140,21

0145,19

8118,93

7106,71

8103,13

0122,09

71,426,949

2029129,14

7112,54

2111,46

699,815 112,60

2127,86

9141,02

7146,17

6119,74

0107,54

9103,92

8123,18

71,437,810

2030130,35

8113,51

8112,35

5100,52

4113,40

1128,68

4141,84

7147,15

7120,54

6108,38

1104,72

9124,27

81,448,698

2031131,57

0114,49

5113,24

6101,23

5114,20

3129,50

2142,67

0148,14

0121,35

5109,21

5105,53

1125,37

21,459,611

2032132,78

4115,47

5114,14

0101,94

8115,00

6130,32

3143,49

6149,12

7122,16

6110,05

1106,33

4126,46

71,470,549

2033133,99

9116,45

6115,03

5102,66

3115,81

2131,14

7144,32

4150,11

6122,98

0110,88

8107,13

9127,56

41,481,512

2034135,21

5117,43

8115,93

3103,37

9116,62

1131,97

4145,15

6151,10

8123,79

61,492,499

Accuracy of FY18 Energy ForecastFor FY18, the CWLD energy forecast considered only four weather variables, those being cooling degree days (CDD), heatingdegree days (HDD), maximum day CDD, and maximum day HDD. This represented a continuation of previous forecastingmethodology, the only difference being the inclusion of the maximum day CDD and HDD. Generally, the 2018 forecast tendedto miss high, though the total load was within the 95% confidence interval 10 out of 12 months.

Table 4: FY18 Energy Forecast Accuracy

Predicted Load(MWH)

Actual Load(MWH)

Difference(MWH)

Within 95% Conf.Interval?

Jan 113,567 108,894 4,673 y

Feb 99,311 97,175 2,136 y

Mar 96,561 93,466 3,095 y

Apr 89,685 79,253 10,432 y

May 94,366 90,192 4,174 y

Jun 116,102 101,053 15,049 n

7

Jul 130,064 121,684 8,380 y

Aug 120,204 114,738 5,466 y

Sep 106,568 111,440 -4,872 y

Oct 93,319 88,252 5,067 y

Nov 91,786 91,874 -88 y

Dec 107,450 97,990 9,460 n

For the two months that the forecasted value was not within the 95% confidence interval, the observed weather differedsignificantly from the mean. For June, we there was a total of 252 CDD, which was under the long term average for June of296 CDD. This amount of CDD for June only reached 23rd percentile for CDD over the 25 years of weather data in the model.Similarly, in December of 2018 Columbia had 872 HDD, which was under the long term average of 937 HDD, which representsthe 32nd percentile over the period. Overall, the 2018 did a good job of estimating the 2019 system load, though the biastowards higher than observed forecasted values is one big reason that CWLD decided to look at the weather variables moreclosely in the 2019 forecast.

One future project that could affect the accuracy of this forecast going forward is the 10 MW Truman solar field that will beoperational in FY20. This project will be behind-the-meter in MISO, and so will be seen as a load reduction of 1-2% yearly.

Non-Coincident Peak Forecast

Monthly Non-Coincident Peak ForecastThe Non-Coincident Peak (“NCP”) forecast follows the same method as the energy forecast provided above. However,generally the weather variables used in these models differ. This is mainly because the energy forecast is based on the totalhourly load for all hours of the course of the month where the NCP forecast is looking at only the highest hour of the month.The predicted weather and customer data was done in the same way as in the energy forecast. CWLD expect a system peak inFY2020 of 275 ± 20 MW, growing at around 0.7% yearly until FY2034 where we expect a NCP of 306 ± 22 MW.

Table 5: Mean Non-Coincident Peak Forecast

R2 0.97 0.96 0.94 0.82 0.82 0.93 0.89 0.83 0.93 0.93 0.74 0.89

S.E. 4.8 5.0 5.4 9.8 12.2 8.1 8.8 10.8 7.7 9.0 11.0 7.7

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2019 189 175 198

2020 210 197 183 171 211 254 275 274 247 191 177 200

2021 213 199 185 172 212 256 277 276 249 192 179 201

2022 215 201 187 174 213 258 279 279 251 194 180 203

2023 217 203 189 175 215 260 281 281 253 195 182 205

2024 219 205 191 176 216 262 284 283 255 197 183 207

2025 222 207 193 178 218 263 286 285 257 198 185 209

2026 224 209 195 179 219 265 288 287 259 200 187 211

2027 226 211 197 180 220 267 290 289 261 201 188 213

2028 228 213 199 182 222 269 292 291 263 203 190 215

2029 231 215 200 183 223 271 295 293 264 204 191 217

2030 233 216 202 184 225 272 297 295 266 206 193 219

2031 235 218 204 185 226 274 299 298 268 207 195 221

2032 238 220 206 187 227 276 301 300 270 209 196 223

8

2033 240 222 208 188 229 278 304 302 272 210 198 225

2034 242 224 210 189 230 280 306 304 274

Table 6: NCP Forecast 95% Confidence Interval Lower Bounds


2019 169 151 181

2020 200 186 171 149 183 236 255 250 230 170 152 182

2021 202 188 173 150 184 238 257 252 232 172 154 184

2022 204 190 175 151 186 239 259 254 234 173 155 186

2023 206 192 177 153 187 241 261 256 236 175 157 188

2024 208 194 178 154 188 243 263 258 237 176 158 190

2025 211 195 180 155 189 244 265 260 239 177 160 192

2026 213 197 182 156 190 246 267 262 241 179 161 193

2027 215 199 184 157 191 247 269 264 243 180 162 195

2028 217 201 186 158 193 249 271 266 244 181 164 197

2029 219 203 188 160 194 251 273 268 246 183 165 199

2030 222 205 190 161 195 252 275 270 248 184 167 201

2031 224 206 191 162 196 254 277 271 250 186 168 202

2032 226 208 193 163 197 255 279 273 251 187 169 204

2033 228 210 195 164 198 257 281 275 253 188 171 206

2034 230 212 197 165 199 258 283 277 255

Table 7: NCP Forecast 95% Confidence Interval Upper Bounds


2019 209 200 215

2020 221 208 195 193 238 273 294 298 265 211 201 217

2021 223 210 197 194 240 275 297 301 267 213 203 219

2022 226 212 199 196 241 277 299 303 269 214 205 221

2023 228 214 201 197 243 279 301 305 271 216 207 223

2024 230 216 203 199 244 281 304 308 273 218 208 225

2025 233 218 205 200 246 283 306 310 275 219 210 227

2026 235 220 207 202 248 285 309 312 277 221 212 229

2027 237 222 209 203 249 287 311 314 279 223 214 231

2028 240 224 211 205 251 289 314 317 281 224 216 233

2029 242 226 213 206 253 291 316 319 283 226 217 235

2030 244 228 215 208 255 293 318 321 285 228 219 237

2031 247 230 217 209 256 295 321 324 287 229 221 239

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2032 249 232 219 211 258 297 323 326 289 231 223 241

2033 252 234 221 212 260 299 326 328 291 233 225 244

2034 254 236 223 214 261 301 328 331 293

Effects of Weather on the ForecastIt is important to remember that the forecasted mean values and confidence intervals for system load are based on averageweather data. If the weather experienced in a given year is significantly different than the average used in the forecast, thenthe actual system load could be much higher or lower than the forecasted value. The table below shows the effect of moreextreme weather on the forecasted NCP for the month of July.

Table 8: Effects of Weather on July NCP Forecast

Variable Mean (µ) Standard Deviation (σ) µ-2σ µ-1σ µ+1σ µ+2σAVG_TEMP 78.722° 2.695 73.332° 76.027° 81.417° 84.112°MIN_TEMP 58.656° 3.506 51.644° 55.15° 62.162° 65.668°Forecasted NCP 274.6 MW -- 260.0 MW 267.3 MW 281.9 MW 289.2 MW

If CWLD knew that the weather was going to be two standard deviations higher than average for next July (which would beexpected less than 2.5% of the time), the forecasted peak would jump to 289.2 MW. This value would become the center of anew 95% confidence interval of approximately 269 MW to 309 MW. While the actual weather experienced is most likely to benear their long term averages, it is important to remember that deviations from these averages can have dramatic effects onthe accuracy of the forecasts.

Accuracy of FY18 NCP ForecastThe accuracy of the FY18 NCP forecast was more mixed than that of the energy forecast. The observed NCP fell outside the95% confidence interval for four months of FY19, and was within the confidence interval in eight out of twelve months.

Table 9: FY18 NCP Forecast Accuracy

Predicted NCP(MW)

Actual NCP (MW) Difference (MW) Within 95% Conf.Interval?

Jan 209 219 -10 y

Feb 197 196 1 y

Mar 181 196 -15 n

Apr 172 149 23 n

May 213 195 18 y

Jun 261 231 30 n

Jul 275 259 16 y

Aug 276 253 23 y

Sep 246 243 3 y

Oct 188 215 -27 n

Nov 174 179 -5 y

Dec 197 181 16 y

The major factor in all four months where the NCP was not within the confidence interval was abnormal weather. In March,the monthly peak occurred on Monday March 4th at 8am, and this happened to be a very cold morning. The averagemaximum HDD for March is 39.84, which represents an average temperature of 25.16° F for the day. On the morning of March4th, however, the average temperature for the day was 8.1° F, which is in the 97.5th percentile for maximum heating degreedays in the month of March. The extremity of the weather on this day lead directly lead to the higher than expected NCP for

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March. Similarly, the maximum cooling degree days on the day of the October peak reached the 95th percentile for extremehigh temperature, leading to the higher than predicted NCP.

The forecast also missed the confidence interval in the months of April and June, and in both cases the weather was unusuallymild. In April, the maximum CDD only reached the 29th percentile, while the maximum HDD reached the 23rd percentile. Sincethe weather was mild all month, there was no extreme event to drive the peak, and so the NCP was lower than usual. ForJune, the maximum CDD for the month was 17.4 (82.4°F daily average temperature), but this was happened on a weekend day.Typically, CWLD reaches its peak during the work week when its larger industrial customers are at full production. The actualsystem peak occurred on Thursday June 27th, which had a maximum CDD of 15.5 and represents the 10th percentile forextreme temperature. Since the temperatures in June were much milder than is typical, the monthly peak was much lowerthan usual.

As mentioned in the Energy forecast section, the 10 MW Truman field solar project will have an effect on our NCP in futureyears. Depending on when the NCP occurs, CWLD expect that this project will reduce the NCP in the summer months bybetween 7 and 10 MW.

MISO Zone 5 Coincident Peak Forecast

Since Zone 5 covers such a small geographic area,the correlation between the Zone 5 peak andCWLD’s NCP is very high. To forecast CWLD’s Zone5 coincident peak, the coincidence factor betweenthe CWLD system load when Zone 5 experienced itsmonthly peak and the CWLD NCP was examined foreach summer month since 2005. It was discoveredthat the coincidence factors were highly consistentwithin the same month, and had little correlationwith the weather experienced during the month.Thus, to forecast the Zone 5 coincident peak, CWLDused the average of coincidence factors for eachmonth and applied them to the NCP forecasted

values using the equation shown below.

𝑍𝑜𝑛𝑒 5 𝐶𝑃 = 𝐶𝑊𝐿𝐷 𝑁𝐶𝑃 * 𝐶𝑜𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝐹𝑎𝑐𝑡𝑜𝑟

For example, for July of 2020 the forecasted NCP is 275 MW. To calculate CWLD’s contribution to the MISO Zone 5 peak,CWLD’s NCP of 275 MW for July was multiplied by average July coincidence factor of 0.972 to get the forecasted Zone 5coincident peak of 267 MW. This calculation was repeated for each summer month, and the results are provided in the tablebelow.

Table 10: Summer Zone 5 Coincident Peak Forecast

Coincidence Factor 0.966 0.972 0.974 0.964

Standard Deviation of CF 0.035 0.014 0.037 0.025

Jun Jul Aug Sep2020 246 267 267 239

2021 247 269 269 240

2022 249 271 271 242

2023 251 273 273 244

2024 253 276 275 246

2025 254 278 277 248

2026 256 280 280 249

2027 258 282 282 251

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2028 260 284 284 253

2029 261 286 286 255

2030 263 289 288 257

2031 265 291 290 259

2032 267 293 292 260

2033 268 295 294 262

2034 270 297 296 264

MISO System Coincident Peak ForecastThe MISO system coincident peak forecast was done in the same way as the Zone 5 forecast, with one exception. Due to thesize of MISO’s service area and the relatively small amount that CWLD contributes to MISO’s system peak, there is much morevariation in the coincidence factors. The size of MISO’s geographic area also means that the weather CWLD experiences isoften very different from the areas where the weather is driving MISO’s system peak. Therefore, CWLD examined the effectson the weather in Columbia and its correlation with the monthly MISO coincidence factor. A linear relationship betweenaverage temperature and coincidence factor was established, allowing CWLD to forecast the monthly coincidence factor.CWLD used the hourly average temperature of when MISO reached its monthly peak, and averaged these for each summermonth since 2007 to establish the coincidence factors used in the forecast.

Table 11: MISO Coincident Peak Forecast

Coincidence Factor 0.917 0.945 0.923 0.880

Standard Deviation of CF 0.054 0.054 0.054 0.054

Jun Jul Aug Sep

2020 233 259 253 218

2021 235 262 255 220

2022 237 264 257 221

2023 238 266 259 223

2024 240 268 261 224

2025 242 270 263 226

2026 243 272 265 228

2027 245 274 267 229

2028 247 276 269 231

2029 248 278 271 233

2030 250 280 273 234

2031 252 283 275 236

2032 253 285 277 238

2033 255 287 279 239

2034 257 289 280 241

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Appendix

I. DataNote that only data used in final models is provided. Complete data sets are available by contacting Columbia Water & Light.

October Data

November Data

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December Data

14

January Data

15

February Data

16

March Data

April Data

17

May Data

June Data

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July Data

August Data

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September Data

II. Regression Results

A. Energy Forecasts

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21

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24

25

26

27

28

29

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B. Demand Forecasts

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32

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III. 2018 Forecast

A. Energy Forecast

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B. Demand Forecast

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IV. 2017 Forecast

A. Energy Forecast

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B. Demand Forecast

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ReferencesCity of Columbia. (2018). FY2008-FY2017-Ten-Year-Trend-Manual.pdf. Retrieved 2019, from como.gov:

https://www.como.gov/finance/wp-content/uploads/sites/21/2018/10/FY2008-FY2017-Ten-Year-Trend-Manual.pdf

University of Missouri-Columbia. (2019). Daily and Hourly Weather Query. Retrieved from Missouri HIstorical AgriculturalWeather Database: http://agebb.missouri.edu/weather/history/index.asp?station_prefix=san

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Documents

Load Forecast FY2020-FY2034