ENERGY CENTER State Utility Forecasting Group (SUFG) Multiple Weather Station Analysis

Transcription

1 Multiple Weather Station Analysis

2 Process We constructed econometric formulations for a few states with multiple weather stations using the data from last year this provides for a comparison to last year s models This resulted in problems with model coefficients of the wrong sign and with variables that were not statistically significant 2

3 2014 TX Model (San Antonio) Dependent Variable: ELECTRICITY_SALES Method: Least Squares Date: 03/13/15 Time: 15:42 Sample: Included observations: 23 Variable Coefficient Std. Error t-statistic Prob. C REAL_INCOME/POPULATION REAL_GSP CDD HDD R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid 2.60E+08 Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

4 2014 TX (multiple stations) Dependent Variable: ELECTRICITY_SALES Method: Least Squares Sample: Included observations: 23 Variable Coefficient Std. Error t-statistic Prob. C REAL_INCOME/POPULATION REAL_GSP DALLAS_TX_C DALLAS_TX_H HOUSTON_TX_C HOUSTON_TX_H LUBBOCK_TX C LUBBOCK_TX H SAN_ANTONIO_TX_C SAN_ANTONIO_TX_H R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid 1.05E+08 Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

5 HDD Correlations Dallas Houston Lubbock San Antonio Dallas Houston Lubbock San Antonio

6 A Different Process We developed virtual weather stations using population-weighted CDD and HDD from multiple weather stations within the states Each state is divided into regions along county lines and a representative weather station is chosen for each region The CDD and HDD for a given year are determined using the population of each region and the CDD and HDD of the region s weather station 6

7 2014 TX (virtual station) Dependent Variable: ELECTRICITY_SALES Method: Least Squares Date: 03/13/15 Time: 15:42 Sample: Included observations: 23 Variable Coefficient Std. Error t-statistic Prob. C REAL_INCOME/POPULATION REAL_GSP CDDMWS HDDMWS R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid 1.92E+08 Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

8 Model Comparison TX Comparison across models Single weather station Populationweighted average weather Multiple weather stations Adjusted R-squared Prob(F-statistic) Durbin-Watson stat Mean absolute error

9 510, , , , , , ,000 ENERGY CENTER TX Model Comparison Texas History 2014 model forecast - single weather station 2014 model forecast - population weighted average weather 9 Annual retail sales in GWh

10 ENERGY CENTER IL Model Comparison 10 Annual retail sales in GWh Illinois History 2014 model forecast - single weather station 2014 model forecast - population weighted average weather

11 135, , , ,000 95,000 85,000 75,000 65,000 ENERGY CENTER IN Model Comparison Indiana 11 Annual retail sales in GWh History 2014 model forecast - single weather station 2014 model forecast - population weighted average weather

12 ENERGY CENTER LA Model Comparison 12 Annual retail sales in GWh Louisiana History 2014 model forecast - single weather station 2014 model forecast - population weighted average weather

13 ENERGY CENTER MI Model Comparison Michigan History 2014 model forecast - single weather station 2014 model forecast - population weighted average weather Annual retail sales in GWh

14 Results We examined the possibility of using multiple weather stations (each as a separate variable) and found it infeasible We believe that the population-weighted virtual station works well but has little impact on the forecast results We propose to use the populationweighted virtual station method 14

15 Population Weighted Virtual Weather Stations Orange indicates a weather station that is used Blue indicates a weather station that could not be used because of data problems

16 Arkansas Population Density Map Fayetteville Harrison Little Rock Source: US Census Bureau 16

17 Iowa Population Density Map Waterloo Cedar Rapids Des Moines Iowa City 17

18 Illinois Population Density Map Chicago Springfield Carbondale 18

19 Indiana Population Density Map South Bend Evansville Indianapolis 19

20 Kentucky Population Density Map Henderson Lexington Paducah Bowling Green 20

21 Louisiana Population Density Map Shreveport New Orleans Source: "Arkansas population map" by Jim Irwin at the English language Wikipedia. 21

22 Michigan Population Density Map Marquette Munising Grand Rapids Detroit 22

23 Minnesota Population Density Map Thief River Falls Duluth Fergus Falls Minneapolis 23

24 Missouri Population Density Map Kansas City St. Louis Springfield 24

25 Mississippi Population Density Map Tupelo Jackson Hattiesburg Pascagoula Gulfport 25

26 Montana Population Density Map Missoula Billings 26

27 North Dakota Population Density Map Fargo Bismarck 27

28 South Dakota Population Density Map Rapid City Sioux Falls 28

29 Texas Population Density Map Lubbock Dallas Houston San Antonio 29

30 Wisconsin Population Density Map Green Bay Milwaukee 30