76 lines
2.7 KiB
R
76 lines
2.7 KiB
R
library(tidyverse)
|
|
library(fixest)
|
|
|
|
NAMES <- c("Trumbull Energy Center","Orange County Advanced Power Station","Shady Hills Combined Cycle Facility","Cumberland (TN)","Kingston","Lincoln Land Energy Center","Delta Blues Advanced Power Station","Homer","Vicksburg","Cheyenne Prairie","Legend and Lone Star","Wolf Summit Energy","Franklin Farms","Viola Generating Station","CPV Basin Ranch Energy Center","Smarr Combined Cycle Energy Facility","Waterford 5 & 6","Lake Charles","Jefferson Power Station")
|
|
CAP <- c(308.7*2+360,
|
|
453*2+400,
|
|
612,
|
|
454.8+323,
|
|
323,
|
|
638.4*2,
|
|
477+341.7,
|
|
4678,
|
|
819.4,
|
|
132,
|
|
754+453,
|
|
600,
|
|
1560,
|
|
705,
|
|
1490,
|
|
1425,
|
|
1640,
|
|
994,
|
|
754
|
|
)
|
|
|
|
DETAILED_REPORT <- c(1,1,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0)
|
|
EMP <- c(25,27,12.5,30,NA,34,21,250,21,12,73,30,44,30,45,30,30,30,22)
|
|
PEAK_CONST <- c(920,NA,600,NA,300,500,300,2500,560,400,NA,400,(1500+1800)/2,500,1000,1200,700,1100,NA)
|
|
DATE <- c(2017,2018,2018,2022,2015,2020,2024,2025,2025,2012,2024,2025,2024,2024,2026,2026,2025,2020,2025)
|
|
DATA <- cbind(CAP,EMP,PEAK_CONST,DETAILED_REPORT,DATE) %>% as.matrix %>% as_tibble
|
|
DATA$NAMES <- NAMES
|
|
DATA
|
|
colnames(DATA ) <- c("Capacity","Emp","Peak_Const","Report_Quality","Report_Year","Plant")
|
|
REG_DATA <-DATA %>% select(Emp,Capacity,Report_Quality,Report_Year)
|
|
REG_DATA_TRIMMED <-DATA %>% filter(Plant!='Homer') %>% select(Emp,Capacity,Report_Quality,Report_Year)
|
|
DATA
|
|
MOD1 <- feols(log(Emp)~Capacity+Report_Quality+Report_Year,REG_DATA,vcov='hetero')
|
|
feols(log(Emp)~Capacity+Report_Quality+Report_Year,REG_DATA,vcov='hetero')
|
|
PRED_DATA <- DATA[1:4,]
|
|
PRED_DATA$Capacity <- c(250,750,1000,1500)
|
|
PRED_DATA$Report_Year <- 2026
|
|
PRED_DATA$Plant <- 'Enyo'
|
|
PRED_DATA$Emp<- NA
|
|
round(exp(predict(MOD1,newdata=PRED_DATA)))
|
|
#PRED_DATA$Capacity<- c(640,1280,1280,3680) #Larger number includes the single cyle turbines that look to be turned off most of the time
|
|
PRED_OPERATING <- round(exp(predict(MOD1,newdata=PRED_DATA)))
|
|
PRED_OPERATING
|
|
|
|
ggplot(REG_DATA,aes(x=Capacity,y=log(Emp)))+geom_point()+geom_smooth(method='lm')
|
|
#feols(log(Emp)~Capacity+Report_Quality+Report_Year,REG_DATA_TRIMMED,vcov='hetero')
|
|
|
|
|
|
MOD2 <- feols(Peak_Const~Capacity+Report_Year+Report_Quality,DATA,vcov='hetero')
|
|
feols(Peak_Const~Capacity+Report_Year+Report_Quality,DATA,vcov='hetero')
|
|
ggplot(DATA,aes(x=Capacity,y=Peak_Const))+geom_point()+geom_smooth(method='lm')
|
|
|
|
###Construction Data
|
|
CONST_SCHEDULE <- c(45,75,90,115,175,235,285,350,400,350,255,175,95,65)
|
|
PEAK_RATIO <- (sum(CONST_SCHEDULE )/12)/400
|
|
PEAK_RATIO
|
|
PRED_DATA_CONST <- PRED_DATA
|
|
PRED_DATA_CONST$Capacity <- c(640,640,0,2400)
|
|
CONST_EMPLOYMENT_PRED <- round(predict(MOD2,PRED_DATA_CONST)*PEAK_RATIO)
|
|
CONST_EMPLOYMENT_PRED
|
|
|
|
MOD2
|
|
CONST_SCHEDULE
|
|
95*2
|
|
|
|
|
|
PRED_DATA <- DATA[1:4,]
|
|
PRED_DATA
|
|
|
|
|
|
|