library(readxl)
library(lubridate)
library(dplyr)
library(tidyr)
Natural gas combine cycle plant generation depends upon market demand . Attemp has been made to calculate the number of days plant was supplying power to grid.It is a first attempt for a comparitive study with counting wind generating days
G17<-read_excel("GOC-2017.xlsx")
G17$Date <- as.Date(G17$Date, format = "%Y-%m-%d") # Changing to Date format
Dz_17Total<-G17 %>% group_by(Date) %>% summarize(Total_2017_Gen=sum(`TOTAL`),n=n())
Dz_17Goreway<-G17 %>% group_by(Date) %>% summarize(Goreway_2017_Total=sum(`SITHE GOREWAY-G11`+
`SITHE GOREWAY-G12`+`SITHE GOREWAY-G13`+`SITHE GOREWAY-G15`),n=n())
Dz_17Halton<-G17 %>% group_by(Date) %>% summarize(Halton_2017_Total=sum(`HALTONHILLS-LT_G1`+`HALTONHILLS-LT_G2`+`HALTONHILLS-LT_G3`),n=n())
Dz_17Portland<-G17 %>% group_by(Date) %>% summarize(Portlands_2017_Total=sum(`PORTLANDS-G1`+`PORTLANDS-G2`+`PORTLANDS-G3`),n=n())
Dz_17Greenfeild<-G17 %>% group_by(Date) %>% summarize(GEC_2017_Total=sum(`GREENFIELD ENERGY CENTRE-G1`+
`GREENFIELD ENERGY CENTRE-G2`+`GREENFIELD ENERGY CENTRE-G3`+`GREENFIELD ENERGY CENTRE-G4`),n=n())
Dz_17Brighton<-G17 %>% group_by(Date) %>% summarize(Brigton_2017_Total=sum(`BRIGHTON BEACH`),n=n())
head(Dz_17Total)
## # A tibble: 6 x 3
## Date Total_2017_Gen n
## <date> <dbl> <int>
## 1 2017-01-01 395129 24
## 2 2017-01-02 400509 24
## 3 2017-01-03 410456 24
## 4 2017-01-04 444225 24
## 5 2017-01-05 450843 24
## 6 2017-01-06 447011 24
Dz_17GB<-cbind(Dz_17Total[,-3],Dz_17Goreway[,2],Dz_17Halton[,2],Dz_17Portland[,2],Dz_17Greenfeild[,2],Dz_17Brighton[,2])
head(Dz_17GB)
## Date Total_2017_Gen Goreway_2017_Total Halton_2017_Total
## 1 2017-01-01 395129 0 0
## 2 2017-01-02 400509 0 0
## 3 2017-01-03 410456 0 0
## 4 2017-01-04 444225 0 0
## 5 2017-01-05 450843 0 6762
## 6 2017-01-06 447011 0 7965
## Portlands_2017_Total GEC_2017_Total Brigton_2017_Total
## 1 0 0 0
## 2 0 0 0
## 3 1435 0 0
## 4 0 85 0
## 5 1358 155 0
## 6 2494 8816 6361
Checking for missing values
any(is.na(Dz_17GB))
## [1] FALSE
sum(is.na(Dz_17GB))
## [1] 0
colSums(is.na(Dz_17GB))
## Date Total_2017_Gen Goreway_2017_Total
## 0 0 0
## Halton_2017_Total Portlands_2017_Total GEC_2017_Total
## 0 0 0
## Brigton_2017_Total
## 0
nrow(Dz_17GB)
## [1] 365
a<-na.omit(Dz_17GB)
any(is.na(a))
## [1] FALSE
Goreway_U_Status_17<-cut(Dz_17Goreway$Goreway_2017_Total,breaks = c(0,100,10000),labels = c(0,1))
Goreway_U_Status_17<-cbind(Dz_17Goreway,Goreway_U_Status_17)
any(is.na(Goreway_U_Status_17))
## [1] TRUE
Goreway_U_Status_17[is.na(Goreway_U_Status_17)]=0
head(Goreway_U_Status_17)
## Date Goreway_2017_Total n Goreway_U_Status_17
## 1 2017-01-01 0 24 0
## 2 2017-01-02 0 24 0
## 3 2017-01-03 0 24 0
## 4 2017-01-04 0 24 0
## 5 2017-01-05 0 24 0
## 6 2017-01-06 0 24 0
Counting of Running/Notrunning
library(plyr)
aa_G_17<-count(Goreway_U_Status_17,"Goreway_U_Status_17")
names(aa_G_17)[1]="Running_Status"
names(aa_G_17)[2]="Goreway_Running_Frequency"
aa_G_17
## # A tibble: 1 x 2
## Running_Status Goreway_Running_Frequency
## <chr> <int>
## 1 Goreway_U_Status_17 365
Halton_U_Status_17<-cut(Dz_17Halton$Halton_2017_Total,breaks = c(0,100,10000),labels = c(0,1))
Halton_U_Status_17<-cbind(Dz_17Halton,Halton_U_Status_17)
any(is.na(Halton_U_Status_17))
## [1] TRUE
Halton_U_Status_17[is.na(Halton_U_Status_17)]=0
##head(Halton_U_Status_18)
aa_H_17<-count(Halton_U_Status_17,"Halton_U_Status_17")
names(aa_H_17)[1]="Running_Status"
names(aa_H_17)[2]="Halton_Running_Frequency"
aa_H_17
## # A tibble: 1 x 2
## Running_Status Halton_Running_Frequency
## <chr> <int>
## 1 Halton_U_Status_17 365
Portland_U_Status_17<-cut(Dz_17GB$Portlands_2017_Total,breaks = c(0,100,10000),labels = c(0,1))
Portland_U_Status_17<-cbind(Dz_17Portland,Portland_U_Status_17)
any(is.na(Portland_U_Status_17))
## [1] TRUE
Portland_U_Status_17[is.na(Portland_U_Status_17)]=0
##head(Portland_U_Status_17)
aa_P_17<-count(Portland_U_Status_17,"Portland_U_Status_17")
names(aa_P_17)[1]="Running_Status"
names(aa_P_17)[2]="Portland_U_Status_17"
aa_P_17
## # A tibble: 1 x 2
## Running_Status Portland_U_Status_17
## <chr> <int>
## 1 Portland_U_Status_17 365
Greenfield_U_Status_17<-cut(Dz_17GB$GEC_2017_Total,breaks = c(0,100,10000),labels = c(0,1))
Greenfield_U_Status_17<-cbind(Dz_17Greenfeild,Greenfield_U_Status_17)
any(is.na(Greenfield_U_Status_17))
## [1] TRUE
Greenfield_U_Status_17[is.na(Greenfield_U_Status_17)]=0
##head(Greenfield_U_Status_17)
aa_GEC_17<-count(Greenfield_U_Status_17,"Greenfield_U_Status_17")
names(aa_GEC_17)[1]="Running_Status"
names(aa_GEC_17)[2]="Greenfield_U_Status_17"
aa_GEC_17
## # A tibble: 1 x 2
## Running_Status Greenfield_U_Status_17
## <chr> <int>
## 1 Greenfield_U_Status_17 365
Brigton_Beach_U_Status_17<-cut(Dz_17GB$Brigton_2017_Total,breaks = c(0,100,10000),labels = c(0,1))
Brigton_Beach_U_Status_17<-cbind(Dz_17Brighton,Brigton_Beach_U_Status_17)
any(is.na(Brigton_Beach_U_Status_17))
## [1] TRUE
Brigton_Beach_U_Status_17[is.na(Brigton_Beach_U_Status_17)]=0
aa_BB_17<-count(Brigton_Beach_U_Status_17,"Brigton_Beach_U_Status_17")
names(aa_BB_17)[1]="Running_Status"
names(aa_BB_17)[2]="Brigton_Beach_U_Status_17"
aa_BB_17
## # A tibble: 1 x 2
## Running_Status Brigton_Beach_U_Status_17
## <chr> <int>
## 1 Brigton_Beach_U_Status_17 365
U_R_Status_17<-cbind(aa_G_17,aa_H_17[,2],aa_P_17[,2],aa_GEC_17[,2],aa_BB_17[,2])
names(U_R_Status_17)[2]="Goreway"
names(U_R_Status_17)[3]="Halton"
names(U_R_Status_17)[4]="Portlands"
names(U_R_Status_17)[5]="Greenfield"
names(U_R_Status_17)[6]="Brigton"
Barplot
head(U_R_Status_17)
## Running_Status Goreway Halton Portlands Greenfield Brigton
## 1 Goreway_U_Status_17 365 365 365 365 365
count<-as.matrix(U_R_Status_17[,-1])
uk<-c("0","1")
barplot(count)
ggplot
library(ggplot2)
Plant_Name <-rep(c("Goreway", "Halton", "Portlands", "Greenfield", "Brigton"), 2)
no<-c(U_R_Status_17[1,2],U_R_Status_17[1,3],U_R_Status_17[1,4],U_R_Status_17[1,5],U_R_Status_17[1,6])
yes<-c(U_R_Status_17[2,2],U_R_Status_17[2,3],U_R_Status_17[2,4],U_R_Status_17[2,5],U_R_Status_17[2,6])
Days <-c(no, yes)
Run_type <-c(rep("no", 5), rep("yes",5))
mydata <-data.frame(Plant_Name, Days)
mydata
## Plant_Name Days
## 1 Goreway 365
## 2 Halton 365
## 3 Portlands 365
## 4 Greenfield 365
## 5 Brigton 365
## 6 Goreway NA
## 7 Halton NA
## 8 Portlands NA
## 9 Greenfield NA
## 10 Brigton NA
p <-ggplot(mydata, aes(Plant_Name, Days))
p +geom_bar(stat= "identity",aes(fill=Run_type),position="dodge")+xlab("Plants Name")+ylab("Number of Days")+theme_bw()
ggplot(data=mydata, aes(x=Plant_Name, y=Days, fill=Run_type)) +
geom_bar(stat="identity", position=position_dodge())+
geom_text(aes(label=Days), vjust=1.6, color="white",
position = position_dodge(0.9), size=3.5)+
scale_fill_brewer(palette="Paired")+
theme_minimal()
## Warning: Removed 5 rows containing missing values (geom_bar).
## Warning: Removed 5 rows containing missing values (geom_text).