In CfSOtago/GREENGridEECA: Supports Analysis of NZ GREEN Grid data for EECA
knitr::opts_chunk$set(echo = FALSE) # by default turn off code echo
# Set start time ----
startTime <- proc.time()
# Local parameters ----
b2Kb <- 1024 #http://whatsabyte.com/P1/byteconverter.htm
b2Mb <- 1048576
plotLoc <- paste0(repoParams$repoLoc, "/docs/plots/") # where to put the plots (if any)
# Packages used in the report ----
rmdLibs <- c("ggplot2", # plots
"kableExtra", # fancy tables
"hms", # times
"skimr", # for skim
"tidyr" # for gather
)
GREENGridEECA::loadLibraries(rmdLibs)
# Local functions ----
\newpage
About
Citation
Report circulation:
- Public – this report is intended for publication following EECA approval.
License
History
Support
\newpage
Introduction
This report uses the GREEN Grid project [@stephenson_smart_2017] research data to analyse a variety of residential household appliances and their contribution to peak demand under several scenarios.
Data
# put all this here before you report on it
# this code _could_ go in the makeFile if you want...
#New DT
powerDT <- copy(origPowerDT) # need to copy as data.table works by reference https://www.rdocumentation.org/packages/data.table/versions/1.12.2/topics/copy
#DT modifications
#Excluding households based on Part A report: https://cfsotago.github.io/GREENGridEECA/#part-a--data-processing
exclude <- c("rf_14", "rf_25", "rf_26", "rf_43", "rf_46")
powerDT <- powerDT[!(linkID %in% exclude)]
# setting negative values to NA
powerDT <- powerDT[, meanPowerW := ifelse(meanPowerW <0, NA, meanPowerW)]
powerDT <- powerDT[, sdPowerW := ifelse(sdPowerW <0, NA, sdPowerW)]
powerDT <- powerDT[, minPowerW := ifelse(minPowerW <0, NA, minPowerW)]
powerDT <- powerDT[, maxPowerW := ifelse(maxPowerW <0, NA, maxPowerW)]
# set to NZ time from UTC
powerDT <- powerDT[, r_dateTime_nz := lubridate::as_datetime(r_dateTimeHalfHour,
tz = "Pacific/Auckland")] # this will be UTC unless you set this
#Define Winter/else
powerDT <- powerDT[, date := lubridate::date(r_dateTime_nz)]
powerDT <- powerDT[, obsHalfHour := hms::as.hms(r_dateTime_nz)]
#powerDT[, obsHalfHour := format(ymd_hms(r_dateTimeHalfHour), "%H:%M:%S")]
powerDT <- powerDT[, month := lubridate::month(r_dateTime_nz)]
powerDT <- powerDT[, peak := 0]
powerDT[, year := lubridate::year(r_dateTime_nz)] # so we can do per-year analysis
powerDT <- powerDT[month == 12 | month == 1 | month == 2, season := "Summer"]
powerDT <- powerDT[month == 3 | month == 4 | month == 5, season := "Autumn"]
powerDT <- powerDT[month == 6 | month == 7 | month == 8, season := "Winter"]
powerDT <- powerDT[month == 9 | month == 10 | month == 11, season := "Spring"]
#Setting times of peak demand
OP1S <- hms::as.hms("00:00:00")
OP1E <- hms::as.hms("16:30:00")
PS <- hms::as.hms("17:00:00")
PE <- hms::as.hms("21:00:00")
OP2S <- hms::as.hms("21:30:00")
OP2E <- hms::as.hms("23:30:00")
powerDT <- powerDT[, peak := ifelse(obsHalfHour >= OP1S & obsHalfHour <= OP1E,
"Off Peak 1",
NA)]
powerDT <- powerDT[, peak := ifelse(obsHalfHour >= OP2S & obsHalfHour <= OP2E,
"Off Peak 2",
peak)]
powerDT <- powerDT[, peak := ifelse(obsHalfHour >= PS & obsHalfHour <= PE,
"Peak",
peak)]
# this stops the RHS coercion errors
#Creating daily avergaes
dailyAvgDT <- copy(powerDT)
dailyAvgDT <- GREENGridEECA::labelEECACircuits(dailyAvgDT)
dailyAvgDT <- dailyAvgDT[, davgW := mean(meanPowerW, na.rm = TRUE), keyby =.(obsHalfHour, eecaCircuit, year, season)]
dailyAvgDT <- dailyAvgDT[, .(davgW=davgW), keyby=.(obsHalfHour, eecaCircuit, year, season)]
dailyAvgDT <- unique(dailyAvgDT)# Removing duplicates
dailyAvgDT$season <- factor(dailyAvgDT$season, levels = c("Spring","Summer",
"Autumn", "Winter"))#This puts the seasons in the right order in the plot
write.csv(dailyAvgDT, file = "GG_daily_average_power_multiple_years.csv")
myPlot <- ggplot2::ggplot(dailyAvgDT[eecaCircuit =="Lighting" &year =="2015"], aes(x=obsHalfHour, colour=season)) +
geom_line(aes(y=davgW))+
facet_grid(season ~ .) +
labs(x='Time of Day', y='Power in W') +
scale_x_time(breaks = c(hms::as.hms("00:00:00"),
hms::as.hms("04:00:00"),
hms::as.hms("08:00:00"),
hms::as.hms("12:00:00"),
hms::as.hms("16:00:00"),
hms::as.hms("20:00:00"),
hms::as.hms("24:00:00")))+
theme(text = element_text(family = "Cambria"),
axis.text.y = element_text(colour = "black"),
axis.text.x = element_text(colour = "black"))+
ggtitle("GG sample daily average lighting demand by season for 2015 in Watts")
myPlot
#ggsave("GG sample daily average lighting demand by season for 2015 in Watts.jpeg", dpi = 900)
myPlot <- ggplot2::ggplot(dailyAvgDT[eecaCircuit =="Hot water" &year =="2015"], aes(x=obsHalfHour, colour=season)) +
geom_line(aes(y=davgW))+
facet_grid(season ~ .) +
labs(x='Time of Day', y='Power in W') +
scale_x_time(breaks = c(hms::as.hms("00:00:00"),
hms::as.hms("04:00:00"),
hms::as.hms("08:00:00"),
hms::as.hms("12:00:00"),
hms::as.hms("16:00:00"),
hms::as.hms("20:00:00"),
hms::as.hms("24:00:00")))+
theme(text = element_text(family = "Cambria"),
axis.text.y = element_text(colour = "black"),
axis.text.x = element_text(colour = "black"))+
ggtitle("GG sample daily average hot water demand by season for 2015 in Watts")
myPlot
#ggsave("GG sample daily average hot water demand by season for 2015 in Watts.jpeg", dpi = 900)
myPlot <- ggplot2::ggplot(dailyAvgDT[eecaCircuit =="Oven" &year =="2015"], aes(x=obsHalfHour, colour=season)) +
geom_line(aes(y=davgW))+
facet_grid(season ~ .) +
labs(x='Time of Day', y='Power in W') +
scale_x_time(breaks = c(hms::as.hms("00:00:00"),
hms::as.hms("04:00:00"),
hms::as.hms("08:00:00"),
hms::as.hms("12:00:00"),
hms::as.hms("16:00:00"),
hms::as.hms("20:00:00"),
hms::as.hms("24:00:00")))+
theme(text = element_text(family = "Cambria"),
axis.text.y = element_text(colour = "black"),
axis.text.x = element_text(colour = "black"))+
ggtitle("GG sample daily average oven demand by season for 2015 in Watts")
myPlot
#ggsave("GG sample daily average oven demand by season for 2015 in Watts.jpeg", dpi = 900)
myPlot <- ggplot2::ggplot(dailyAvgDT[eecaCircuit =="Heat Pump or Heating" &year =="2015"], aes(x=obsHalfHour, colour=season)) +
geom_line(aes(y=davgW))+
facet_grid(season ~ .) +
labs(x='Time of Day', y='Power in W') +
scale_x_time(breaks = c(hms::as.hms("00:00:00"),
hms::as.hms("04:00:00"),
hms::as.hms("08:00:00"),
hms::as.hms("12:00:00"),
hms::as.hms("16:00:00"),
hms::as.hms("20:00:00"),
hms::as.hms("24:00:00")))+
theme(text = element_text(family = "Cambria"),
axis.text.y = element_text(colour = "black"),
axis.text.x = element_text(colour = "black"))+
ggtitle("GG sample daily average heat pump demand by season for 2015 in Watts")
myPlot
#ggsave("GG sample daily average heat pump demand by season for 2015 in Watts.jpeg", dpi = 900)
CfSOtago/GREENGridEECA documentation built on May 1, 2022, 8:08 p.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set(echo = FALSE) # by default turn off code echo
# Set start time ---- startTime <- proc.time() # Local parameters ---- b2Kb <- 1024 #http://whatsabyte.com/P1/byteconverter.htm b2Mb <- 1048576 plotLoc <- paste0(repoParams$repoLoc, "/docs/plots/") # where to put the plots (if any) # Packages used in the report ---- rmdLibs <- c("ggplot2", # plots "kableExtra", # fancy tables "hms", # times "skimr", # for skim "tidyr" # for gather ) GREENGridEECA::loadLibraries(rmdLibs) # Local functions ----
\newpage
About
Citation
Report circulation:
- Public – this report is intended for publication following EECA approval.
License
History
Support
\newpage
Introduction
This report uses the GREEN Grid project [@stephenson_smart_2017] research data to analyse a variety of residential household appliances and their contribution to peak demand under several scenarios.
Data
# put all this here before you report on it # this code _could_ go in the makeFile if you want... #New DT powerDT <- copy(origPowerDT) # need to copy as data.table works by reference https://www.rdocumentation.org/packages/data.table/versions/1.12.2/topics/copy #DT modifications #Excluding households based on Part A report: https://cfsotago.github.io/GREENGridEECA/#part-a--data-processing exclude <- c("rf_14", "rf_25", "rf_26", "rf_43", "rf_46") powerDT <- powerDT[!(linkID %in% exclude)] # setting negative values to NA powerDT <- powerDT[, meanPowerW := ifelse(meanPowerW <0, NA, meanPowerW)] powerDT <- powerDT[, sdPowerW := ifelse(sdPowerW <0, NA, sdPowerW)] powerDT <- powerDT[, minPowerW := ifelse(minPowerW <0, NA, minPowerW)] powerDT <- powerDT[, maxPowerW := ifelse(maxPowerW <0, NA, maxPowerW)] # set to NZ time from UTC powerDT <- powerDT[, r_dateTime_nz := lubridate::as_datetime(r_dateTimeHalfHour, tz = "Pacific/Auckland")] # this will be UTC unless you set this #Define Winter/else powerDT <- powerDT[, date := lubridate::date(r_dateTime_nz)] powerDT <- powerDT[, obsHalfHour := hms::as.hms(r_dateTime_nz)] #powerDT[, obsHalfHour := format(ymd_hms(r_dateTimeHalfHour), "%H:%M:%S")] powerDT <- powerDT[, month := lubridate::month(r_dateTime_nz)] powerDT <- powerDT[, peak := 0] powerDT[, year := lubridate::year(r_dateTime_nz)] # so we can do per-year analysis powerDT <- powerDT[month == 12 | month == 1 | month == 2, season := "Summer"] powerDT <- powerDT[month == 3 | month == 4 | month == 5, season := "Autumn"] powerDT <- powerDT[month == 6 | month == 7 | month == 8, season := "Winter"] powerDT <- powerDT[month == 9 | month == 10 | month == 11, season := "Spring"] #Setting times of peak demand OP1S <- hms::as.hms("00:00:00") OP1E <- hms::as.hms("16:30:00") PS <- hms::as.hms("17:00:00") PE <- hms::as.hms("21:00:00") OP2S <- hms::as.hms("21:30:00") OP2E <- hms::as.hms("23:30:00") powerDT <- powerDT[, peak := ifelse(obsHalfHour >= OP1S & obsHalfHour <= OP1E, "Off Peak 1", NA)] powerDT <- powerDT[, peak := ifelse(obsHalfHour >= OP2S & obsHalfHour <= OP2E, "Off Peak 2", peak)] powerDT <- powerDT[, peak := ifelse(obsHalfHour >= PS & obsHalfHour <= PE, "Peak", peak)] # this stops the RHS coercion errors
#Creating daily avergaes dailyAvgDT <- copy(powerDT) dailyAvgDT <- GREENGridEECA::labelEECACircuits(dailyAvgDT) dailyAvgDT <- dailyAvgDT[, davgW := mean(meanPowerW, na.rm = TRUE), keyby =.(obsHalfHour, eecaCircuit, year, season)] dailyAvgDT <- dailyAvgDT[, .(davgW=davgW), keyby=.(obsHalfHour, eecaCircuit, year, season)] dailyAvgDT <- unique(dailyAvgDT)# Removing duplicates dailyAvgDT$season <- factor(dailyAvgDT$season, levels = c("Spring","Summer", "Autumn", "Winter"))#This puts the seasons in the right order in the plot write.csv(dailyAvgDT, file = "GG_daily_average_power_multiple_years.csv")
myPlot <- ggplot2::ggplot(dailyAvgDT[eecaCircuit =="Lighting" &year =="2015"], aes(x=obsHalfHour, colour=season)) + geom_line(aes(y=davgW))+ facet_grid(season ~ .) + labs(x='Time of Day', y='Power in W') + scale_x_time(breaks = c(hms::as.hms("00:00:00"), hms::as.hms("04:00:00"), hms::as.hms("08:00:00"), hms::as.hms("12:00:00"), hms::as.hms("16:00:00"), hms::as.hms("20:00:00"), hms::as.hms("24:00:00")))+ theme(text = element_text(family = "Cambria"), axis.text.y = element_text(colour = "black"), axis.text.x = element_text(colour = "black"))+ ggtitle("GG sample daily average lighting demand by season for 2015 in Watts") myPlot #ggsave("GG sample daily average lighting demand by season for 2015 in Watts.jpeg", dpi = 900)
myPlot <- ggplot2::ggplot(dailyAvgDT[eecaCircuit =="Hot water" &year =="2015"], aes(x=obsHalfHour, colour=season)) + geom_line(aes(y=davgW))+ facet_grid(season ~ .) + labs(x='Time of Day', y='Power in W') + scale_x_time(breaks = c(hms::as.hms("00:00:00"), hms::as.hms("04:00:00"), hms::as.hms("08:00:00"), hms::as.hms("12:00:00"), hms::as.hms("16:00:00"), hms::as.hms("20:00:00"), hms::as.hms("24:00:00")))+ theme(text = element_text(family = "Cambria"), axis.text.y = element_text(colour = "black"), axis.text.x = element_text(colour = "black"))+ ggtitle("GG sample daily average hot water demand by season for 2015 in Watts") myPlot #ggsave("GG sample daily average hot water demand by season for 2015 in Watts.jpeg", dpi = 900)
myPlot <- ggplot2::ggplot(dailyAvgDT[eecaCircuit =="Oven" &year =="2015"], aes(x=obsHalfHour, colour=season)) + geom_line(aes(y=davgW))+ facet_grid(season ~ .) + labs(x='Time of Day', y='Power in W') + scale_x_time(breaks = c(hms::as.hms("00:00:00"), hms::as.hms("04:00:00"), hms::as.hms("08:00:00"), hms::as.hms("12:00:00"), hms::as.hms("16:00:00"), hms::as.hms("20:00:00"), hms::as.hms("24:00:00")))+ theme(text = element_text(family = "Cambria"), axis.text.y = element_text(colour = "black"), axis.text.x = element_text(colour = "black"))+ ggtitle("GG sample daily average oven demand by season for 2015 in Watts") myPlot #ggsave("GG sample daily average oven demand by season for 2015 in Watts.jpeg", dpi = 900)
myPlot <- ggplot2::ggplot(dailyAvgDT[eecaCircuit =="Heat Pump or Heating" &year =="2015"], aes(x=obsHalfHour, colour=season)) + geom_line(aes(y=davgW))+ facet_grid(season ~ .) + labs(x='Time of Day', y='Power in W') + scale_x_time(breaks = c(hms::as.hms("00:00:00"), hms::as.hms("04:00:00"), hms::as.hms("08:00:00"), hms::as.hms("12:00:00"), hms::as.hms("16:00:00"), hms::as.hms("20:00:00"), hms::as.hms("24:00:00")))+ theme(text = element_text(family = "Cambria"), axis.text.y = element_text(colour = "black"), axis.text.x = element_text(colour = "black"))+ ggtitle("GG sample daily average heat pump demand by season for 2015 in Watts") myPlot #ggsave("GG sample daily average heat pump demand by season for 2015 in Watts.jpeg", dpi = 900)
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