.Rhistory

library(gplots)
library(RColorBrewer)
source('evaluate_pi.R')
var_x_all = c('Environment:Site Direct Solar Radiation Rate per Area',
'Environment:Site Outdoor Air Drybulb Temperature',
'Environment:Site Outdoor Air Relative Humidity',
'People Occupant Count',
'Zone Mean Air Temperature')
var_y = c('Electricity:Building')
var_y = c('Electricity:Building')
var_x <- combinations[,c]
x_str <- paste(var_x, collapse = '_')
var_x<-var_x_all
var_x <- combinations[,c]
x_str <- paste(var_x, collapse = '_')
x_str <- str_replace_all(x_str, ":", " ")
y_str <- str_replace_all(var_y, ":", " ")
out_fname <- paste("results", y_str, x_str, sep='-')
out_fname <- str_replace_all(out_fname, 'Environment|Site|Outdoor|Rate per Area|Direct|People','')
out_fname <- gsub("\\s+", "", str_trim(out_fname))
load(paste(out_fname,'RData',sep='.'))
print(eval$plot_boxplot)
var_x_all = c('Environment:Site Direct Solar Radiation Rate per Area',
'Environment:Site Outdoor Air Drybulb Temperature',
'Environment:Site Outdoor Air Relative Humidity',
#'People Occupant Count',
'Zone Mean Air Temperature')
var_x<-var_x_all
x_str <- paste(var_x, collapse = '_')
x_str <- str_replace_all(x_str, ":", " ")
y_str <- str_replace_all(var_y, ":", " ")
out_fname <- paste("results", y_str, x_str, sep='-')
out_fname <- str_replace_all(out_fname, 'Environment|Site|Outdoor|Rate per Area|Direct|People','')
out_fname <- gsub("\\s+", "", str_trim(out_fname))
load(paste(out_fname,'RData',sep='.'))
print(eval$plot_errorbars)
print(eval$plot_boxplot)
print(eval$plot_errorbars)
var_x_all = c('Environment:Site Direct Solar Radiation Rate per Area',
'Environment:Site Outdoor Air Drybulb Temperature',
'Environment:Site Outdoor Air Relative Humidity',
'People Occupant Count',
'Zone Mean Air Temperature')
var_x<-var_x_all
x_str <- paste(var_x, collapse = '_')
x_str <- str_replace_all(x_str, ":", " ")
y_str <- str_replace_all(var_y, ":", " ")
out_fname <- paste("results", y_str, x_str, sep='-')
out_fname <- str_replace_all(out_fname, 'Environment|Site|Outdoor|Rate per Area|Direct|People','')
out_fname <- gsub("\\s+", "", str_trim(out_fname))
load(paste(out_fname,'RData',sep='.'))
print(eval$plot_errorbars)
var_x_all = c('Environment:Site Direct Solar Radiation Rate per Area',
'Environment:Site Outdoor Air Drybulb Temperature',
'Environment:Site Outdoor Air Relative Humidity',
#'People Occupant Count',
'Zone Mean Air Temperature')
var_y = c('Electricity:Building')
var_x<-var_x_all
x_str <- paste(var_x, collapse = '_')
x_str <- str_replace_all(x_str, ":", " ")
y_str <- str_replace_all(var_y, ":", " ")
out_fname <- paste("results", y_str, x_str, sep='-')
out_fname <- str_replace_all(out_fname, 'Environment|Site|Outdoor|Rate per Area|Direct|People','')
out_fname <- gsub("\\s+", "", str_trim(out_fname))
load(paste(out_fname,'RData',sep='.'))
print(eval$plot_boxplot)
var_x_all = c('Environment:Site Direct Solar Radiation Rate per Area',
'Environment:Site Outdoor Air Drybulb Temperature',
#'Environment:Site Outdoor Air Relative Humidity',
'People Occupant Count')
x_str <- paste(var_x, collapse = '_')
x_str <- str_replace_all(x_str, ":", " ")
y_str <- str_replace_all(var_y, ":", " ")
out_fname <- paste("results", y_str, x_str, sep='-')
out_fname <- str_replace_all(out_fname, 'Environment|Site|Outdoor|Rate per Area|Direct|People','')
out_fname <- gsub("\\s+", "", str_trim(out_fname))
load(paste(out_fname,'RData',sep='.'))
print(eval$plot_boxplot)
var_x<-var_x_all
x_str <- paste(var_x, collapse = '_')
x_str <- str_replace_all(x_str, ":", " ")
y_str <- str_replace_all(var_y, ":", " ")
out_fname <- paste("results", y_str, x_str, sep='-')
out_fname <- str_replace_all(out_fname, 'Environment|Site|Outdoor|Rate per Area|Direct|People','')
out_fname <- gsub("\\s+", "", str_trim(out_fname))
load(paste(out_fname,'RData',sep='.'))
print(eval$plot_boxplot)
################################################################################
# Evaluate occupancy by resolution
types = c('space', 'floor', 'single')
var_x = c('Environment:Site Direct Solar Radiation Rate per Area',
'Environment:Site Outdoor Air Drybulb Temperature',
'People Occupant Count')
var_y = c('Electricity:Building')
dat_ppl <- as.data.frame(matrix(nrow = 0, ncol = 4))
colnames(dat_ppl) <- c('picp', 'nrpiw','cvrmse','cwc')
for (i in (1:5)) {
for (type in types) {
x_str <- paste(var_x, collapse = '_')
x_str <- str_replace_all(x_str, ":", " ")
y_str <- str_replace_all(var_y, ":", " ")
out_fname <- paste("results", i, type, y_str, x_str, sep='-')
out_fname <- str_replace_all(out_fname, 'Environment|Site|Outdoor|Rate per Area|Direct|People','')
out_fname <- gsub("\\s+", "", str_trim(out_fname))
load(paste(out_fname,'.RData'))
temp = data.frame(picp=eval$picp, nrpiw=eval$nrpiw,
cvrmse=eval$cvrmse, cwc=eval$cwc)
rownames(temp) <- paste(type,i,sep='_')
dat_ppl <- rbind(dat_ppl,temp)
}
}
View(dat_ppl)
View(dat_ppl)
library(stringr)
library(graphics)
library(grDevices)
library(gplots)
library(RColorBrewer)
source('evaluate_pi.R')
################################################################################
# Evaluate occupancy by resolution
types = c('space', 'floor', 'single')
var_x = c('Environment:Site Direct Solar Radiation Rate per Area',
'Environment:Site Outdoor Air Drybulb Temperature',
'People Occupant Count')
var_y = c('Electricity:Building')
dat_ppl <- as.data.frame(matrix(nrow = 0, ncol = 6))
colnames(dat_ppl) <- c('run','resolution','picp', 'nrpiw','cvrmse','cwc')
for (i in (1:5)) {
for (type in types) {
x_str <- paste(var_x, collapse = '_')
x_str <- str_replace_all(x_str, ":", " ")
y_str <- str_replace_all(var_y, ":", " ")
out_fname <- paste("results", i, type, y_str, x_str, sep='-')
out_fname <- str_replace_all(out_fname, 'Environment|Site|Outdoor|Rate per Area|Direct|People','')
out_fname <- gsub("\\s+", "", str_trim(out_fname))
load(paste(out_fname,'.RData'))
temp = data.frame(run=i,resolution=type,
picp=eval$picp, nrpiw=eval$nrpiw,
cvrmse=eval$cvrmse, cwc=eval$cwc)
dat_ppl <- rbind(dat_ppl,temp)
}
}
ggplot(data=dat_ppl, aes(x=run, y=cvrmse, group=resolution, colour=resolution)) +
geom_line() +
geom_point()
ggplot(data=dat_ppl, aes(x=run, y=cvrmse, group=resolution, colour=resolution)) +
geom_bar()
ggplot(data=dat_ppl, aes(x=run, y=cvrmse, fill=resolution)) +
geom_bar()
ggplot(data=dat_ppl, aes(x=run, y=cvrmse, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge())
ggplot(data=dat_ppl, aes(x=run, y=picp, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge())
ggplot(data=dat_ppl, aes(x=run, y=picp, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black")
ggplot(data=dat_ppl, aes(x=run, y=cvrmse, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('CVRMSE [%]')
ggplot(data=dat_ppl, aes(x=run, y=picp, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Prediction Interval Coverage Probability')
ggplot(data=dat_ppl, aes(x=run, y=nrpiw, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Prediction Interval Coverage Probability (PICP)')
ggplot(data=dat_ppl, aes(x=run, y=nrpiw, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('NRPIW')
ggplot(data=dat_ppl, aes(x=run, y=nrpiw, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Normalized Root Mean Squared Prediction Interval Width (NRPIW)')
ggplot(data=dat_ppl, aes(x=run, y=picp, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Prediction Interval Coverage Probability (PICP)')
ggplot(data=dat_ppl, aes(x=run, y=nrpiw, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Normalized Root Mean Squared Prediction Interval Width (NRPIW)')
ggplot(data=dat_ppl, aes(x=run, y=cvrmse, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Mean Posterior CVRMSE [%]')
ggplot(data=dat_ppl, aes(x=run, y=cvrmse, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Mean Posterior CVRMSE [%]') +
theme(plot.axis=element_text(size=20))
?theme
ggplot(data=dat_ppl, aes(x=run, y=cvrmse, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Mean Posterior CVRMSE [%]') +
theme(axis.text=element_text(size=20))
ggplot(data=dat_ppl, aes(x=run, y=cvrmse, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Mean Posterior CVRMSE [%]') +
theme(text=element_text(size=20))
ggplot(data=dat_ppl, aes(x=run, y=picp, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Prediction Interval Coverage Probability (PICP)') +
theme(text=element_text(size=20))
ggplot(data=dat_ppl, aes(x=run, y=nrpiw, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Normalized Root Mean Squared Prediction Interval Width (NRPIW)')+
theme(text=element_text(size=20))
ggplot(data=dat_ppl, aes(x=run, y=nrpiw, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Normalized RMS Prediction Interval Width (NRPIW)')+
theme(text=element_text(size=20))
ggplot(data=dat_ppl, aes(x=run, y=cvrmse, fill=resolution)) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab('Mean Posterior CVRMSE [%]') +
theme(text=element_text(size=20))
run_num=5
load(paste('bc_data_byZone_',run_num,'.RData',sep=''))
# run different combinations
type = 'space' # options: c('single', 'floor', 'space')
var_y = c('Electricity:Building')
library(eplusr)
library(rstan)
source('functions_eplusr.R')
source('gp_utility.R')
source('evaluate_pi.R')
# Occupant Count ##########################################
ppl_all <- select_x(xf, xc, xpred, 'people')
ppl_basement <- select_x(xf, xc, xpred, 'BASEMENT.*people')
ppl_bot <- select_x(xf, xc, xpred, 'BOT.*people')
ppl_mid <- select_x(xf, xc, xpred, 'MID.*people')
ppl_top <- select_x(xf, xc, xpred, 'TOP.*people')
ppl_openOfc <- select_x(xf, xc, xpred, 'ZN_1.*people|ZN_3.*people')
ppl_privateOfc <- select_x(xf, xc, xpred, 'ZN_2.*people')
ppl_meeting <- select_x(xf, xc, xpred, 'ZN_4.*people')
ppl_corridor <- select_x(xf, xc, xpred, 'CORE.*people')
ppl_aux <- select_x(xf, xc, xpred, 'BASEMENT.*people')
# remove all people from xf, xc and xpred
xf_sub <- xf[,!grepl('people',names(xf),ignore.case = TRUE),with=FALSE]
xc_sub <- xc[,!grepl('people',names(xc),ignore.case = TRUE),with=FALSE]
xpred_sub <- xpred[,!grepl('people',names(xpred),ignore.case = TRUE),with=FALSE]
yf<-bc_data_sub$yf$`Electricity:Building [J](Hourly)`
# remove all people from xf, xc and xpred
xf_sub <- xf[,!grepl('people',names(xf),ignore.case = TRUE),with=FALSE]
xc_sub <- xc[,!grepl('people',names(xc),ignore.case = TRUE),with=FALSE]
xpred_sub <- xpred[,!grepl('people',names(xpred),ignore.case = TRUE),with=FALSE]
# seperate by floor
xf_sub$`Basement People Occupant Count` <- rowSums(ppl_basement$xf_sub)
xf_sub$`Bot People Occupant Count` <- rowSums(ppl_bot$xf_sub)
xf_sub$`Mid People Occupant Count` <- rowSums(ppl_mid$xf_sub)
xf_sub$`Top People Occupant Count` <- rowSums(ppl_top$xf_sub)
# seperate by space type
xf_sub$`Open Office People Occupant Count` <- rowSums(ppl_openOfc$xf_sub)
`Open Office People Occupant Count` <- rowSums(ppl_openOfc$xf_sub)
`Open Office People Occupant Count` <- rowSums(ppl_openOfc$xf_sub)
# seperate by space type
xf_sub$`Open Office People Occupant Count` <- rowSums(ppl_openOfc$xf_sub)
xf_sub$`Private Office People Occupant Count` <- rowSums(ppl_privateOfc$xf_sub)
xf_sub$`Meeting People Occupant Count` <- rowSums(ppl_meeting$xf_sub)
xf_sub$`Corridor People Occupant Count` <- rowSums(ppl_corridor$xf_sub)
xf_sub$`Auxiliary People Occupant Count` <- rowSums(ppl_aux$xf_sub)
plot(xf_sub$`Mid People Occupant Count`,yf)
library(eplusr)
library(rstan)
source('functions_eplusr.R')
source('gp_utility.R')
source('evaluate_pi.R')
load('bc_data_occSimulator.RData')
# sample size
n_train <- 150
n_test_train <- 0
n_test_outOfSample <- 100
n_com <- 150
n_iter <- 300
run_num <- 1
var_y = c('Electricity:Building')
# set x used for BC
var_x = c('Environment:Site Direct Solar Radiation Rate per Area',
'Environment:Site Outdoor Air Drybulb Temperature',
'People Occupant Count')
var_t = c("ZoneInfiltration",
"Lights",
"ElectricEquipment",
"Chiller")
tf_distr <- list('beta_shape_a'=rep(1,5),
'beta_shape_b'=rep(1,5))
bc_data <- get_system_data(var_y = var_y,
var_x = var_x,
var_t = var_t,
obs_field, obs_com, param_values)
xf<-bc_data$xf;xc<-bc_data$xc
bc_data_non_zero <- subset_bc_data(bc_data, num_sim, which(bc_data$yf != 0))
field_idx <- seq_len(nrow(bc_data_non_zero$yf))
subset_field_idx <- sample(field_idx, n_train)
subset_test_idx <- c(sample(subset_field_idx,n_test_train),
sample(setdiff(field_idx,subset_field_idx), n_test_outOfSample))
bc_test <- list(y_true = bc_data_non_zero$yf[subset_test_idx,],
x_pred = bc_data_non_zero$xf[subset_test_idx,])
xpred<-bc_test$x_pred
# Occupant Count ##########################################
ppl_all <- select_x(xf, xc, xpred, 'people')
ppl_basement <- select_x(xf, xc, xpred, 'BASEMENT.*people')
ppl_bot <- select_x(xf, xc, xpred, 'BOT.*people')
ppl_mid <- select_x(xf, xc, xpred, 'MID.*people')
ppl_top <- select_x(xf, xc, xpred, 'TOP.*people')
ppl_openOfc <- select_x(xf, xc, xpred, 'ZN_1.*people|ZN_3.*people')
ppl_privateOfc <- select_x(xf, xc, xpred, 'ZN_2.*people')
ppl_meeting <- select_x(xf, xc, xpred, 'ZN_4.*people')
ppl_corridor <- select_x(xf, xc, xpred, 'CORE.*people')
ppl_aux <- select_x(xf, xc, xpred, 'BASEMENT.*people')
# remove all people from xf, xc and xpred
xf_sub <- xf[,!grepl('people',names(xf),ignore.case = TRUE),with=FALSE]
xc_sub <- xc[,!grepl('people',names(xc),ignore.case = TRUE),with=FALSE]
xpred_sub <- xpred[,!grepl('people',names(xpred),ignore.case = TRUE),with=FALSE]
# sum as single X
xf_sub$`Total People Occupant Count` <- rowSums(ppl_all$xf_sub)
xc_sub$`Total People Occupant Count` <- rowSums(ppl_all$xc_sub)
xpred_sub$`Total People Occupant Count` <- rowSums(ppl_all$xpred_sub)
# seperate by floor
xf_sub$`Basement People Occupant Count` <- rowSums(ppl_basement$xf_sub)
xf_sub$`Bot People Occupant Count` <- rowSums(ppl_bot$xf_sub)
xf_sub$`Mid People Occupant Count` <- rowSums(ppl_mid$xf_sub)
xf_sub$`Top People Occupant Count` <- rowSums(ppl_top$xf_sub)
# seperate by space type
xf_sub$`Open Office People Occupant Count` <- rowSums(ppl_openOfc$xf_sub)
xf_sub$`Private Office People Occupant Count` <- rowSums(ppl_privateOfc$xf_sub)
xf_sub$`Meeting People Occupant Count` <- rowSums(ppl_meeting$xf_sub)
xf_sub$`Corridor People Occupant Count` <- rowSums(ppl_corridor$xf_sub)
xf_sub$`Auxiliary People Occupant Count` <- rowSums(ppl_aux$xf_sub)
plot(xf_sub$`Mid People Occupant Count`,bc_data$yf)
plot(xf_sub$`Mid People Occupant Count`,bc_data$yf$`Electricity:Building [J](Hourly)`)
plot(xf_sub$`Private Office People Occupant Count`,bc_data$yf$`Electricity:Building [J](Hourly)`)
plot(xf_sub$`Meeting People Occupant Count`,bc_data$yf$`Electricity:Building [J](Hourly)`)
setwd("~/Documents/GitHub/occupant-calibration")
install.packages("tidyverse")
library(tidyverse)
wifi <- paste(folder,"AS2_WIFI_2018.csv",sep='/')
folder <- "data"
wifi <- paste(folder,"AS2_WIFI_2018.csv",sep='/')
wifi <- read_csv(paste(folder,"AS2_WIFI_2018.csv",sep='/'))
View(wifi)
wifi <- read_csv(paste('data/AS2','WIFI','2018.csv',sep='_'))
btu <- read_csv(paste('data/AS2','BTU','2018.csv',sep='_'))
elec <- read_csv(paste('data/AS2','KWH','2018.csv',sep='_'))
View(wifi)
parse_date(wifi$X1)
parse_date(as.character(wifi$X1))
?gather
wifi_df <- gather(wifi,key = 'hour', value = "value")
View(wifi_df)
View(wifi)
wifi_df <- gather(wifi,key = 'hour', value = "value", `0`, `1`, `2`)
View(wifi_df)
wifi_df <- gather(wifi,key = 'hour', value = "value", seq(0,23))
seq(0,23)
wifi_df <- gather(wifi,key = 'hour', value = "value", 2:ncol(wifi))
View(wifi_df)
kwh <- read_csv(paste('data/AS2','KWH','2018.csv',sep='_'))
elec_tidy <- gather(kwh, key = 'hour', value = 'kwh', 2:ncol(kwh))
kwh <- read_csv(paste('data/AS2','KWH','2018.csv',sep='_'))
kwh <- read_csv(paste('resources/data/AS2','KWH','2018.csv',sep='_'))
wifi <- read_csv(paste('resources/data/AS2','WIFI','2018.csv',sep='_'))
btu <- read_csv(paste('resources/data/AS2','BTU','2018.csv',sep='_'))
kwh <- read_csv(paste('resources/data/AS2','KWH','2018.csv',sep='_'))
wifi <- read_csv(paste('resources/data/AS2','WIFI','2018.csv',sep='_'))
View(btu)
wifi <- read_csv(paste('resources/data/AS2','WIFI','2018.csv',sep='_'))
btu <- read_csv(paste('resources/data/AS2','BTU','2018.csv',sep='_'))
kwh <- read_csv(paste('resources/data/AS2','KWH','2018.csv',sep='_'))
?read_csv
View(btu)
spec(...)
wifi_tidy <- gather(wifi,key = 'hour', value = "count", 2:ncol(wifi))
btu_tidy <- gather(btu, key = 'hour', value = "btu", 2:ncol(btu))
elec_tidy <- gather(kwh, key = 'hour', value = 'kwh', 2:ncol(kwh))
kwh_tidy <- gather(kwh, key = 'hour', value = 'kwh', 2:ncol(kwh))
library(tidyverse)
folder <- "data"
wifi <- read_csv(paste('resources/data/AS2','WIFI','2018.csv',sep='_'))
btu <- read_csv(paste('resources/data/AS2','BTU','2018.csv',sep='_'))
kwh <- read_csv(paste('resources/data/AS2','KWH','2018.csv',sep='_'))
wifi_tidy <- gather(wifi,key = 'hour', value = "count", 2:ncol(wifi))
btu_tidy <- gather(btu, key = 'hour', value = "btu", 2:ncol(btu))
kwh_tidy <- gather(kwh, key = 'hour', value = 'kwh', 2:ncol(kwh))
paste(kwh_tidy$date,kwh_tidy$hour)
library(tidyverse)
parse_datetime(paste(kwh_tidy$date,kwh_tidy$hour), "d m y h")
parse_date_time(paste(kwh_tidy$date,kwh_tidy$hour), "d m y h")
library(lubridate)
parse_date_time(paste(kwh_tidy$date,kwh_tidy$hour), "d m y h")
parse_date_time(paste(kwh_tidy$date,kwh_tidy$hour), "d m y H")
paste(kwh_tidy$date,kwh_tidy$hour)
View(kwh_tidy)
?gather
cooling_df <- data.frame(timestep=parse_date_time(paste(cooling_tidy$date,cooling_tidy$hour), "d m y H"),
cooling_btu = btu_tidy$btu)
wifi <- read_csv(paste('resources/data/AS2','WIFI','2018.csv',sep='_'))
cooling <- read_csv(paste('resources/data/AS2','BTU','2018.csv',sep='_'))
electricity <- read_csv(paste('resources/data/AS2','KWH','2018.csv',sep='_'))
wifi_tidy <- gather(wifi,key = 'hour', value = "count", 2:ncol(wifi))
cooling_tidy <- gather(btu, key = 'hour', value = "btu", 2:ncol(btu))
electricity_tidy <- gather(kwh, key = 'hour', value = 'kwh', 2:ncol(kwh))
wifi_df <- data.frame(timestep=parse_date_time(paste(wifi_tidy$date, wifi_tidy$hour), "d m y H"),
wifi_count = wifi_tidy$count)
cooling_df <- data.frame(timestep=parse_date_time(paste(cooling_tidy$date,cooling_tidy$hour), "d m y H"),
cooling_btu = btu_tidy$btu)
View(cooling_tidy)
View(cooling_df)
cooling_df <- data.frame(timestep=parse_date_time(paste(cooling_tidy$date,cooling_tidy$hour), "d m y H"),
cooling_btu = cooling_tidy$btu)
View(wifi_df)
parse_date_time(paste(wifi_tidy$date, wifi_tidy$hour), "d m y H")
wifi_df$timestep
wifi_df$timestep[1]
x=paste(wifi_tidy$date, wifi_tidy$hour)
View(x)
x<-parse_date_time(x)
x<-parse_date_time(x, 'd m y H')
View(x)
x[1]
print(x[1])
x
order(x)
x_sorted <- x[order(x)]
x_sorted
dat <- bind_rows(electricity_df, cooling_df, wifi_df, id='timestep')
wifi <- read_csv(paste('resources/data/AS2','WIFI','2018.csv',sep='_'))
cooling <- read_csv(paste('resources/data/AS2','BTU','2018.csv',sep='_'))
electricity <- read_csv(paste('resources/data/AS2','KWH','2018.csv',sep='_'))
wifi_tidy <- gather(wifi,key = 'hour', value = "count", 2:ncol(wifi))
cooling_tidy <- gather(btu, key = 'hour', value = "btu", 2:ncol(btu))
electricity_tidy <- gather(kwh, key = 'hour', value = 'kwh', 2:ncol(kwh))
wifi_df <- data.frame(timestep=parse_date_time(paste(wifi_tidy$date, wifi_tidy$hour), "d m y H"),
wifi_count = wifi_tidy$count)
cooling_df <- data.frame(timestep=parse_date_time(paste(cooling_tidy$date,cooling_tidy$hour), "d m y H"),
cooling_btu = cooling_tidy$btu)
electricity_df <- data.frame(timestep=parse_date_time(paste(electricity_tidy$date,electricity_tidy$hour), "d m y H"),
electricity_kwh = electricity_tidy$kwh)
dat <- bind_rows(electricity_df, cooling_df, wifi_df, id='timestep')
dat <- bind_rows(list(electricity_df, cooling_df, wifi_df), id='timestep')
wifi <- read_csv(paste('resources/data/AS2','WIFI','2018.csv',sep='_'))
cooling <- read_csv(paste('resources/data/AS2','BTU','2018.csv',sep='_'))
electricity <- read_csv(paste('resources/data/AS2','KWH','2018.csv',sep='_'))
wifi_tidy <- gather(wifi,key = 'hour', value = "count", 2:ncol(wifi))
cooling_tidy <- gather(btu, key = 'hour', value = "btu", 2:ncol(btu))
electricity_tidy <- gather(kwh, key = 'hour', value = 'kwh', 2:ncol(kwh))
wifi_df <- data.frame(timestep=parse_date_time(paste(wifi_tidy$date, wifi_tidy$hour), "d m y H"),
wifi_count = wifi_tidy$count)
cooling_df <- data.frame(timestep=parse_date_time(paste(cooling_tidy$date,cooling_tidy$hour), "d m y H"),
cooling_btu = cooling_tidy$btu)
electricity_df <- data.frame(timestep=parse_date_time(paste(electricity_tidy$date,electricity_tidy$hour), "d m y H"),
electricity_kwh = electricity_tidy$kwh)
dat <- bind_rows(electricity_df, cooling_df, wifi_df)
View(dat)
View(electricity_df)
View(elec_tidy)
View(wifi_df)
View(wifi_tidy)
paste(wifi_tidy$date, wifi_tidy$hour+1)
paste(wifi_tidy$date, as.numeric(wifi_tidy$hour)+1)
wifi_df <- data.frame(timestep=parse_date_time(paste(wifi_tidy$date, as.numeric(wifi_tidy$hour)+1), "d m y H"),
wifi_count = wifi_tidy$count)
View(wifi_df)
library(tidyverse)
library(lubridate)
folder <- "data"
wifi <- read_csv(paste('resources/data/AS2','WIFI','2018.csv',sep='_'))
cooling <- read_csv(paste('resources/data/AS2','BTU','2018.csv',sep='_'))
electricity <- read_csv(paste('resources/data/AS2','KWH','2018.csv',sep='_'))
wifi_tidy <- gather(wifi,key = 'hour', value = "count", 2:ncol(wifi))
cooling_tidy <- gather(btu, key = 'hour', value = "btu", 2:ncol(btu))
electricity_tidy <- gather(kwh, key = 'hour', value = 'kwh', 2:ncol(kwh))
wifi_df <- data.frame(timestep=parse_date_time(paste(wifi_tidy$date, as.numeric(wifi_tidy$hour)+1), "d m y H"),
wifi_count = wifi_tidy$count)
cooling_df <- data.frame(timestep=parse_date_time(paste(cooling_tidy$date,as.numeric(cooling_tidy$hour)+1), "d m y H"),
cooling_btu = cooling_tidy$btu)
electricity_df <- data.frame(timestep=parse_date_time(paste(electricity_tidy$date,as.numeric(electricity_tidy$hour)+1), "d m y H"),
electricity_kwh = electricity_tidy$kwh)
dat <- bind_rows(electricity_df, cooling_df, wifi_df, id='timestep')
dat <- bind_rows(electricity_df, cooling_df, wifi_df)
View(dat)
one <- mtcars[1:4, ]
two <- mtcars[11:14, ]
View(one)
View(two)
bind_rows(one, two)
one
two
dat <- bind_cols(electricity_df, cooling_df, wifi_df, id='timestep')
dat <- full_join(electricity_df, cooling_df, wifi_df, id='timestep')
dat <- full_join(electricity_df, cooling_df, wifi_df, by='timestep')
View(dat)
dat <- full_join(list(electricity_df, cooling_df, wifi_df), by='timestep')
dat <- full_join(list(electricity_df, cooling_df, wifi_df), by='timestep', type='full')
library(plyr)
dat <- full_join(list(electricity_df, cooling_df, wifi_df), by='timestep', type='full')
dat <- join_all(list(electricity_df, cooling_df, wifi_df), by='timestep', type='full')
View(dat)
dat <- dat[order(dat$timestep),]
View(dat)
setwd("~/Documents/GitHub/occupant-calibration")
library(eplusr)
path_resources <- paste(getwd(), "resources","idf", sep = '/')
# idf for large reference building with occupancy schedule based on LBNL occupancy simulator
path_idf <- file.path(path_resources, "AS2.idf")
library(eplusr)
path_resources <- paste(getwd(), "resources","idf", sep = '/')
# idf for large reference building with occupancy schedule based on LBNL occupancy simulator
path_idf <- file.path(path_resources, "AS2.idf")
path_idf
source('~/Documents/GitHub/occupant-calibration/run_sim.R', echo=TRUE)
idf <- read_idf(path_idf)
path_idf
path_resources <- paste(getwd(), "resources", sep = '/')
# idf for large reference building with occupancy schedule based on LBNL occupancy simulator
path_idf <- file.path(path_resources, "idf/AS2.idf")
idf <- read_idf(path_idf)
epw <- read_epw(path_epw)