ecobee/ecobee.R

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#setup ----
library(tidyverse)
library(lubridate)
library(httr)
library(jsonlite)
library(plotly)
library(scales)
library(readODS)
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setwd("~/Documents/dataProjects/ecobee")
pause = function()
{
if (interactive())
{
invisible(readline(prompt = "Press <Enter> to continue..."))
}
else
{
cat("Press <Enter> to continue...")
invisible(readLines(file("stdin"), 1))
}
}
#make a file called 'apiKey' in your working directory that has your api key for your ecobee
if(file.exists('authTokens.Rda')) {
#if the authToken file already exists, then refresh it
load(file = "authTokens.Rda")
url <- paste0("https://api.ecobee.com/token?grant_type=refresh_token&code=",
authTokens$refreshToken,
"&client_id=",
read_file('apiKey'))
res <- content(httr::GET(url = url))
authTokens <- data.frame(accessToken = res$access_token,
refreshToken = res$refresh_token)
save(authTokens, file = "authTokens.Rda")
access_token <- authTokens$accessToken
remove(res, url, authTokens)
} else {
#if the authToken file does not exist than set it up, this will require adding the app to your ecobee account
permissions <- 'smartRead'
url <- paste0('https://api.ecobee.com/authorize?response_type=ecobeePin&client_id=',read_file('apiKey'),'&scope=',permissions)
res1 <- content(httr::GET(url = url))
message(paste("add a new app to your ecobee account and authorize it with this PIN -", res1$ecobeePin))
pause()
url2 <- paste0("https://api.ecobee.com/token?grant_type=ecobeePin&code=",res1$code,"&client_id=",read_file('apiKey'))
res2 <- content(httr::GET(url = url2))
authTokens <- data.frame(accessToken = res2$access_token, refreshToken = res2$refresh_token)
save(authTokens, file = "authTokens.Rda")
access_token <- authTokens$accessToken
rm(permissions, url, res1, url2, res2)
}
# load data ----
startdate <- ymd('2021-11-13')
enddate <- Sys.Date()
columns <- c('zoneClimate',
'compHeat1',
'auxHeat1',
'compCool1',
'fan',
'outdoorTemp',
'outdoorHumidity',
'zoneAveTemp',
'zoneHeatTemp',
'zoneCoolTemp',
'zoneHumidity',
'zoneHvacMode')
months <- data.frame(start = seq(floor_date(startdate, unit = "months"),
floor_date(enddate, unit = "months"),
length.out = interval(floor_date(startdate, unit = "months"), enddate) %/% months(1) + 1))
months <- months %>%
mutate(end = start + months(1) - 1)
ecobee_new <- list(NULL)
for(i in 1:nrow(months)){
message(paste("Downloading", months$start[[i]], "to", months$end[[i]]))
url <- paste0('https://api.ecobee.com/1/runtimeReport?format=json&body={"startDate":',
paste0('\"',as.Date(months$start[i]),'\",'),
'"endDate":',
paste0('\"',as.Date(months$end[i]),'\",'),
'"columns":',
paste0('\"',paste0(columns, collapse = ","),'\"'),
',"selection":{"selectionType":"thermostats","selectionMatch":"413788899054"}}')
ecobee_month <- httr::GET(
url = url,
add_headers('Content-Type' = 'application/json;charset=UTF-8',
'Authorization' = paste0('Bearer ', access_token)
)
) %>% httr::content()
# parse data ----
ecobee_new[[i]] <- tibble(ecobee_month[["reportList"]][[1]][["rowList"]]) %>%
separate(sep = ",",
col = 1,
into = c('date', 'time', columns),
convert=TRUE) %>%
mutate("duration_sec" = 5*60,
"dateTime" = ymd_hms(paste(date, time)),
outdoortemp_bin = round(outdoorTemp)) %>%
filter(ymd(date) <= Sys.Date())
}
ecobee <- bind_rows(ecobee_new)
save(ecobee, file = 'data/ecobee.Rda')
daterange <- data.frame(start = min(ecobee$date), end = max(ecobee$date))
# import utility data ----
utility_data <- read_ods(path = "~/Documents/1333 E 6th St/Expenses.ods", sheet = 2)
utility_data <- utility_data %>%
mutate(start_date = mdy(`start date`),
end_date = mdy(`end date`))
for(i in 1:nrow(utility_data)){
utility_data[i, "heat_pump_hours"] <- sum(ecobee %>%
filter(dateTime > utility_data$start_date[i],
dateTime <= utility_data$end_date[i]) %>%
pull(compHeat1), na.rm = TRUE)/3600
utility_data[i, "heat_aux_hours"] <- sum(ecobee %>%
filter(dateTime > utility_data$start_date[i],
dateTime <= utility_data$end_date[i]) %>%
pull(auxHeat1), na.rm = TRUE)/3600
utility_data[i, "cool_pump_hours"] <- sum(ecobee %>%
filter(dateTime > utility_data$start_date[i],
dateTime <= utility_data$end_date[i]) %>%
pull(compCool1), na.rm = TRUE)/3600
}
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# color scale ----
temp_types <- data.frame(column = c('zoneAveTemp', 'zoneHeatTemp', 'zoneCoolTemp', 'outdoorTemp'),
label = c('Indoor', 'Set (heat)', 'Set (cool)', 'Outdoor'),
color = c('black', 'orange', 'blue', 'grey'))
equipment_types <- data.frame(column = c('compHeat1', 'auxHeat1', 'compCool1', 'fan'),
label = c('Heat pump (heat)', 'Aux heat', 'Heat pump (cool)', 'Fan'),
color = c('orange', 'red', 'blue', 'grey'),
offset = c(0,1,2,3))
equipment_y <- max(ecobee$zoneCoolTemp, ecobee$outdoorTemp, na.rm = TRUE) + 1
# make plots ----
ggplot() +
geom_line(data = ecobee %>% pivot_longer(cols = c(zoneAveTemp, outdoorTemp, zoneHeatTemp, zoneCoolTemp), names_to = "temp_type", values_to = "temp"),
aes(x = dateTime,
y = temp,
color = temp_type))+
geom_rect(data = ecobee %>% pivot_longer(cols = c(compHeat1, auxHeat1, compCool1), names_to = "equipment", values_to = "active_sec") %>% filter(active_sec > 0),
aes(xmin = dateTime,
xmax = dateTime + active_sec,
ymin = equipment_y + equipment_types[match(equipment, equipment_types$column), 4],
ymax = equipment_y + equipment_types[match(equipment, equipment_types$column), 4] + 1,
fill = equipment)) +
scale_color_manual(labels = temp_types$label, values = temp_types %>% pull(color, column)) +
scale_fill_manual(labels = equipment_types$label, values = equipment_types %>% pull(color, column)) +
theme(legend.title = element_blank()) +
labs(x = 'Date',
y = 'Temperature (\u00B0F)')
ggplot() +
geom_line(data = ecobee %>% filter(date > Sys.Date() - 3) %>% pivot_longer(cols = c(zoneAveTemp, outdoorTemp, zoneHeatTemp, zoneCoolTemp), names_to = "temp_type", values_to = "temp"),
aes(x = dateTime,
y = temp,
color = temp_type))+
geom_rect(data = ecobee %>% filter(date > Sys.Date() - 3) %>% pivot_longer(cols = c(compHeat1, auxHeat1, compCool1), names_to = "equipment", values_to = "active_sec") %>% filter(active_sec > 0),
aes(xmin = dateTime,
xmax = dateTime + active_sec,
ymin = equipment_y + equipment_types[match(equipment, equipment_types$column), 4],
ymax = equipment_y + equipment_types[match(equipment, equipment_types$column), 4] + 1,
fill = equipment)) +
scale_color_manual(labels = temp_types$label, values = temp_types %>% pull(color, column)) +
scale_fill_manual(labels = equipment_types$label, values = equipment_types %>% pull(color, column)) +
theme(legend.title = element_blank()) +
labs(x = 'Date',
y = 'Temperature (\u00B0F)')
ggplot(data = ecobee %>%
filter(date != "2021-11-13",
date != today()) %>%
filter(zoneClimate != "") %>%
pivot_longer(cols = c(compHeat1, auxHeat1, compCool1, fan), names_to = "equipment", values_to = "active_sec") %>%
group_by(outdoortemp_bin, equipment, zoneClimate) %>%
summarize(percent_active = sum(active_sec, na.rm = TRUE)/sum(duration_sec, na.rm = TRUE),
n = n()),
aes(x = outdoortemp_bin,
y = percent_active,
color = equipment,
shape = zoneClimate)) +
geom_point(aes(size = n)) +
geom_smooth(aes(linetype = zoneClimate),
se = FALSE) +
scale_color_manual(labels = equipment_types$label, values = equipment_types %>% pull(color, column)) +
scale_y_continuous(labels = label_percent(), expand = expansion(mult = c(0,0))) +
labs(x = 'Outdoor Temperature (\u00B0F)',
y = 'Percent of the time the equipment runs')
ggplot() +
geom_col(data = ecobee %>% pivot_longer(cols = c(compHeat1, auxHeat1, compCool1), names_to = "equipment", values_to = "active_sec"),
aes(x = as.Date(dateTime) + hours(12),
y = active_sec/60/60,
fill = equipment)) +
scale_fill_manual(labels = equipment_types$label, values = equipment_types %>% pull(color, column)) +
geom_line(data = ecobee,
aes(x = dateTime,
y = outdoorTemp/5,
color = 'Outdoor')) +
labs(x = 'date',
y = 'Time equipment runs (hours)') +
scale_y_continuous(sec.axis = sec_axis(trans = ~ .x*5,
name = "Temperature (\u00B0F)"))
ggplot() +
geom_col(data = ecobee %>% pivot_longer(cols = c(compHeat1, auxHeat1, compCool1), names_to = "equipment", values_to = "active_sec"),
aes(x = floor_date(dateTime, 'weeks'),
y = active_sec/60/60,
fill = equipment)) +
scale_fill_manual(labels = equipment_types$label, values = equipment_types %>% pull(color, column)) +
geom_line(data = ecobee,
aes(x = dateTime,
y = outdoorTemp/5*7,
color = 'Outdoor')) +
labs(x = 'date',
y = 'Time equipment runs (hours)') +
scale_y_continuous(sec.axis = sec_axis(trans = ~ .x*5/7,
name = "Temperature (\u00B0F)"))
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ggplot(data = ecobee %>%
filter(date != "2021-11-13",
date != today()) %>%
group_by(date) %>%
summarise(outdoorTemp = mean(outdoorTemp),
compHeat1 = sum(compHeat1, na.rm = TRUE),
compCool1 = sum(compCool1, na.rm = TRUE),
auxHeat1 = sum(auxHeat1, na.rm = TRUE)) %>%
pivot_longer(cols = c(compHeat1, compCool1, auxHeat1), names_to = 'equipment', values_to = 'active_sec') %>%
filter(active_sec > 0),
aes(x = outdoorTemp,
y = active_sec/60/60,
color = equipment)) +
geom_point() +
geom_smooth(se = FALSE) +
scale_y_continuous(limits = c(0, 24), expand = expansion(mult = c(0,0))) +
scale_color_manual(labels = equipment_types$label, values = equipment_types %>% pull(color, column)) +
labs(x = 'Outdoor Temperature (\u00B0F)',
y = 'Time equipment runs (hours)')