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This code is meant to match PPL data to building information and reorganize it to generate electricity data by building (or combinations of buildings for those metered together).
The main steps involved are:
library(tidyverse) # load tidyverse── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(DT) # library to create tables
library(scales) # library to format dollars
Attaching package: 'scales'
The following object is masked from 'package:purrr':
discard
The following object is masked from 'package:readr':
col_factorkwh <- read.csv("./data/FY25 PPL Electricity Data.csv", strip.white = T) # load PPL data
buildings <- read.csv("./keys/fy25_building_list.csv", strip.white = T) # load building information
key <- read.csv("./keys/meter_building_key.csv", strip.white = T) # load key to link meters to buildings
occupants <- read.csv("./keys/housing_counts.csv", strip.white = T)
# generate electricity totals by meter for the year
kwh_annual <- kwh %>%
group_by(meter_origin) %>%
summarize(kwh = sum(total_kwh, na.rm = T))
# filter to AY24/25 and average across the year
occu_agg <- occupants %>%
mutate(sem_yr = paste(semester, year)) %>%
filter(sem_yr %in% c("Fall 2024", "Spring 2025")) %>%
group_by(NAME, sem_yr) %>%
summarize(occupants = sum(occupants),
n = n()) %>%
group_by(NAME) %>%
summarize(occupants = mean(occupants))`summarise()` has grouped output by 'NAME'. You can override using the
`.groups` argument.# store conversion factors
dollars_kwh <- 0.0813
co2_kg_kwh <- 0.299511787str(kwh)'data.frame': 55138 obs. of 6 variables:
$ account_number: num 1e+09 1e+09 1e+09 1e+09 1e+09 ...
$ meter_origin : chr "152 W Louther St *Apt 2" "152 W Louther St *Apt 2" "152 W Louther St *Apt 2" "152 W Louther St *Apt 2" ...
$ meter_number : int 300056642 300056642 300056642 300056642 300056642 300056642 300056642 300056642 300056642 300056642 ...
$ date : chr "7/18/24" "7/19/24" "7/20/24" "7/21/24" ...
$ total_kwh : num 28 26.4 26.5 26.3 27.2 ...
$ ave_temp : int 78 76 78 81 75 78 80 79 76 76 ...str(buildings)'data.frame': 133 obs. of 13 variables:
$ banner_code: chr "1110" "1540" "1035" "1195" ...
$ NAME : chr "162-164 Dickinson Ave." "46 S. West St." "57 S. College" "61 N. West St." ...
$ TYPE : chr "Academic" "Academic" "Academic" "Academic" ...
$ type_new : chr "Academic" "Academic" "Academic" "Academic" ...
$ occupant : chr "DEAL Archeology Labs" "Music office/rehearsal space" "Education Dept. Offices" "House Divided/Diamond 6" ...
$ address : chr "162-164 Dickinson Ave." "46 S. West St." "57 S. College St." "61 N. West St." ...
$ date_constr: chr "" "" "" "1895" ...
$ date_acqd : int 1998 1982 1979 1993 NA 1946 NA NA 1987 1986 ...
$ date_reno : int 2010 NA NA NA 2009 NA 1940 1997 NA NA ...
$ sqft : int 2500 1775 4576 3091 29133 12600 33692 4000 6638 3138 ...
$ rental : int 0 0 0 0 0 0 0 1 0 0 ...
$ main_meter : int 0 0 0 1 1 1 1 0 1 1 ...
$ weis_meter : int 0 0 0 0 0 0 0 0 0 0 ...str(key)'data.frame': 151 obs. of 2 variables:
$ meter_origin: chr "100 S College St" "100 S West St" "101 S College St" "102 S West St *Apt 1" ...
$ NAME : chr "Drayer Hall" "100 S. West St." "Landis House" "100 S. West St." ...buildings_main <- buildings %>%
filter(main_meter == 1) %>% # filter to main meter buildings
summarize(sqft = sum(sqft, na.rm = T)) %>% # sum sqft for these buildings
mutate(NAME = "Main Meter",
type_new = "Main Meter",
occupant = "Mixed",
address = "Many different",
date_constr = NA,
date_acqd = NA,
date_reno = NA,
rental = 0)
buildings_weis <- buildings %>%
filter(weis_meter == 1) %>% # filter to weis buildings
summarize(sqft = sum(sqft, na.rm = T)) %>%
mutate(NAME = "Weis Meter",
type_new = "Weis Meter",
occupant = "Mixed",
address = "Many different",
date_constr = NA,
date_acqd = NA,
date_reno = NA,
rental = 0)
# Generate clean building lookup table
buildings_clean <- buildings %>%
filter(main_meter == 0 & weis_meter == 0) %>% # keep only buildings on individual meters
select(NAME, type_new, occupant, address, date_constr, date_acqd, date_reno, sqft, rental) %>% # select relevant columns
rbind(buildings_main, buildings_weis)joined <- kwh_annual %>%
left_join(key, by = "meter_origin") %>% # join to key to match meters to building names
full_join(buildings_clean, by = "NAME", relationship = "many-to-one") %>% # join to building info by name
filter(!is.na(meter_origin)) %>% # filter out those with NA values for meter
group_by(type_new, NAME, occupant, date_constr, date_acqd, date_reno, rental) %>% # group by building
summarize(kwh = sum(kwh, na.rm = T), # sum kwh by building
sqft = mean(sqft, na.rm = T)) %>% # take mean to preserve sqft data
filter(!is.na(type_new)) %>% # filter out those with NA for type
left_join(occu_agg, by = "NAME") %>%
mutate(kwh_sqft = kwh/sqft, # calculate kwh per sqft
kwh_person = kwh/occupants,
dollars = kwh*dollars_kwh,
ghg_kgCO2 = kwh*co2_kg_kwh,
type = type_new) %>%
ungroup() %>%
mutate_all(~ifelse(is.nan(.), NA, .)) `summarise()` has grouped output by 'type_new', 'NAME', 'occupant',
'date_constr', 'date_acqd', 'date_reno'. You can override using the `.groups`
argument.joined_daily <- kwh %>%
left_join(key, by = "meter_origin") %>% # join to key to match meters to building names
full_join(buildings_clean, by = "NAME", relationship = "many-to-one") %>% # join to building info by name
filter(!is.na(meter_origin)) %>%
group_by(type_new, NAME, date) %>%
summarize(kwh = sum(total_kwh, na.rm = T),
sqft = mean(sqft, na.rm = T),
ave_temp = mean(ave_temp, na.rm = T)) %>%
filter(!is.na(type_new)) %>%
mutate(kwh_sqft = kwh/sqft, # calculate kwh per sqft
type = type_new) %>%
ungroup() %>%
select(-type_new) %>%
mutate_all(~ifelse(is.nan(.), NA, .)) # convert NaN to NA`summarise()` has grouped output by 'type_new', 'NAME'. You can override using
the `.groups` argument.joined_pretty <- joined %>%
mutate(kwh = round(kwh, digits = 0),
dollars = paste("$",round(dollars, digits = 0)),
sqft = round(sqft, digits = 0),
kwh_sqft = round(kwh_sqft, digits = 1),
occupants = round(occupants, digits = 0),
kwh_person = round(kwh_person, digits = 0)) %>%
select(type, NAME, date_constr, date_reno, rental, kwh, dollars, sqft, kwh_sqft, occupants, kwh_person) %>%
arrange(desc(sqft))
datatable(joined_pretty,
filter = 'top',
rownames = FALSE,
colnames = c("Building\ntype","Name","Date\nconstructed","Date\nrenovated",
"Rental?","kWh", "Est cost", "Square\nfootage","kWh\nper sqft",
"Acad yr\noccupants", "kWh\nper person"))write.csv(joined_daily, "./output/kwh_daily.csv", row.names = F)
write.csv(joined, "./output/kwh_annual.csv", row.names = F)
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.7.8
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] scales_1.3.0 DT_0.33 lubridate_1.9.3 forcats_1.0.0
[5] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[9] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
[13] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] sass_0.4.8 utf8_1.2.4 generics_0.1.3 stringi_1.8.3
[5] hms_1.1.3 digest_0.6.37 magrittr_2.0.3 timechange_0.3.0
[9] evaluate_0.23 grid_4.3.2 fastmap_1.1.1 rprojroot_2.0.4
[13] jsonlite_1.8.8 processx_3.8.3 whisker_0.4.1 ps_1.7.5
[17] promises_1.2.1 httr_1.4.7 fansi_1.0.6 crosstalk_1.2.1
[21] jquerylib_0.1.4 cli_3.6.2 rlang_1.1.3 ellipsis_0.3.2
[25] munsell_0.5.0 withr_3.0.0 cachem_1.0.8 yaml_2.3.8
[29] tools_4.3.2 tzdb_0.4.0 colorspace_2.1-0 httpuv_1.6.13
[33] vctrs_0.6.5 R6_2.5.1 lifecycle_1.0.4 git2r_0.33.0
[37] htmlwidgets_1.6.4 fs_1.6.3 pkgconfig_2.0.3 callr_3.7.3
[41] pillar_1.9.0 bslib_0.6.1 later_1.3.2 gtable_0.3.4
[45] glue_1.7.0 Rcpp_1.1.0 xfun_0.41 tidyselect_1.2.0
[49] rstudioapi_0.16.0 knitr_1.45 htmltools_0.5.7 rmarkdown_2.25
[53] compiler_4.3.2 getPass_0.2-4