Main Meter data processing
Prof. D
2026-02-16
Last updated: 2026-02-15
Checks: 6 1
Knit directory: dickinson_power/
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Ignored: data/FY25 Main Meter Data.xlsx
Ignored: data/building_list_FY25_updated.xlsx
Ignored: data/graph_data_life_exp.csv
Ignored: data/~$building_list_FY25.xlsx
Ignored: data/~$building_list_FY25_updated.xlsx
Ignored: output/annual_kwh.csv
Ignored: output/building_check.csv
Ignored: output/building_check.xlsx
Ignored: output/daily_kwh.csv
Ignored: output/kwh_annual.csv
Ignored: output/kwh_daily.csv
Ignored: output/kwh_main_annual.csv
Ignored: output/kwh_main_daily.csv
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Purpose
This code loads and processes the data for individual buildings on the main meter, combines them into a single dataset, and summarizes the completeness of the data.
Load
library(tidyverse)
library(readxl)
library(DT)# load names of tabs
sheet <- excel_sheets("./data/FY25 Main Meter Data.xlsx")
# applying sheet names to dataframe names
data <- lapply(setNames(sheet, sheet),
function(x)
read_excel("./data/FY25 Main Meter Data.xlsx", sheet=x))
# attaching all dataframes together
main_bldgs <- bind_rows(data, .id="Sheet") %>%
rename(building = Sheet,
date = Date,
kWh1 = 3,
kWh2 = 4) %>%
mutate(kwh = ifelse(is.na(kWh1), kWh2, kWh1),
date = mdy(date)) %>%
filter(date < ymd("2025-07-01"),
date > ymd("2024-06-30")) %>%
select(-kWh1, -kWh2) %>%
mutate(month = month(date, label = TRUE),
day = wday(date, label = TRUE)) %>%
select(building, month, day, date, kwh)Check
str(main_bldgs)tibble [8,395 × 5] (S3: tbl_df/tbl/data.frame)
$ building: chr [1:8395] "Althouse" "Althouse" "Althouse" "Althouse" ...
$ month : Ord.factor w/ 12 levels "Jan"<"Feb"<"Mar"<..: 7 7 7 7 7 7 7 7 7 7 ...
$ day : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tue"<..: 2 3 4 5 6 7 1 2 3 4 ...
$ date : Date[1:8395], format: "2024-07-01" "2024-07-02" ...
$ kwh : num [1:8395] 339 346 351 344 336 212 223 362 370 384 ...Summarize
# store main meter total kwh from PPL data
main_tot <- 11648808
annual_non_na <- main_bldgs %>%
filter(!is.na(kwh)) %>%
group_by(building) %>%
summarize(non_na = n())
annual <- main_bldgs %>%
group_by(building) %>%
summarize(kwh = sum(kwh, na.rm = T),
days = n()) %>%
left_join(annual_non_na, by = "building") %>%
mutate(perc_days = round((non_na/days)*100, digits = 0))
# which are the missing days?
annual_missing <- main_bldgs %>%
filter(is.na(kwh)) %>%
group_by(date) %>%
summarize(n = n())
# store total for all submetered buildings
indiv_tot <- sum(annual$kwh)
# What proportion of the total is captured here?
indiv_tot / main_tot[1] 0.3898098Summary graphs
ggplot(main_bldgs, aes(x = month, y = kwh, fill = building)) +
geom_col(position = "stack") +
theme_bw() +
labs(title = "Buildings with Submeters on the Main Meter",
x = "",
y = "Electricity use (kWh)")
# store buildings with over 90% days of data
comp_bldg <- filter(annual, perc_days > 90)
ggplot(filter(main_bldgs, building %in% comp_bldg$building),
aes(x = month, y = kwh, fill = building)) +
geom_col(position = "stack") +
theme_bw() +
labs(title = "Buildings with Submeters on the Main Meter\n
(those with electricity data for >90% of days)",
x = "",
y = "Electricity use (kWh)")
Tables
Note: I’m guessing CHW corresponds to
central heating (?) This would be the part of Kaufman Hall that is
associated with producing energy for the rest of campus.
Annual summary
datatable(annual, filter = 'top', rownames=FALSE,
colnames = c("Building","Total kWh","# Days", "Days with data", "Days with data (%)"))Daily data
datatable(select(main_bldgs, building, date, kwh),
filter = 'top', rownames = FALSE, colnames = c("Building","Date","kWh"))Export
write.csv(annual, "./output/kwh_main_annual.csv", row.names = F)
write.csv(main_bldgs, "./output/kwh_main_daily.csv", row.names = F)
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.7.2
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] DT_0.33 readxl_1.4.3 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_1.3.0 tibble_3.2.1 ggplot2_3.5.0 tidyverse_2.0.0
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_0.6.34 magrittr_2.0.3 timechange_0.3.0
[9] evaluate_0.23 grid_4.3.2 fastmap_1.1.1 cellranger_1.1.0
[13] rprojroot_2.0.4 workflowr_1.7.1 jsonlite_1.8.8 promises_1.2.1
[17] fansi_1.0.6 crosstalk_1.2.1 scales_1.3.0 jquerylib_0.1.4
[21] cli_3.6.2 rlang_1.1.3 ellipsis_0.3.2 munsell_0.5.0
[25] withr_3.0.0 cachem_1.0.8 yaml_2.3.8 tools_4.3.2
[29] tzdb_0.4.0 colorspace_2.1-0 httpuv_1.6.13 vctrs_0.6.5
[33] R6_2.5.1 lifecycle_1.0.4 git2r_0.33.0 htmlwidgets_1.6.4
[37] fs_1.6.3 pkgconfig_2.0.3 pillar_1.9.0 bslib_0.6.1
[41] later_1.3.2 gtable_0.3.4 glue_1.7.0 Rcpp_1.0.12
[45] highr_0.10 xfun_0.41 tidyselect_1.2.0 rstudioapi_0.15.0
[49] knitr_1.45 farver_2.1.1 htmltools_0.5.7 labeling_0.4.3
[53] rmarkdown_2.25 compiler_4.3.2