Last updated: 2026-03-02

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Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/.DS_Store
    Ignored:    data/FY25 Main Meter Data.xlsx
    Ignored:    data/building_list_FY25_updated.xlsx
    Ignored:    data/graph_data_life_exp.csv
    Ignored:    keys/.DS_Store
    Ignored:    output/building_check.csv
    Ignored:    output/building_check.xlsx
    Ignored:    output/kwh_annual.csv
    Ignored:    output/kwh_annual_20260225.csv
    Ignored:    output/kwh_annual_20260226.csv
    Ignored:    output/kwh_daily.csv
    Ignored:    output/kwh_daily_20260225.csv
    Ignored:    output/kwh_daily_20260226.csv
    Ignored:    output/kwh_main_annual.csv
    Ignored:    output/kwh_main_daily.csv

Unstaged changes:
    Modified:   analysis/_site.yml
    Modified:   analysis/data_wrangling_final.Rmd
    Modified:   analysis/main_meter_processing.Rmd
    Modified:   keys/occupancy_key.csv

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File Version Author Date Message
Rmd e752845 maggiedouglas 2026-02-26 adjust data processing + building summary
Rmd 182fffe maggiedouglas 2026-02-16 add temp to main meter building data
html 182fffe maggiedouglas 2026-02-16 add temp to main meter building data
Rmd 1e2d797 maggiedouglas 2026-02-15 Processed main meter data
html 1e2d797 maggiedouglas 2026-02-15 Processed main meter data

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))

# load temp data
temp <- read.csv("./keys/temp_by_date.csv") %>%
  mutate(date = mdy(date))

# attaching all dataframes together 
## (reads in two kWh columns b/c slightly diff names)
main_bldgs <- bind_rows(data, .id="Sheet") %>%
  rename(building = Sheet,
         date = Date, 
         kWh1 = 3,
         kWh2 = 4) %>%
  mutate(kwh = ifelse(is.na(kWh1), kWh2, kWh1), # reconcile kWh to condense to one column
         date = mdy(date)) %>%
  filter(date < ymd("2025-07-01"),  # filter to fiscal year 25
         date > ymd("2024-06-30")) %>%
  select(-kWh1, -kWh2) %>% # remove extra kWh columns
  mutate(month = month(date, label = TRUE), 
         day = wday(date, label = TRUE)) %>% # generate month and day
  select(building, month, day, date, kwh) %>%
  left_join(temp, by = "date") # add back in temperature + DD

Check

str(main_bldgs)
tibble [8,395 × 8] (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 ...
 $ ave_temp: int [1:8395] 69 73 78 81 84 86 84 84 86 87 ...
 $ cdd_65  : num [1:8395] 6 9.6 13.5 16.2 20.1 22.2 19 19.7 22.4 22.6 ...
 $ hdd_65  : num [1:8395] 0 1.3 0 0 0 0 0 0 0 0 ...

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 = round(sum(kwh, na.rm = T), digits = 0),
            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 percentage of the total is captured here?
(indiv_tot / main_tot)*100
[1] 38.98098

Summary graphs

ggplot(main_bldgs, aes(x = month, y = kwh, fill = building)) +
  facet_wrap(. ~ building, scales = "free") +
  geom_col(position = "stack") +
  theme_bw() +
  theme(legend.position = "none") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
  labs(title = "Buildings with Submeters on the Main Meter",
       x = "",
       y = "Electricity use (kWh)")

Version Author Date
1e2d797 maggiedouglas 2026-02-15

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, ave_temp),
          filter = 'top', rownames = FALSE, colnames = c("Building","Date","kWh", "Temp (F)"))

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 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] 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_1.3.1     tibble_3.2.1    ggplot2_3.5.1   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_0.6.37     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    whisker_0.4.1    
[17] promises_1.2.1    fansi_1.0.6       crosstalk_1.2.1   scales_1.3.0     
[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   pillar_1.9.0     
[41] bslib_0.6.1       later_1.3.2       gtable_0.3.4      glue_1.7.0       
[45] Rcpp_1.1.0        highr_0.10        xfun_0.41         tidyselect_1.2.0 
[49] rstudioapi_0.16.0 knitr_1.45        farver_2.1.1      htmltools_0.5.7  
[53] labeling_0.4.3    rmarkdown_2.25    compiler_4.3.2