Campus building summary
Maggie Douglas
2026-03-04
Last updated: 2026-03-04
Checks: 6 1
Knit directory: dickinson_power/
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Load data
library(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_factorlibrary(RColorBrewer)daily_full <- read.csv("./output/kwh_daily_2026-03-04.csv") %>%
mutate(date = as_date(date)) # convert back to date
joined_full <- read.csv("./output/kwh_annual_2026-03-04.csv")
# store conversion factors
dollars_kwh <- 0.08138507
co2_kg_kwh <- 0.30082405# generate summary by building category
joined_cat <- joined_full %>%
mutate(kwh_sqft = kwh_corr/sqft, # calculate kwh per sqft
kwh_person = kwh_corr/occupants,
dollars = kwh_corr*dollars_kwh,
ghg_kgCO2 = kwh_corr*co2_kg_kwh) %>%
group_by(type) %>%
summarize(n = n(),
kwh = sum(kwh_corr),
dollars = sum(dollars),
ghg_kgCO2 = sum(ghg_kgCO2),
sqft = sum(sqft, na.rm = T),
med_kwh_sqft = median(kwh_sqft, na.rm = T),
kwh_sqft_25 = quantile(kwh_sqft, .25, na.rm = T),
kwh_sqft_75 = quantile(kwh_sqft, .75, na.rm = T)) %>%
arrange(-kwh)Building type summary
Electricity use summary
joined_pretty_cat <- joined_cat %>%
mutate(n = ifelse(n == 1, "-", n),
kwh = round(kwh, digits = 0),
dollars = paste("$",round(dollars, digits = 0)),
ghg_MTCO2 = round(ghg_kgCO2/1000, digits = 0),
sqft = round(sqft, digits = 0),
med_kwh_sqft = round(med_kwh_sqft, digits = 1),
kwh_sqft_25 = round(kwh_sqft_25, digits = 1),
kwh_sqft_75 = round(kwh_sqft_75, digits = 1)) %>%
select(type, n, kwh, dollars, ghg_MTCO2, sqft, med_kwh_sqft, kwh_sqft_25, kwh_sqft_75) %>%
arrange(desc(med_kwh_sqft))datatable(joined_pretty_cat,
filter = 'top',
rownames = FALSE,
colnames = c("Building\ntype","Buildings\nwith data", "kWh", "Est. cost",
"CO2e\n(MT)", "Square\nfootage", "Median\nkWh\nper sqft",
"25th Perc.", "75th Perc."),
caption = "Table 1. Descriptive statistics for annual electricity use by building type. Annual estimates for each building/meter were generated by multiplying daily mean values by 365 days in the year. Those estimates were then summarized here.")Electricity use over the year
daily_graph <- daily_full %>%
mutate(date = as_date(date),
month = month(date, label = TRUE),
day = wday(date, label = TRUE),
type_brief = recode(type,
'Res Hall - U' = 'Res Hall',
'Res Hall - S' = 'Res Hall',
'Res Hall - M' = 'Res Hall',
'Res Hall - L' = 'Res Hall')) %>%
filter(!is.na(month))
ggplot(filter(daily_graph, meter != "Submeter"),
aes(x = month, y = kwh/10^6, fill = reorder(type_brief, kwh, FUN = sum))) +
geom_col(position = "stack") +
scale_fill_brewer(type = "qual", palette = "Paired") +
theme_bw() +
labs(x = "", y = "Electricity use (million kWh)", fill = "",
title = "Electricity use by building type")
Submeter data
ggplot(filter(daily_graph, meter == "Submeter"),
aes(x = month, y = kwh/10^6, fill = reorder(type_brief, kwh, FUN = sum))) +
geom_col(position = "stack") +
scale_fill_brewer(type = "qual", palette = "Paired") +
theme_bw() +
labs(x = "", y = "Electricity use (million kWh)", fill = "",
title = "Electricity data from the submeters")Warning: Removed 1876 rows containing missing values or values outside the scale range
(`geom_col()`).
ggplot(daily_graph, aes(x = month, y = kwh/10^6, fill = reorder(type, kwh, FUN = 'sum'))) +
geom_col(position = "stack") +
facet_wrap(. ~ type, scales = "free") +
theme_bw() +
labs(x = "", y = "Electricity use (million kWh)", fill = "") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
theme(legend.position = "none")
Electricity intensity by type
to_exclude <- filter(joined_full, days_perc < 90)
intensity <- joined_full %>%
filter(!(type %in% c("Res Hall - U","Production", "Non-building"))) %>%
mutate(kwh_sqft = kwh_corr/sqft,
meter_type = ifelse(type %in% c("Main Meter","Weis Meter"), "Agg.", "Individual"))
# median and IQR come from EIA (2022) table, annual kWh per square foot for Colleges/Universities
# https://www.eia.gov/consumption/commercial/data/2018/ce/pdf/c22.pdf
ggplot(intensity,
aes(x = reorder(type, kwh_sqft, FUN = "median"),
y = kwh_sqft, fill = type)) +
annotate("rect", xmin = -Inf, xmax = Inf, ymin = 7.4, ymax = 14.3, color = "lightgray", alpha = 0.3) +
geom_hline(yintercept = 10.3, linetype = "dashed", color = "white") +
facet_grid(meter_type ~ ., scales = "free_y", space = "free_y") +
geom_boxplot() +
coord_flip() +
theme_bw() +
theme(legend.position = "none") +
labs(x = "", y = "kWh per sqft per year",
title = "Electricity Intensity by Building Type")
Summary statistics
summary(joined_full) type meter NAME days_perc
Length:104 Length:104 Length:104 Min. : 36.71
Class :character Class :character Class :character 1st Qu.: 95.34
Mode :character Mode :character Mode :character Median :100.00
Mean : 95.72
3rd Qu.:100.00
Max. :100.00
kwh kwh_corr sqft occupants
Min. :-4274284 Min. :-4274284 Min. : 500 Min. : 1.50
1st Qu.: 8249 1st Qu.: 8249 1st Qu.: 1925 1st Qu.: 5.00
Median : 20783 Median : 21273 Median : 5912 Median : 9.50
Mean : 162525 Mean : 203347 Mean : 30890 Mean : 33.14
3rd Qu.: 88355 3rd Qu.: 88355 3rd Qu.: 24421 3rd Qu.: 40.88
Max. :11648808 Max. :11648808 Max. :1119435 Max. :161.50
NA's :14 NA's :46 ## Stats for campus buildings
stats <- filter(joined_full, meter != "Submeter" & type != "Production")
# million square feet
sum(stats$kwh)/10^6 # million kwh[1] 17.23632sum(stats$dollars)/10^6 # million $[1] 0sum(stats$ghg_kgCO2)/1000 # MT CO2e[1] 0## Stats for solar
solar <- filter(joined_full, type == "Production")
sum(solar$kwh)/10^6 # million kwh[1] -4.274285sum(solar$dollars)/10^6 # million $[1] 0sum(solar$ghg_kgCO2)/1000 # MT CO2e[1] 0
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] RColorBrewer_1.1-3 scales_1.3.0 DT_0.33 lubridate_1.9.3
[5] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
[9] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1
[13] 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 rprojroot_2.0.4
[13] workflowr_1.7.1 jsonlite_1.8.8 whisker_0.4.1 promises_1.2.1
[17] fansi_1.0.6 crosstalk_1.2.1 jquerylib_0.1.4 cli_3.6.2
[21] rlang_1.1.3 ellipsis_0.3.2 munsell_0.5.0 withr_3.0.0
[25] cachem_1.0.8 yaml_2.3.8 tools_4.3.2 tzdb_0.4.0
[29] colorspace_2.1-0 httpuv_1.6.13 vctrs_0.6.5 R6_2.5.1
[33] lifecycle_1.0.4 git2r_0.33.0 htmlwidgets_1.6.4 fs_1.6.3
[37] pkgconfig_2.0.3 pillar_1.9.0 bslib_0.6.1 later_1.3.2
[41] gtable_0.3.4 glue_1.7.0 Rcpp_1.1.0 highr_0.10
[45] xfun_0.41 tidyselect_1.2.0 rstudioapi_0.16.0 knitr_1.45
[49] farver_2.1.1 htmltools_0.5.7 labeling_0.4.3 rmarkdown_2.25
[53] compiler_4.3.2