cluster_sessions: Cluster sessions with 'mclust' package

In mcanigueral/evprof: Electric Vehicle Charging Sessions Profiling and Modelling

Cluster sessions with mclust package

Description

Cluster sessions with mclust package

Usage

cluster_sessions(
  sessions,
  k,
  seed,
  mclust_tol = 1e-08,
  mclust_itmax = 10000,
  log = FALSE,
  start = getOption("evprof.start.hour")
)

Arguments

sessions	tibble, sessions data set in evprof standard format
k	number of clusters
seed	random seed
mclust_tol	tolerance parameter for clustering
mclust_itmax	maximum number of iterations
log	logical, whether to transform ConnectionStartDateTime and ConnectionHours variables to natural logarithmic scale (base = exp(1)).
start	integer, start hour in the x axis of the plot.

Value

list with two attributes: sessions and models

Examples

library(dplyr)

# Select working day sessions (`Timecycle == 1`) that
# disconnect the same day (`Disconnection == 1`)
sessions_day <- california_ev_sessions %>%
  divide_by_timecycle(
    months_cycles = list(1:12), # Not differentiation between months
    wdays_cycles = list(1:5, 6:7) # Differentiation between workdays/weekends
  ) %>%
  divide_by_disconnection(
    division_hour = 10, start = 3
  ) %>%
  filter(
    Disconnection == 1, Timecycle == 1
  ) %>%
  sample_frac(0.05)
plot_points(sessions_day, start = 3)

# Identify two clusters
sessions_clusters <- cluster_sessions(
  sessions_day, k=2, seed = 1234, log = TRUE
)

# The column `Cluster` has been added
names(sessions_clusters$sessions)
plot_points(sessions_clusters$sessions) +
  ggplot2::aes(color = Cluster)

mcanigueral/evprof documentation built on June 2, 2025, 12:43 p.m.