# Electric Vehicle Charging Sessions Profiling and Modelling

## Documentation for package ‘evprof’ version 1.1.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

choose_k_GMM | Visualize BIC indicator to choose the number of clusters
cluster_sessions | Cluster sessions with 'mclust' package
cut_sessions | Cut outliers based on minimum and maximum limits of ConnectionHours and ConnectionStartDateTime variables
define_clusters | Define each cluster with a user profile interpretation
detect_outliers | Detect outliers
divide_by_disconnection | Divide sessions by disconnection day
divide_by_timecycle | Divide sessions by time-cycle
drop_outliers | Drop outliers
get_charging_rates_distribution | Get charging rates distribution in percentages
get_connection_models | Get a tibble of connection GMM for every user profile
get_daily_avg_n_sessions | Get the daily average number of sessions given a range of years, months and weekdays
get_daily_n_sessions | Get daily number of sessions given a range of years, months and weekdays
get_dbscan_params | Get the minPts and eps values for DBSCAN to label only a specific percentage as noise
get_energy_models | Get a tibble of energy GMM for every user profile
get_ev_model | Get the EV model object of class 'evmodel'
plot_bivarGMM | Plot Bivariate Gaussian Mixture Models
plot_density_2D | Density plot in 2D, considering Start time and Connection duration as variables
plot_density_3D | Density plot in 3D, considering Start time and Connection duration as variables
plot_division_lines | Iteration over evprof::plot_division_line function to plot multiple lines
plot_energy_models | Compare density of estimated energy with density of real energy vector
plot_histogram | Histogram of a variable from sessions data set
plot_histogram_grid | Grid of multiple variable histograms
plot_kNNdist | Plot kNNdist
plot_model_clusters | Plot all bi-variable GMM (clusters) with the colors corresponding to the assigned user profile. This shows which clusters correspond to which user profile, and the proportion of every user profile.
plot_outliers | Plot outlying sessions
plot_points | Scatter plot of sessions
read_ev_model | Read an EV model JSON file and convert it to object of class 'evmodel'
round_to_interval | Round to nearest interval
save_clustering_iterations | Save iteration plots in PDF file
save_ev_model | Save the EV model object of class 'evmodel' to a JSON file
set_profiles | Classify sessions into user profiles
summarise_sessions | Statistic summary of sessions features