Package index • evprof

Data exploratory analysis

Plots

plot_points(): Scatter plot of sessions

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_histogram(): Histogram of a variable from sessions data set

plot_histogram_grid(): Grid of multiple variable histograms

Sessions data set analysis

summarise_sessions(): Statistic summary of sessions features

get_charging_rates_distribution(): Get charging rates distribution in percentages

get_daily_n_sessions(): Get daily number of sessions given a range of years, months and weekdays

get_daily_avg_n_sessions(): Get the daily average number of sessions given a range of years, months and weekdays

round_to_interval(): Round to nearest interval

Preprocessing

DBSCAN outliers cleaning

cut_sessions(): Cut outliers based on minimum and maximum limits of ConnectionHours and ConnectionStartDateTime variables

plot_kNNdist(): Plot kNNdist

get_dbscan_params(): Get the minPts and eps values for DBSCAN to label only a specific percentage as noise

plot_outliers(): Plot outlying sessions

detect_outliers(): Detect outliers

drop_outliers(): Drop outliers

Sessions division

plot_division_lines(): Iteration over evprof::plot_division_line function to plot multiple lines

divide_by_disconnection(): Divide sessions by disconnection day

divide_by_timecycle(): Divide sessions by time-cycle

Clustering

choose_k_GMM(): Visualize BIC indicator to choose the number of clusters

cluster_sessions(): Cluster sessions with mclust package

save_clustering_iterations(): Save iteration plots in PDF file

plot_bivarGMM(): Plot Bivariate Gaussian Mixture Models

Profiling

define_clusters(): Define each cluster with a user profile interpretation

set_profiles(): Classify sessions into user profiles

Modeling

Connection Models

get_connection_models(): Get a tibble of connection GMM for every user profile

plot_connection_models(): 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.

Energy Models

get_energy_models(): Get a tibble of energy GMM for every user profile

plot_energy_models(): Compare density of estimated energy with density of real energy vector

EV model object

get_ev_model(): Get the EV model object of class evmodel

save_ev_model(): Save the EV model object of class evmodel to a JSON file

read_ev_model(): Read an EV model JSON file and convert it to object of class evmodel