Package: evprof 1.2.0

Marc Cañigueral

evprof: Electric Vehicle Charging Sessions Profiling and Modelling

Tools for modelling electric vehicle charging sessions into generic groups with similar connection patterns called "user profiles", using Gaussian Mixture Models clustering. The clustering and profiling methodology is described in Cañigueral and Meléndez (2021, ISBN:0142-0615) <doi:10.1016/j.ijepes.2021.107195>.

Authors:Marc Cañigueral [aut, cre, cph]

evprof_1.2.0.tar.gz
evprof_1.2.0.zip(r-4.7)evprof_1.2.0.zip(r-4.6)evprof_1.2.0.zip(r-4.5)
evprof_1.2.0.tgz(r-4.6-any)evprof_1.2.0.tgz(r-4.5-any)
evprof_1.2.0.tar.gz(r-4.7-any)evprof_1.2.0.tar.gz(r-4.6-any)
evprof_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
evprof/json (API)

# Install 'evprof' in R:
install.packages('evprof', repos = c('https://resourcefully-dev.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/resourcefully-dev/evprof/issues

Pkgdown/docs site:https://resourcefully-dev.github.io

On CRAN:

Conda:

3.88 score 3 stars 7 scripts 542 downloads 32 exports 70 dependencies

Last updated from:c8ed1e5fe8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK158
source / vignettesOK165
linux-release-x86_64OK218
macos-release-arm64OK127
macos-oldrel-arm64OK142
windows-develOK150
windows-releaseOK160
windows-oldrelOK145
wasm-releaseOK111

Exports:choose_k_GMMcluster_sessionscut_sessionsdefine_clustersdetect_outliersdivide_by_disconnectiondivide_by_timecycledrop_outliersget_charging_rates_distributionget_connection_modelsget_daily_avg_n_sessionsget_daily_n_sessionsget_dbscan_paramsget_energy_modelsget_ev_modelplot_bivarGMMplot_connection_modelsplot_density_2Dplot_density_3Dplot_division_linesplot_energy_modelsplot_histogramplot_histogram_gridplot_kNNdistplot_outliersplot_pointsread_ev_modelround_to_intervalsave_clustering_iterationssave_ev_modelset_profilessummarise_sessions

Dependencies:askpassbase64encbslibcachemclicowplotcpp11crosstalkcurldata.tabledbscandigestdplyrevaluatefarverfastmapfontawesomefsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttrisobandjquerylibjsonliteknitrlabelinglaterlazyevallifecyclelubridatemagrittrMASSmclustmemoisemimeopensslotelpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownS7sassscalesstringistringrsystibbletidyrtidyselecttimechangetinytexutf8vctrsviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Visualize BIC indicator to choose the number of clusterschoose_k_GMM
Cluster sessions with 'mclust' packagecluster_sessions
Cut outliers based on minimum and maximum limits of ConnectionHours and ConnectionStartDateTime variablescut_sessions
Define each cluster with a user profile interpretationdefine_clusters
Detect outliersdetect_outliers
Divide sessions by disconnection daydivide_by_disconnection
Divide sessions by time-cycledivide_by_timecycle
Drop outliersdrop_outliers
Get charging rates distribution in percentagesget_charging_rates_distribution
Get a tibble of connection GMM for every user profileget_connection_models
Get the daily average number of sessions given a range of years, months and weekdaysget_daily_avg_n_sessions
Get daily number of sessions given a range of years, months and weekdaysget_daily_n_sessions
Get the minPts and eps values for DBSCAN to label only a specific percentage as noiseget_dbscan_params
Get a tibble of energy GMM for every user profileget_energy_models
Get the EV model object of class 'evmodel'get_ev_model
Plot Bivariate Gaussian Mixture Modelsplot_bivarGMM
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_connection_models
Density plot in 2D, considering Start time and Connection duration as variablesplot_density_2D
Density plot in 3D, considering Start time and Connection duration as variablesplot_density_3D
Iteration over evprof::plot_division_line function to plot multiple linesplot_division_lines
Compare density of estimated energy with density of real energy vectorplot_energy_models
Histogram of a variable from sessions data setplot_histogram
Grid of multiple variable histogramsplot_histogram_grid
Plot kNNdistplot_kNNdist
Plot outlying sessionsplot_outliers
Scatter plot of sessionsplot_points
Read an EV model JSON file and convert it to object of class 'evmodel'read_ev_model
Round to nearest intervalround_to_interval
Save iteration plots in PDF filesave_clustering_iterations
Save the EV model object of class 'evmodel' to a JSON filesave_ev_model
Classify sessions into user profilesset_profiles
Statistic summary of sessions featuressummarise_sessions