Package: evprof 1.1.2

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_1.1.2.tar.gz
evprof_1.1.2.zip(r-4.5)evprof_1.1.2.zip(r-4.4)evprof_1.1.2.zip(r-4.3)
evprof_1.1.2.tgz(r-4.4-any)evprof_1.1.2.tgz(r-4.3-any)
evprof_1.1.2.tar.gz(r-4.5-noble)evprof_1.1.2.tar.gz(r-4.4-noble)
evprof_1.1.2.tgz(r-4.4-emscripten)evprof_1.1.2.tgz(r-4.3-emscripten)
evprof.pdf |evprof.html✨
evprof/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/mcanigueral/evprof/issues

Datasets:

    On CRAN:

    3.78 score 2 stars 6 scripts 204 downloads 32 exports 75 dependencies

    Last updated 8 months agofrom:aef11e5112. Checks:OK: 5 NOTE: 2. Indexed: yes.

    TargetResultDate
    Doc / VignettesOKNov 23 2024
    R-4.5-winOKNov 23 2024
    R-4.5-linuxOKNov 23 2024
    R-4.4-winNOTENov 23 2024
    R-4.4-macNOTENov 23 2024
    R-4.3-winOKNov 23 2024
    R-4.3-macOKNov 23 2024

    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_density_2Dplot_density_3Dplot_division_linesplot_energy_modelsplot_histogramplot_histogram_gridplot_kNNdistplot_model_clustersplot_outliersplot_pointsread_ev_modelround_to_intervalsave_clustering_iterationssave_ev_modelset_profilessummarise_sessions

    Dependencies:askpassbase64encbslibcachemclicolorspacecowplotcpp11crosstalkcurldata.tabledbscandigestdplyrevaluatefansifarverfastmapfontawesomefsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttrisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelubridatemagrittrMASSMatrixmclustmemoisemgcvmimemunsellnlmeopensslpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownsassscalesstringistringrsystibbletidyrtidyselecttimechangetinytexutf8vctrsviridisLitewithrxfunyaml

    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
    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 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_model_clusters
    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