Package: elcf4R 0.4.1

elcf4R: Electricity Load Curves Forecasting at Individual Level

Implements forecasting methods for individual electricity load curves, including Kernel Wavelet Functional (KWF), clustered KWF, Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), and Long Short-Term Memory (LSTM) models. Provides normalized dataset adapters for iFlex, StoreNet, Low Carbon London, and REFIT; download and read support for IDEAL and GX; explicit Python backend selection for TensorFlow-based LSTM fits; helpers for daily segmentation and rolling-origin benchmarking; and compact shipped example panels and benchmark-result datasets.

Authors:Frederic Bertrand [cre, aut], Fatima Fahs [aut], Myriam Maumy-Bertrand [aut]

elcf4R_0.4.1.tar.gz
elcf4R_0.4.1.zip(r-4.7)elcf4R_0.4.1.zip(r-4.6)elcf4R_0.4.1.zip(r-4.5)
elcf4R_0.4.1.tgz(r-4.6-x86_64)elcf4R_0.4.1.tgz(r-4.6-arm64)elcf4R_0.4.1.tgz(r-4.5-x86_64)elcf4R_0.4.1.tgz(r-4.5-arm64)
elcf4R_0.4.1.tar.gz(r-4.7-arm64)elcf4R_0.4.1.tar.gz(r-4.7-x86_64)elcf4R_0.4.1.tar.gz(r-4.6-arm64)elcf4R_0.4.1.tar.gz(r-4.6-x86_64)
elcf4R_0.4.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
elcf4R/json (API)

# Install 'elcf4R' in R:
install.packages('elcf4R', repos = c('https://fbertran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/fbertran/elcf4r/issues

Pkgdown/docs site:https://fbertran.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

4.70 score 1 stars 7 scripts 539 downloads 25 exports 53 dependencies

Last updated from:fe13164be4. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK199
linux-devel-x86_64OK221
source / vignettesOK220
linux-release-arm64OK199
linux-release-x86_64OK223
macos-release-arm64OK114
macos-release-x86_64OK421
macos-oldrel-arm64OK108
macos-oldrel-x86_64OK443
windows-develOK219
windows-releaseOK233
windows-oldrelOK276
wasm-releaseOK116

Exports:elcf4r_assign_kwf_clusterselcf4r_benchmarkelcf4r_build_benchmark_indexelcf4r_build_daily_segmentselcf4r_calendar_groupselcf4r_classify_thermosensitivityelcf4r_download_elmaselcf4r_download_gxelcf4r_download_idealelcf4r_download_storenetelcf4r_fit_gamelcf4r_fit_kwfelcf4r_fit_kwf_clusteredelcf4r_fit_lstmelcf4r_fit_marselcf4r_kwf_cluster_dayselcf4r_metricselcf4r_normalize_panelelcf4r_read_gxelcf4r_read_idealelcf4r_read_iflexelcf4r_read_lclelcf4r_read_refitelcf4r_read_storenetelcf4r_use_tensorflow_env

Dependencies:backportsbase64encbitbit64blobcachemcliconfigcpp11data.tableDBIdottyearthfastmapFormulagenericsglueherejsonlitekeras3latticelifecyclemagrittrMatrixmemoisemgcvnlmepkgconfigplotmoplotrixpngprocessxpsR6rappdirsRcppRcppTOMLreticulaterlangrprojrootRSQLiterstudioapitensorflowtfautographtfrunstidyselectvctrswaveletswhiskerwithrxml2yamlzeallot

iFlex Workflow with Shipped Example Data
Overview | Inspect the shipped iFlex example | Build daily segments | Fit forecasting models on the example panel | Run a small rolling benchmark | Inspect shipped benchmark results
Rendered fromelcf4R-iflex-workflow.Rmdusingknitr::rmarkdown

Last update: 2026-04-09
Started: 2026-03-30

Datasets and shipped artifacts
Overview | Supported dataset matrix | Scaffolded, unshipped datasets | Shipped example panels | Shipped benchmark result datasets | Rebuilding the shipped artifacts | Example: daily segments from a shipped panel | Example: rerun a tiny benchmark locally
Rendered fromelcf4R-datasets-vignette.Rmdusingknitr::rmarkdown

Last update: 2026-04-07
Started: 2026-03-28

Readme and manuals

Help Manual

Help pageTopics
Forecasting Individual Electricity Load Curveselcf4R-package elcf4R
Assign segments to a fitted KWF clustering modelelcf4r_assign_kwf_clusters
Run a rolling-origin benchmark on a normalized panelelcf4r_benchmark
Build a day-level benchmark index from a normalized panelelcf4r_build_benchmark_index
Build daily load-curve segments from a normalized panelelcf4r_build_daily_segments
Derive deterministic KWF calendar groupselcf4r_calendar_groups
Classify thermosensitivity from daily load dataelcf4r_classify_thermosensitivity
Download the ELMAS dataset from figshareelcf4r_download_elmas
Download selected GX dataset componentselcf4r_download_gx
Download selected IDEAL dataset componentselcf4r_download_ideal
Download one or more StoreNet household files from figshareelcf4r_download_storenet
Toy subset of ELMAS hourly cluster profileselcf4r_elmas_toy
Fit a GAM model for load curveselcf4r_fit_gam
Fit a Kernel Wavelet Functional model for daily load curveselcf4r_fit_kwf
Fit a clustered KWF model for daily load curveselcf4r_fit_kwf_clustered
Fit an LSTM model for daily load curveselcf4r_fit_lstm
Fit a MARS model for load curveselcf4r_fit_mars
iFlex benchmark index of complete participant-dayselcf4r_iflex_benchmark_index
iFlex benchmark results for shipped forecasting methodselcf4r_iflex_benchmark_results
iFlex example panel for package exampleselcf4r_iflex_example
Cluster daily segments for clustered KWFelcf4r_kwf_cluster_days
Low Carbon London benchmark results for shipped forecasting methodselcf4r_lcl_benchmark_results
Low Carbon London example panel for package exampleselcf4r_lcl_example
Forecast accuracy metrics for load curveselcf4r_metrics
Normalize a load panel to the elcf4R schemaelcf4r_normalize_panel
Read and normalize the GX residential transformer-level scaffoldelcf4r_read_gx
Read and normalize the IDEAL hourly aggregate-electricity scaffoldelcf4r_read_ideal
Read and normalize the iFlex hourly datasetelcf4r_read_iflex
Read and normalize the Low Carbon London datasetelcf4r_read_lcl
Read and normalize the REFIT cleaned household datasetelcf4r_read_refit
Read and normalize the StoreNet household datasetelcf4r_read_storenet
REFIT benchmark results for shipped forecasting methodselcf4r_refit_benchmark_results
REFIT example panel for package exampleselcf4r_refit_example
StoreNet benchmark results for shipped forecasting methodselcf4r_storenet_benchmark_results
StoreNet example panel for package exampleselcf4r_storenet_example
Select the Python environment used for TensorFlow-backed LSTM fitselcf4r_use_tensorflow_env
Assign new segments to a fitted KWF clustering modelpredict.elcf4r_kwf_clusters
Predict from an 'elcf4r_model'predict.elcf4r_model