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.

Version: 0.4.0
Depends: R (≥ 3.5.0)
Imports: stats, utils, mgcv, earth, keras3, Rcpp, tensorflow, data.table, wavelets, jsonlite, xml2, DBI, RSQLite
LinkingTo: Rcpp
Suggests: knitr, reticulate, rmarkdown, testthat (≥ 3.0.0)
Published: 2026-04-21
DOI: 10.32614/CRAN.package.elcf4R
Author: Frederic Bertrand ORCID iD [cre, aut], Fatima Fahs [aut], Myriam Maumy-Bertrand ORCID iD [aut]
Maintainer: Frederic Bertrand <frederic.bertrand at lecnam.net>
BugReports: https://github.com/fbertran/elcf4R/issues
License: GPL-3
URL: https://fbertran.github.io/elcf4R/, https://github.com/fbertran/elcf4R
NeedsCompilation: yes
Citation: elcf4R citation info
Materials: README, NEWS
CRAN checks: elcf4R results

Documentation:

Reference manual: elcf4R.html , elcf4R.pdf
Vignettes: Datasets and shipped artifacts (source, R code)
iFlex Workflow with Shipped Example Data (source, R code)

Downloads:

Package source: elcf4R_0.4.0.tar.gz
Windows binaries: r-devel: elcf4R_0.4.0.zip, r-release: elcf4R_0.4.0.zip, r-oldrel: elcf4R_0.4.0.zip
macOS binaries: r-release (arm64): elcf4R_0.4.0.tgz, r-oldrel (arm64): not available, r-release (x86_64): elcf4R_0.4.0.tgz, r-oldrel (x86_64): elcf4R_0.4.0.tgz

Linking:

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