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elcf4R  

Electricity Load Curves Forecasting at Individual Level
View on CRAN: Click here


Download and install elcf4R package within the R console
Install from CRAN:
install.packages("elcf4R")

Install from Github:
library("remotes")
install_github("cran/elcf4R")

Install by package version:
library("remotes")
install_version("elcf4R", "0.4.0")



Attach the package and use:
library("elcf4R")
Maintained by
Frederic Bertrand
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-04-21
Latest Update: 2026-04-21
Description:
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.
How to cite:
Frederic Bertrand (2026). elcf4R: Electricity Load Curves Forecasting at Individual Level. R package version 0.4.0, https://cran.r-project.org/web/packages/elcf4R. Accessed 26 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:35), 0.4.0 (2026-04-21 22:52)
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Complete documentation for elcf4R
Functions, R codes and Examples using the elcf4R R package
Full elcf4R package functions and examples
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