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elcf4R

Electricity Load Curves Forecasting at Individual Level

v0.4.2 · May 26, 2026 · GPL-3

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, Smart-Grid Smart-City, 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.

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r-devel-linux-x86_64-debian-clang OK
r-devel-linux-x86_64-debian-gcc OK
r-devel-linux-x86_64-fedora-clang OK
r-devel-linux-x86_64-fedora-gcc OK
r-devel-windows-x86_64 OK
r-oldrel-macos-arm64 OK
r-oldrel-macos-x86_64 OK
r-oldrel-windows-x86_64 OK
r-patched-linux-x86_64 OK
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Check History

OK 10 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Apr 25, 2026
ERROR 7 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Apr 22, 2026
ERROR r-devel-macos-arm64

whether package can be installed

Installation failed.
See ‘/Volumes/Builds/packages/sonoma-arm64/results/4.6/elcf4R.Rcheck/00install.out’ for details.

Dependency Network

Dependencies Reverse dependencies mgcv earth keras3 Rcpp tensorflow data.table wavelets jsonlite xml2 DBI RSQLite elcf4R

Version History

2 tracked
updated 0.4.2 ← 0.4.0 diff May 26, 2026
new 0.4.0 Apr 21, 2026