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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.4.0 |
rolling linux/jammy R-4.5 | elcf4R_0.4.0.tar.gz |
813.5 KiB |
0.4.0 |
rolling linux/noble R-4.5 | elcf4R_0.4.0.tar.gz |
814.9 KiB |
0.4.0 |
rolling source/ R- | elcf4R_0.4.0.tar.gz |
363.4 KiB |
0.4.0 |
latest linux/jammy R-4.5 | elcf4R_0.4.0.tar.gz |
813.5 KiB |
0.4.0 |
latest linux/noble R-4.5 | elcf4R_0.4.0.tar.gz |
814.9 KiB |
0.4.0 |
latest source/ R- | elcf4R_0.4.0.tar.gz |
363.4 KiB |
0.4.0 |
2026-04-26 source/ R- | elcf4R_0.4.0.tar.gz |
363.4 KiB |
0.4.0 |
2026-04-23 source/ R- | elcf4R_0.4.0.tar.gz |
363.4 KiB |