# Electricity Load Curves Forecasting at Individual Level

## Documentation for package ‘elcf4R’ version 0.4.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

elcf4R-package | Forecasting Individual Electricity Load Curves
elcf4R | Forecasting Individual Electricity Load Curves
elcf4r_assign_kwf_clusters | Assign segments to a fitted KWF clustering model
elcf4r_benchmark | Run a rolling-origin benchmark on a normalized panel
elcf4r_build_benchmark_index | Build a day-level benchmark index from a normalized panel
elcf4r_build_daily_segments | Build daily load-curve segments from a normalized panel
elcf4r_calendar_groups | Derive deterministic KWF calendar groups
elcf4r_classify_thermosensitivity | Classify thermosensitivity from daily load data
elcf4r_download_elmas | Download the ELMAS dataset from figshare
elcf4r_download_gx | Download selected GX dataset components
elcf4r_download_ideal | Download selected IDEAL dataset components
elcf4r_download_sgsc | Download the Smart-Grid Smart-City interval-reading archive
elcf4r_download_storenet | Download one or more StoreNet household files from figshare
elcf4r_elmas_toy | Toy subset of ELMAS hourly cluster profiles
elcf4r_fit_gam | Fit a GAM model for load curves
elcf4r_fit_kwf | Fit a Kernel Wavelet Functional model for daily load curves
elcf4r_fit_kwf_clustered | Fit a clustered KWF model for daily load curves
elcf4r_fit_lstm | Fit an LSTM model for daily load curves
elcf4r_fit_mars | Fit a MARS model for load curves
elcf4r_iflex_benchmark_index | iFlex benchmark index of complete participant-days
elcf4r_iflex_benchmark_results | iFlex benchmark results for shipped forecasting methods
elcf4r_iflex_example | iFlex example panel for package examples
elcf4r_kwf_cluster_days | Cluster daily segments for clustered KWF
elcf4r_lcl_benchmark_results | Low Carbon London benchmark results for shipped forecasting methods
elcf4r_lcl_example | Low Carbon London example panel for package examples
elcf4r_metrics | Forecast accuracy metrics for load curves
elcf4r_normalize_panel | Normalize a load panel to the elcf4R schema
elcf4r_read_gx | Read and normalize the GX residential transformer-level scaffold
elcf4r_read_ideal | Read and normalize the IDEAL hourly aggregate-electricity scaffold
elcf4r_read_iflex | Read and normalize the iFlex hourly dataset
elcf4r_read_lcl | Read and normalize the Low Carbon London dataset
elcf4r_read_refit | Read and normalize the REFIT cleaned household dataset
elcf4r_read_sgsc | Read and normalize the Smart-Grid Smart-City interval-reading dataset
elcf4r_read_storenet | Read and normalize the StoreNet household dataset
elcf4r_refit_benchmark_results | REFIT benchmark results for shipped forecasting methods
elcf4r_refit_example | REFIT example panel for package examples
elcf4r_sgsc_example | Compact Smart-Grid Smart-City example panel
elcf4r_storenet_benchmark_results | StoreNet benchmark results for shipped forecasting methods
elcf4r_storenet_example | StoreNet example panel for package examples
elcf4r_use_tensorflow_env | Select the Python environment used for TensorFlow-backed LSTM fits
predict.elcf4r_kwf_clusters | Assign new segments to a fitted KWF clustering model
predict.elcf4r_model | Predict from an 'elcf4r_model'