Package index • elcf4R

All functions

elcf4R elcf4R-package: 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_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_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_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