NEWS
elcf4R 0.4.0 (2026-04-21)
- Added scaffolded download/read support for IDEAL through
elcf4r_download_ideal() and elcf4r_read_ideal(), focused on extracted
hourly aggregate-electricity summaries from auxiliarydata.zip.
- Added scaffolded download/read support for GX through
elcf4r_download_gx() and elcf4r_read_gx(), with support for either the
official SQLite database or flat exports normalized into the common panel
schema.
- Added offline tests for IDEAL and GX download-resolution helpers and
normalization readers, including GX SQLite-table detection.
- Updated package docs and the dataset vignette to describe IDEAL as an
unshipped household-level scaffold under the current
CC BY 4.0 source
record, and GX as an unshipped secondary transformer/community-level
scaffold with explicit licence re-verification guidance before
redistribution.
- Removed implicit
RETICULATE_PYTHON mutation from the LSTM backend probe
and added explicit, user-driven Python selection through
elcf4r_use_tensorflow_env().
- Tightened CRAN-facing package metadata and examples, including a shorter
elcf4r_benchmark() help example that runs on a toy single-entity panel.
elcf4R 0.3.0
- Replaced the previous KWF baseline with a wavelet-based implementation using
wavelets, deterministic calendar groups, kernel weighting and
approximation/detail mean correction.
- Replaced the unused
src/kwf_core.cpp placeholder with compiled KWF helper
routines for distances, kernel weights, group restriction and mean-corrected
forecasts, and wired the R KWF path to those accelerators.
- Added a first-class clustered KWF workflow with thermosensitivity
classification, wavelet-feature clustering helpers, cluster assignment, and
a dedicated
elcf4r_fit_kwf_clustered() model path.
- Generalized dataset ingestion around a common normalized panel schema and
added dataset adapters for iFlex, StoreNet, Low Carbon London and REFIT.
- Implemented
elcf4r_download_storenet() with figshare API resolution for
known household article IDs and an archive fallback for broader StoreNet
retrieval.
- Added a generic rolling-origin benchmark API through
elcf4r_build_benchmark_index() and elcf4r_benchmark(), with saved
predictions, backend metadata and support for gam, mars, kwf,
kwf_clustered and lstm.
- Completed benchmark metric coverage so shipped benchmark artifacts now carry
populated NMAE, NRMSE, sMAPE and MASE values for all shipped result rows.
- Added shipped example panels and saved benchmark-result datasets for
StoreNet, Low Carbon London and REFIT, complementing the existing iFlex
example and benchmark artifacts.
- Expanded the shipped benchmark cohorts to stronger rolling windows: iFlex now
uses 15 households with 28 train days and 7 test days; the shipped LCL and
REFIT benchmark cohorts are now filtered to thermosensitive seasonal windows
so
kwf_clustered rows are benchmarked rather than skipped.
- Reworked dataset-facing documentation to describe the supported reader
surface, shipped artifacts and reproducible
data-raw/ rebuild scripts.
- Clarified the dataset roadmap around IDEAL and GX: IDEAL is a future
candidate dataset with a currently verified CC BY 4.0 source record, while
GX is treated as a secondary transformer-level benchmark candidate that
requires explicit licence re-verification before any shipped subset is added.
elcf4R 0.2.0
- Added an iFlex preprocessing pipeline with normalized panel readers,
daily-segment builders, compact shipped example data, and saved benchmark
result datasets.
- Added package documentation and vignettes for the shipped iFlex workflows
and benchmark outputs, and documented the bundled
elcf4r_elmas_toy
dataset.
- Replaced the placeholder KWF/LSTM paths with working model wrappers,
unified
predict.elcf4r_model(), and migrated the LSTM implementation to
keras3 with automatic detection of the r-tensorflow virtualenv.
- Cleaned up package metadata, namespace declarations, tests, and examples so
package checks now pass apart from environment-specific CRAN notes.
elcf4R 0.1.0
- Package creation and initial release containing estimators,
autoplot helpers, and reliability utilities.