elcf4R 0.4.0
- 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.