Overview

elcf4R now supports four household-oriented public data sources through a common normalized panel schema:

• elcf4r_read_iflex()
• elcf4r_read_storenet()
• elcf4r_read_lcl()
• elcf4r_read_refit()

The package also ships compact example panels and saved benchmark results so the main vignettes run without external downloads. Raw source files stay in data-raw/ and are not redistributed through the package unless a compact derived artifact has been explicitly built and saved.

Two additional unshipped scaffolds are also available:

• elcf4r_download_ideal() / elcf4r_read_ideal() for aggregate-electricity hourly summaries from IDEAL.
• elcf4r_download_gx() / elcf4r_read_gx() for the GX transformer/community-level dataset.

Supported dataset matrix

The current dataset surface is:

All four household readers return the same core columns:

• dataset
• entity_id
• timestamp
• date
• time_index
• y
• temp
• dow
• month
• resolution_minutes

Dataset-specific metadata columns are preserved when available.

Scaffolded, unshipped datasets

IDEAL and GX are intentionally documented separately from the core shipped household matrix.

Notes:

• IDEAL support in this release is limited to aggregate electricity and does not attempt to parse the raw 1 Hz stream.
• GX is not an individual-household dataset. It is useful as a secondary benchmark source for weather and community-level demand behavior, but it is not folded into the package’s core household benchmark claims.

Shipped example panels

The shipped examples are small normalized panels intended for package examples and vignette code.

example_sizes <- Filter(
  Negate(is.null),
  list(
    .elcf4r_example_size_row("elcf4r_iflex_example", "iflex"),
    .elcf4r_example_size_row("elcf4r_storenet_example", "storenet"),
    .elcf4r_example_size_row("elcf4r_lcl_example", "lcl"),
    .elcf4r_example_size_row("elcf4r_refit_example", "refit")
  )
)

if (length(example_sizes) == 0L) {
  data.frame()
} else {
  do.call(rbind, example_sizes)
}
#> data frame with 0 columns and 0 rows

These objects can be passed directly to:

• elcf4r_build_daily_segments()
• elcf4r_build_benchmark_index()
• elcf4r_benchmark()

Shipped benchmark result datasets

Each supported household dataset now has a saved benchmark-result object built from a fixed local cohort and a deterministic rolling-origin design.

benchmark_summary <- Filter(
  Negate(is.null),
  list(
    .elcf4r_benchmark_summary("elcf4r_iflex_benchmark_results", "iflex"),
    .elcf4r_benchmark_summary("elcf4r_storenet_benchmark_results", "storenet"),
    .elcf4r_benchmark_summary("elcf4r_lcl_benchmark_results", "lcl"),
    .elcf4r_benchmark_summary("elcf4r_refit_benchmark_results", "refit")
  )
)

if (length(benchmark_summary) == 0L) {
  data.frame()
} else {
  benchmark_summary <- do.call(rbind, benchmark_summary)
  benchmark_summary[, c("dataset", "method", "nmae", "nrmse", "smape", "mase")]
}
#> data frame with 0 columns and 0 rows

These shipped benchmark tables are poster-style artifacts. They are not intended to replace full local benchmarking on the raw datasets.

Rebuilding the shipped artifacts

Each shipped dataset is reproducible from a data-raw/ script:

• data-raw/elcf4r_iflex_subsets.R
• data-raw/elcf4r_iflex_benchmark_results.R
• data-raw/elcf4r_storenet_artifacts.R
• data-raw/elcf4r_lcl_artifacts.R
• data-raw/elcf4r_refit_artifacts.R

The general pattern is:

1. Place the original raw files in data-raw/.
2. Read them through elcf4r_read_*().
3. Build a normalized day index with elcf4r_build_benchmark_index().
4. Save a compact example panel.
5. Run elcf4r_benchmark() on a fixed cohort and save the result table.

This keeps the package lightweight while making the shipped examples and benchmark summaries reproducible.

Example: daily segments from a shipped panel

if (exists("elcf4r_iflex_example", inherits = FALSE)) {
  iflex_segments <- elcf4r_build_daily_segments(
    elcf4r_iflex_example,
    carry_cols = c("dataset", "participation_phase", "price_signal")
  )

  dim(iflex_segments$segments)
  head(iflex_segments$covariates[, c("entity_id", "date", "temp_mean", "price_signal")])
} else {
  data.frame()
}
#> data frame with 0 columns and 0 rows

Example: rerun a tiny benchmark locally

if (exists("elcf4r_lcl_example", inherits = FALSE)) {
  tiny_index <- elcf4r_build_benchmark_index(
    elcf4r_lcl_example,
    carry_cols = "dataset"
  )

  tiny_benchmark <- elcf4r_benchmark(
    panel = elcf4r_lcl_example,
    benchmark_index = tiny_index,
    methods = c("gam", "kwf"),
    cohort_size = 1,
    train_days = 10,
    test_days = 2,
    include_predictions = FALSE
  )

  tiny_benchmark$results[, c("entity_id", "method", "test_date", "nmae", "mase")]
} else {
  data.frame()
}
#> data frame with 0 columns and 0 rows