# Datasets and shipped artifacts

#### elcf4R package

# 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:

Dataset | Reader | Resolution | Temperature in normalized panel | Shipped example | Shipped benchmark
--- | --- | --- | --- | --- | ---
iFlex | `elcf4r_read_iflex()` | hourly | yes | `elcf4r_iflex_example` | `elcf4r_iflex_benchmark_results`
StoreNet (`H6_W`) | `elcf4r_read_storenet()` | 1 minute | optional, source-dependent | `elcf4r_storenet_example` | `elcf4r_storenet_benchmark_results`
Low Carbon London | `elcf4r_read_lcl()` | 30 minutes | no | `elcf4r_lcl_example` | `elcf4r_lcl_benchmark_results`
REFIT | `elcf4r_read_refit()` | user-selected resample | no | `elcf4r_refit_example` | `elcf4r_refit_benchmark_results`
ELMAS | not part of the common household reader set | hourly | no | `elcf4r_elmas_toy` | none

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.

Dataset | Helper surface | Level | Current scaffold scope | Shipped example | Shipped benchmark | Licence note
--- | --- | --- | --- | --- | --- | ---
IDEAL | `elcf4r_download_ideal()`, `elcf4r_read_ideal()` | household | aggregate-electricity hourly summaries from `auxiliarydata.zip` | no | no | the current Edinburgh DataShare record states `CC BY 4.0`
GX | `elcf4r_download_gx()`, `elcf4r_read_gx()` | transformer/community | SQLite or flat-export normalization to the common panel schema | no | no | treat licence terms as dataset-record specific and recheck before redistribution

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