Fit an LSTM model for daily load curves — elcf4r_fit

The LSTM implementation uses one or more previous daily curves to predict the next daily curve. When use_temperature = TRUE and temp_mean is available in covariates, the daily mean temperature is added as a second input feature repeated across the within-day time steps.

Usage

elcf4r_fit_lstm(
  segments,
  covariates = NULL,
  use_temperature = FALSE,
  lookback_days = 1L,
  units = 16L,
  epochs = 10L,
  batch_size = 8L,
  validation_split = 0,
  seed = NULL,
  verbose = 0L
)

Arguments

segments: Matrix or data frame of past daily load curves (rows are days, columns are time points).
covariates: Optional data frame with one row per training day.
use_temperature: Logical. If TRUE, use temp_mean from covariates as an additional input feature when available.
lookback_days: Number of past daily curves used as one training input.
units: Number of hidden units in the LSTM layer.
epochs: Number of training epochs.
batch_size: Batch size used in keras3::fit().
validation_split: Validation split passed to keras3::fit().
seed: Optional integer seed passed to TensorFlow. When NULL, the current backend RNG state is used.
verbose: Verbosity level passed to keras3::fit() and predict().

Value

An object of class elcf4r_model with method = "lstm".

Examples

if (interactive() &&
    requireNamespace("reticulate", quietly = TRUE) &&
    reticulate::virtualenv_exists("r-tensorflow")) {
  elcf4r_use_tensorflow_env(virtualenv = "r-tensorflow")
  if (isTRUE(getFromNamespace(".elcf4r_lstm_backend_available", "elcf4R")())) {
    id1 <- subset(
      elcf4r_iflex_example,
      entity_id == unique(elcf4r_iflex_example$entity_id)[1]
    )
    daily <- elcf4r_build_daily_segments(id1)
    fit <- elcf4r_fit_lstm(
      segments = daily$segments[1:10, ],
      covariates = daily$covariates[1:10, ],
      use_temperature = TRUE,
      epochs = 1,
      units = 4,
      batch_size = 2,
      verbose = 0
    )
    length(predict(fit))
  }
}