configurator.config_entsoe.ConfigEntsoe

configurator.config_entsoe.ConfigEntsoe(
    country_code='DE',
    periods=default_periods(),
    lags_consider=(lambda: list(range(1, 24)))(),
    train_size=(lambda: pd.Timedelta(days=(3 * 365)))(),
    end_train_default='2025-12-31 00:00+00:00',
    delta_val=(lambda: pd.Timedelta(hours=(24 * 7 * 10)))(),
    predict_size=24,
    cv_block_size=None,
    refit_size=7,
    random_state=314159,
    n_hyperparameters_trials=20,
    data_filename='interim/energy_load.csv',
    targets=None,
    use_outlier_detection=True,
    contamination=0.01,
    imputation_method='weighted',
    window_size=72,
    imputation_window_size=None,
    use_exogenous_features=True,
    latitude=51.5136,
    longitude=7.4653,
    timezone='UTC',
    state='NW',
    include_weather_windows=False,
    include_holiday_features=False,
    include_holiday_adjacency_features=False,
    use_population_weighted_weather=False,
    include_degree_hours=False,
    include_apparent_temperature=False,
    degree_hours_base_heating=15.0,
    degree_hours_base_cooling=22.0,
    include_ephemeris_features=False,
    include_day_type_features=False,
    include_school_holiday_features=False,
    poly_features_degree=1,
    max_poly_features=10,
    poly_mi_n_jobs=-1,
    poly_mi_sample_size=4000,
    include_covid_infection_rate=False,
    include_entsoe_forecast_load=False,
    include_entsoe_renewable_forecast=False,
    include_entsoe_net_load=False,
    include_entsoe_day_ahead_price=False,
    include_football_match_window=False,
    include_energy_saving_window=False,
    index_name='Time (UTC)',
    bounds=None,
    verbose=False,
    cache_home=None,
    n_trials_optuna=15,
    n_trials_spotoptim=10,
    n_initial_spotoptim=5,
    max_time_spotoptim=None,
    warm_start_lags=(lambda: list(DEFAULT_WARM_START_LAGS))(),
    task='lazy',
    agg_weights=None,
    forecaster_factory=None,
    data_loader=None,
    test_data_loader=None,
    auto_save_models=True,
    data_frame_name='default',
    number_folds=10,
    on_weather_failure='raise',
    on_exog_provider_failure='raise',
    exog_max_gap_hours=0,
    exog_max_tail_gap_hours=0,
    exog_provider_window='full',
    target_qc_range_mw=None,
    target_qc_step_mw=None,
    target_qc_window_days=None,
    target_corruption_policy='abort',
    target_max_heal_hours=0,
    target_anchor_zone_hours=168,
    target_qc_deviation_mw=None,
    target_qc_deviation_ref=None,
    target_qc_deviation_slots=2,
    retrain_max_age=(lambda: pd.Timedelta(days=7))(),
)

Configuration for the ENTSO-E forecasting pipeline.

Single-target counterpart to ConfigMulti, used by the ENTSO-E CLI (spotforecast2.tasks.task_entsoe) and any single-target pipeline routed through spotforecast2.multitask.runner.run(config_cls=ConfigEntsoe).

ConfigEntsoe inherits every field and method of ConfigMulti — so any feature flag added to ConfigMulti is available here automatically (this is what closes the historical feature-flag parity gap structurally, rather than via a hand-maintained mirror). It differs from ConfigMulti in exactly two ways:

• index_name defaults to "Time (UTC)" (the ENTSO-E CSV time column) instead of "DateTime".
• it adds retrain_max_age — the maximum age of a previously trained model before retraining is required (consumed by spotforecast2_safe.manager.trainer.should_retrain).

See ConfigMulti for the full field reference (training/validation windows, feature toggles, exogenous-provider flags, target-corruption knobs, …).

Parameters

Name	Type	Description	Default
index_name	str	Datetime column name used when resetting the index. Defaults to "Time (UTC)".	'Time (UTC)'
retrain_max_age	pd.Timedelta	Maximum age of a trained model before a retrain is forced. Defaults to pd.Timedelta(days=7).	(lambda: pd.Timedelta(days=7))()

Examples

import pandas as pd
from spotforecast2_safe.configurator.config_entsoe import ConfigEntsoe
from spotforecast2_safe.configurator.config_multi import ConfigMulti

config = ConfigEntsoe(country_code="DE")
# ENTSO-E-specific defaults:
print("index_name:", config.index_name)
print("retrain_max_age:", config.retrain_max_age)
assert config.index_name == "Time (UTC)"
assert config.retrain_max_age == pd.Timedelta(days=7)

# Inherits the full ConfigMulti surface, incl. the opt-in feature flags:
assert isinstance(config, ConfigMulti)
config = ConfigEntsoe(
    include_ephemeris_features=True,
    include_day_type_features=True,
    include_degree_hours=True,
)
print("ephemeris:", config.include_ephemeris_features)
print("predict_size:", config.predict_size)

index_name: Time (UTC)
retrain_max_age: 7 days 00:00:00
ephemeris: True
predict_size: 24