preprocessing.exog_providers.EntsoeRenewableForecastProvider

preprocessing.exog_providers.EntsoeRenewableForecastProvider(
    data_home=None,
    max_gap=0,
    max_tail_gap=0,
    provider_window=None,
)

ENTSO-E day-ahead wind and solar generation forecast.

Reads interim/renewable_forecast.csv via spotforecast2_safe.data.fetch_data.load_renewable_forecast and emits two columns: entsoe_wind_forecast (sum of all wind columns) and entsoe_solar_forecast (sum of all solar columns). Day-ahead forecasts are leakage-clean; the realised generation must never be used.

Parameters

Name	Type	Description	Default
data_home	`DataHome`	Root data directory forwarded to the loader.	`None`
max_gap	int	Maximum contiguous missing-value run healed by `_align_to_index`. See `_align_to_index` for full semantics. Defaults to `0`.	`0`
max_tail_gap	int	Extended healing budget for the trailing-edge NaN run. See `_align_to_index`. Defaults to `0`.	`0`
provider_window	Optional[pd.DatetimeIndex]	Validation index passed to `_align_to_index` as validate_index. See `_align_to_index`. Defaults to `None`.	`None`

Examples

import os
import shutil
import tempfile

import pandas as pd

from spotforecast2_safe.preprocessing.exog_providers import (
    EntsoeRenewableForecastProvider,
)

tmp = tempfile.mkdtemp()
os.environ["SPOTFORECAST2_DATA"] = tmp
os.makedirs(os.path.join(tmp, "interim"), exist_ok=True)
idx = pd.date_range("2023-06-01", periods=24, freq="h", tz="UTC")
pd.DataFrame(
    {"Solar": 3.0, "Wind Onshore": 5.0}, index=idx
).rename_axis("Time (UTC)").to_csv(
    os.path.join(tmp, "interim", "renewable_forecast.csv")
)

provider = EntsoeRenewableForecastProvider()
out = provider.build(idx)
print(out.columns.tolist(), out.shape, out.dtypes.iloc[0].name)
assert set(out.columns) == {"entsoe_wind_forecast", "entsoe_solar_forecast"}
assert out.shape == (24, 2)

shutil.rmtree(tmp)
del os.environ["SPOTFORECAST2_DATA"]

['entsoe_wind_forecast', 'entsoe_solar_forecast'] (24, 2) float32

Methods

Name	Description
build	Return wind and solar forecast columns aligned to index.

build

preprocessing.exog_providers.EntsoeRenewableForecastProvider.build(index)

Return wind and solar forecast columns aligned to index.

Parameters

Name	Type	Description	Default
index	pd.DatetimeIndex	Hourly `DatetimeIndex` (tz-aware UTC) for the forecast window.	required

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: Two columns — `entsoe_wind_forecast` and `entsoe_solar_forecast` — as `float32`.

Raises

Name	Type	Description
	`ExogProviderError`	If `interim/renewable_forecast.csv` is missing or contains no wind or solar columns.

Examples

import os
import shutil
import tempfile

import pandas as pd

from spotforecast2_safe.preprocessing.exog_providers import (
    EntsoeRenewableForecastProvider,
)

tmp = tempfile.mkdtemp()
os.environ["SPOTFORECAST2_DATA"] = tmp
os.makedirs(os.path.join(tmp, "interim"), exist_ok=True)
idx = pd.date_range("2023-06-01", periods=12, freq="h", tz="UTC")
pd.DataFrame(
    {"Solar": 2.0, "Wind Onshore": 4.0}, index=idx
).rename_axis("Time (UTC)").to_csv(
    os.path.join(tmp, "interim", "renewable_forecast.csv")
)

out = EntsoeRenewableForecastProvider().build(idx)
print(out.columns.tolist(), out.shape)
assert not out.isna().any().any()

shutil.rmtree(tmp)
del os.environ["SPOTFORECAST2_DATA"]

['entsoe_wind_forecast', 'entsoe_solar_forecast'] (12, 2)