downloader.entsoe.extract_entsoe_hourly_forecast

downloader.entsoe.extract_entsoe_hourly_forecast(
    interim,
    *,
    column='Forecasted Load',
)

Resample a sub-hourly ENTSO-E interim frame to a clean hourly series.

Takes a pd.DataFrame with a DatetimeIndex (typically read from an ENTSO-E interim CSV that contains 15-minute data) and returns the named column resampled to hourly means, with NaN observations dropped.

Parameters

Name	Type	Description	Default
interim	pd.DataFrame	Source `pd.DataFrame`. The index must be a `pd.DatetimeIndex`; any frequency is accepted.	required
column	str	Name of the column to extract and resample. Defaults to `"Forecasted Load"`.	`'Forecasted Load'`

Returns

Name	Type	Description
	pd.Series	A `pd.Series` with a regular hourly `DatetimeIndex` and
	pd.Series	`name == column`. Only hours with at least one non-`NaN`
	pd.Series	sub-hourly observation are present (`dropna` is applied after
	pd.Series	`resample("h").mean()`).

Raises

Name	Type	Description
	TypeError	If interim is not a `pd.DataFrame`.
	TypeError	If `interim.index` is not a `pd.DatetimeIndex`.
	KeyError	If column is not found in `interim.columns`.
	ValueError	If the resampled series is empty (all observations are `NaN` or the column had no data).

Examples

import pandas as pd
from spotforecast2_safe.downloader.entsoe import extract_entsoe_hourly_forecast

# Build a 15-minute DataFrame for two hours.
idx = pd.date_range("2026-06-14 00:00", periods=8, freq="15min", tz="UTC")
df = pd.DataFrame({"Forecasted Load": [100.0, 110.0, 90.0, 120.0,
                                       80.0, 95.0, 105.0, 115.0]},
                  index=idx)
out = extract_entsoe_hourly_forecast(df)
print(out)
# 2026-06-14 00:00:00+00:00    105.0
# 2026-06-14 01:00:00+00:00     98.75
# Name: Forecasted Load, dtype: float64

2026-06-14 00:00:00+00:00    105.00
2026-06-14 01:00:00+00:00     98.75
Freq: h, Name: Forecasted Load, dtype: float64