Repository: oemof/demandlib

Issue:
Add new standard load profiles from 2025
New load profiles exist here:
https://www.bdew.de/energie/standardlastprofile-strom/

H25 - Haushalt
G25 - Gewerbe allgemein
L25 - Landwirtschaftsbetriebe
P25 - PV
S25 - Speicher + PV

Discussion:
@p-snft: Should we use them for years after 2025 and keep the previous for years up to 2024?@maurerle: It is up to the grid operators:

> Jedem Netzbetreiber steht es dabei selbstverständlich frei, bei der Bilanzierung auf die aktualisierten Profile aus dem Jahr 2025, die alten Profile aus dem Jahr 1999, eigene Profile oder eine Mischung der verschiedenen Optionen zurückzugreifen. [^1]

So I would just add them as an option here: https://demandlib.readthedocs.io/en/latest/bdew.html#electrical-profiles with the names "H25" and so on, which would not interfer with the existing modeling options..?
So one can use them alongside together:

```python
from demandlib import bdew
e_slp = bdew.ElecSlp(year=2020)

# get the "h0" and "g0" profile
profiles = e_slp.get_profiles("h0", "h25")
```

[^1]: https://www.bdew.de/energie/standardlastprofile-strom/@p-snft: Makes sense. I didn't realise the new profiles have distinct names.

As the new profiles break with the option to have custom seasons, I think it would be a good idea to have distinct classes for all different profiles. So, you would

```python
from oemof.demandlib.bdew import H0, H25

profile_h0 = H0(year=2025)
profile_h25 = H25(year=2025)
```

This way, we could also fix the fact that h0_dyn does not work with changed seasons. (Also: I believe that "h0" should be the dynamic profile.)

Solution patch:
diff --git a/examples/bdew_slp25.py b/examples/bdew_slp25.py
new file mode 100644
index 0000000..2b7e3db
--- /dev/null
+++ b/examples/bdew_slp25.py
@@ -0,0 +1,50 @@
+"""
+SPDX-FileCopyrightText: Deutsches Zentrum für Luft- und Raumfahrt
+SPDX-FileCopyrightText: Patrik Schönfeldt
+
+
+SPDX-License-Identifier: MIT
+"""
+
+import datetime
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+from demandlib import bdew
+
+index15m = pd.date_range(
+    start="2020-01-01 00:00",
+    end="2020-01-31 23:45",
+    freq="15min",
+)
+index1m = pd.date_range(
+    start="2020-01-01 00:00",
+    end="2020-01-31 23:45",
+    freq="1min",
+)
+index3h = pd.date_range(
+    start="2020-01-01 00:00",
+    end="2020-01-31 23:45",
+    freq="3h",
+)
+
+holidays = [
+    datetime.date(2020, 1, 1),
+    datetime.date(2020, 1, 2),
+    datetime.date(2020, 1, 3),
+    datetime.date(2020, 1, 4),
+    datetime.date(2020, 1, 5),
+]
+
+h25_holidays = bdew.H25(index1m, holidays)
+h25_std = bdew.H25(index15m)
+h25_3h = bdew.H25(index3h)
+
+plt.step(h25_holidays.index, h25_holidays, label="Holidays", where="post")
+plt.step(h25_std.index, h25_std, label="Standard", where="post")
+plt.step(h25_3h.index, h25_3h, label="Three hours", where="post")
+plt.legend()
+
+plt.show()
diff --git a/src/demandlib/__init__.py b/src/demandlib/__init__.py
index 995b9fb..55479a4 100644
--- a/src/demandlib/__init__.py
+++ b/src/demandlib/__init__.py
@@ -1 +1,9 @@
 __version__ = "0.2.2a1"
+
+from . import bdew
+from . import vdi
+
+__all__ = [
+    "bdew",
+    "vdi",
+]
diff --git a/src/demandlib/bdew/__init__.py b/src/demandlib/bdew/__init__.py
index b8c114a..ae70645 100644
--- a/src/demandlib/bdew/__init__.py
+++ b/src/demandlib/bdew/__init__.py
@@ -7,5 +7,20 @@ SPDX-FileCopyrightText: Patrik Schönfeldt
 SPDX-License-Identifier: MIT
 """
 
-from .elec_slp import ElecSlp  # noqa: F401
-from .heat_building import HeatBuilding  # noqa: F401
+from ._profiles25 import G25
+from ._profiles25 import H25
+from ._profiles25 import L25
+from ._profiles25 import P25
+from ._profiles25 import S25
+from .elec_slp import ElecSlp
+from .heat_building import HeatBuilding
+
+__all__ = [
+    "ElecSlp",
+    "HeatBuilding",
+    "G25",
+    "H25",
+    "L25",
+    "P25",
+    "S25",
+]
diff --git a/src/demandlib/bdew/_profiles25.py b/src/demandlib/bdew/_profiles25.py
new file mode 100644
index 0000000..ebaa867
--- /dev/null
+++ b/src/demandlib/bdew/_profiles25.py
@@ -0,0 +1,250 @@
+# -*- coding: utf-8 -*-
+"""
+Implementation of the 2025 BDEW standard load profiles:
+* G25 (static), industrial profile based on 2022 and 2023 data
+* H25 (dynamic), household profile based on 2018 and 2019 data
+* L25 (static), farm profile based on previous L0 profile
+* P25 (dynamic), profile for households with PV based on 2022 and 2023 data
+* S25 (dynamic), profile for households with BES based on 2022 and 2023 data
+
+SPDX-FileCopyrightText: Deutsches Zentrum für Luft- und Raumfahrt
+SPDX-FileCopyrightText: Patrik Schönfeldt
+
+SPDX-License-Identifier: MIT
+"""
+
+import os
+
+import numpy as np
+import pandas as pd
+
+from demandlib.tools import set_holidays_in_df
+
+_bdew_datapath = os.path.join(os.path.dirname(__file__), "bdew_data")
+
+
+class BDEW25Profile(pd.Series):
+    """
+    Electrical standard load profiles based on the BDEW method (2025 revision)
+
+    Parameters
+    ----------
+    timeindex : pd.DatetimeIndex
+        Time index to produce the SLP for. Only constant step-size is
+        supported, i.e. timeindex.freq needs to be defined. If time
+        steps of 15 minutes are used, that are aligned with the full
+        hour, the original SLP is reproduced. Lower resolutions will
+        be downsampled, higher resolutions wil be padded. (Pading is
+        the correct handling because the SLP defines constant values
+        for 15 minutes.)
+
+    Optional Parameters
+    -------------------
+    holidays : dictionary or list
+        The keys of the dictionary or the items of the list should be datetime
+        objects of the days that are holidays.
+    """
+
+    def __init__(
+        self,
+        timeindex: pd.DatetimeIndex,
+        holidays: dict | list | None = None,
+    ):
+        original_timedelta = pd.Timedelta(timeindex.freq)
+        if original_timedelta == pd.Timedelta("00:15:00"):
+            timeindex15m = timeindex
+        else:
+            timeindex15m = pd.date_range(
+                start=timeindex[0],
+                end=timeindex[-1],
+                freq="15min",
+            )
+        new_df = pd.DataFrame(
+            data={
+                "month": timeindex15m.month,
+                "weekday": timeindex15m.day_of_week + 1,
+                "hour": timeindex15m.hour,
+                "minute": timeindex15m.minute,
+            },
+            index=timeindex15m,
+        )
+
+        set_holidays_in_df(new_df, holidays=holidays)
+
+        new_df.replace({"weekday": [1, 2, 3, 4, 5]}, "WT", inplace=True)
+        new_df.replace({"weekday": [6]}, "SA", inplace=True)
+        new_df.replace(
+            {"weekday": [0, 7]}, "FT", inplace=True
+        )  # 0 for holiday
+
+        new_df = new_df.merge(
+            self.raw_profile_data,
+            on=["month", "weekday", "hour", "minute"],
+            how="inner",
+        )
+        new_df.set_index(timeindex15m, inplace=True)
+
+        values = new_df.value
+        if original_timedelta > pd.Timedelta("00:15:00"):
+            values = values.resample(original_timedelta).mean()
+        elif original_timedelta < pd.Timedelta("00:15:00"):
+            values = values.reindex(timeindex, method="ffill")
+
+        super().__init__(data=values, index=timeindex)
+
+    @property
+    def datafile(self):
+        raise NotImplementedError(
+            "BDEW25Profile needs to be implemented,"
+            " please use one of its children instead."
+        )
+
+    @property
+    def raw_profile_data(self):
+        profile_data = pd.read_csv(
+            _bdew_datapath + self.datafile,
+            header=[0, 1],
+        )
+
+        profile_data.rename(
+            columns={
+                "Januar": 1,
+                "Februar": 2,
+                "März": 3,
+                "April": 4,
+                "Mai": 5,
+                "Juni": 6,
+                "Juli": 7,
+                "August": 8,
+                "September": 9,
+                "Oktober": 10,
+                "November": 11,
+                "Dezember": 12,
+            },
+            inplace=True,
+        )
+        del profile_data[("Unnamed: 0_level_0", "[kWh]")]
+
+        hours = [h for h in range(24) for _ in range(4)]
+        minutes = [m for _ in range(24) for m in range(0, 60, 15)]
+        serialised_data = []
+        for column in profile_data.columns:
+            serialised_data.append(
+                pd.DataFrame(
+                    data={
+                        "month": column[0],
+                        "weekday": column[1],
+                        "hour": hours,
+                        "minute": minutes,
+                        "value": 4 * profile_data[column],
+                    }
+                )
+            )
+
+        profile_data = pd.concat(serialised_data, ignore_index=True)
+        return profile_data
+
+
+class DynamicBDEW25Profile(BDEW25Profile):
+    """
+    BDEW25 SLP considering the dynamisation_function.
+    See BDEW25Profile for more details.
+    """
+
+    def __init__(
+        self, timeindex: pd.DatetimeIndex, holidays: dict | list | None = None
+    ):
+        super().__init__(timeindex=timeindex, holidays=holidays)
+        self.update(self * self.dynamisation_function(timeindex))
+
+    @staticmethod
+    def dynamisation_function(timeindex: pd.DatetimeIndex) -> pd.Series:
+
+        # cast to float hear because of miscalculations when using integers
+        day_of_year = np.array(timeindex.day_of_year, dtype=float)
+
+        return pd.Series(
+            -3.92 * 10**-10 * day_of_year**4
+            + 3.2 * 10**-7 * day_of_year**3
+            - 7.02 * 10**-5 * day_of_year**2
+            + 0.0021 * day_of_year
+            + 1.24,
+            index=timeindex,
+        )
+
+
+class G25(BDEW25Profile):
+    """
+    SLP for industries, see BDEW25Profile for more details.
+    """
+
+    def __init__(
+        self, timeindex: pd.DatetimeIndex, holidays: dict | list | None = None
+    ):
+        super().__init__(timeindex=timeindex, holidays=holidays)
+
+    @property
+    def datafile(self):
+        return "/g25.csv"
+
+
+class H25(DynamicBDEW25Profile):
+    """
+    SLP for households, see DynamicBDEW25Profile for more details.
+    """
+
+    def __init__(
+        self, timeindex: pd.DatetimeIndex, holidays: dict | list | None = None
+    ):
+        super().__init__(timeindex=timeindex, holidays=holidays)
+
+    @property
+    def datafile(self):
+        return "/h25.csv"
+
+
+class L25(BDEW25Profile):
+    """
+    SLP for farms, see BDEW25Profile for more details.
+    """
+
+    def __init__(
+        self, timeindex: pd.DatetimeIndex, holidays: dict | list | None = None
+    ):
+        super().__init__(timeindex=timeindex, holidays=holidays)
+
+    @property
+    def datafile(self):
+        return "/l25.csv"
+
+
+class P25(DynamicBDEW25Profile):
+    """
+    SLP for housholds with generic PV system,
+    see DynamicBDEW25Profile for more details.
+    """
+
+    def __init__(
+        self, timeindex: pd.DatetimeIndex, holidays: dict | list | None = None
+    ):
+        super().__init__(timeindex=timeindex, holidays=holidays)
+
+    @property
+    def datafile(self):
+        return "/p25.csv"
+
+
+class S25(DynamicBDEW25Profile):
+    """
+    SLP for housholds with generic PV system and battery storage,
+    see DynamicBDEW25Profile for more details.
+    """
+
+    def __init__(
+        self, timeindex: pd.DatetimeIndex, holidays: dict | list | None = None
+    ):
+        super().__init__(timeindex=timeindex, holidays=holidays)
+
+    @property
+    def datafile(self):
+        return "/s25.csv"
diff --git a/src/demandlib/bdew/bdew_data/g25.csv b/src/demandlib/bdew/bdew_data/g25.csv
new file mode 100644
index 0000000..785200e
--- /dev/null
+++ b/src/demandlib/bdew/bdew_data/g25.csv
@@ -0,0 +1,98 @@
+,Januar,Januar,Januar,Februar,Februar,Februar,März,März,März,April,April,April,Mai,Mai,Mai,Juni,Juni,Juni,Juli,Juli,Juli,August,August,August,September,September,September,Oktober,Oktober,Oktober,November,November,November,Dezember,Dezember,Dezember
+[kWh],SA,FT,WT,SA,FT,WT,SA,FT,WT,SA,FT,WT,SA,FT,WT,SA,FT,WT,SA,FT,WT,SA,FT,WT,SA,FT,WT,SA,FT,WT,SA,FT,WT,SA,FT,WT
+00:00-00:15,15.045,14.658,14.832,14.762,14.795,14.967,14.746,14.351,14.645,13.754,13.616,13.948,13.226,13.122,13.160,13.708,13.369,13.818,13.829,13.485,13.346,13.746,13.421,13.154,13.324,12.899,13.044,13.562,13.174,13.129,14.693,14.118,14.236,15.623,14.905,15.472
+00:15-00:30,14.908,14.602,14.721,14.586,14.794,14.810,14.561,14.235,14.524,13.604,13.558,13.793,13.178,12.968,13.015,13.500,13.075,13.602,13.606,13.199,13.169,13.689,13.156,13.039,13.133,12.798,12.863,13.390,13.096,12.969,14.562,13.994,14.135,15.499,14.685,15.337
+00:30-00:45,14.785,14.430,14.618,14.464,14.687,14.709,14.562,14.108,14.431,13.581,13.322,13.684,13.189,12.782,12.892,13.290,13.078,13.485,13.375,12.984,13.006,13.432,13.055,12.949,12.911,12.569,12.718,13.252,12.880,12.830,14.492,13.783,14.002,15.441,14.674,15.256
+00:45-01:00,14.739,14.244,14.501,14.480,14.418,14.603,14.371,13.994,14.364,13.547,13.282,13.570,13.114,12.695,12.749,13.244,12.855,13.368,13.275,12.901,12.919,13.301,12.902,12.832,12.856,12.444,12.570,13.167,12.757,12.718,14.395,13.785,13.897,15.322,14.606,15.136
+01:00-01:15,14.674,14.226,14.448,14.412,14.379,14.608,14.423,13.922,14.257,13.438,13.283,13.505,12.971,12.712,12.673,13.262,12.860,13.232,13.234,12.757,12.846,13.215,12.756,12.774,12.722,12.311,12.497,13.139,12.721,12.638,14.269,13.739,13.834,15.174,14.477,15.053
+01:15-01:30,14.591,14.113,14.371,14.261,14.241,14.546,14.326,13.945,14.174,13.378,13.205,13.421,12.895,12.587,12.639,13.104,12.683,13.204,13.196,12.662,12.765,13.142,12.727,12.763,12.532,12.336,12.483,13.107,12.606,12.594,14.135,13.601,13.916,15.070,14.390,14.921
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+01:45-02:00,14.421,14.139,14.328,14.270,14.264,14.481,14.203,13.724,14.079,13.164,13.116,13.343,12.812,12.462,12.530,12.961,12.647,13.134,13.026,12.645,12.682,12.974,12.673,12.663,12.374,12.163,12.432,12.884,12.411,12.475,13.879,13.455,13.707,15.036,14.229,14.909
+02:00-02:15,14.328,14.069,14.306,14.291,14.078,14.538,14.135,13.798,14.090,13.152,13.099,13.333,12.644,12.376,12.500,12.971,12.620,13.085,13.032,12.499,12.662,12.884,12.586,12.599,12.430,12.065,12.408,12.764,12.411,12.485,13.789,13.449,13.663,14.946,14.263,14.883
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+04:15-04:30,14.806,13.942,15.276,14.490,14.166,15.342,14.382,13.865,15.060,13.607,13.124,14.340,12.902,12.515,13.450,12.917,12.507,13.751,13.147,12.574,13.306,13.117,12.604,13.427,12.878,12.206,13.498,13.153,12.516,13.632,14.484,13.763,14.855,15.483,14.288,15.927
+04:30-04:45,15.082,13.890,15.786,14.788,14.131,15.865,14.627,13.823,15.664,14.020,13.164,15.103,13.253,12.491,14.104,13.214,12.517,14.239,13.375,12.472,13.843,13.414,12.692,13.955,13.171,12.112,14.096,13.724,12.455,14.247,14.656,13.758,15.360,15.788,14.264,16.370
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diff --git a/src/demandlib/bdew/bdew_data/h25.csv b/src/demandlib/bdew/bdew_data/h25.csv
new file mode 100644
index 0000000..1d93f44
--- /dev/null
+++ b/src/demandlib/bdew/bdew_data/h25.csv
@@ -0,0 +1,98 @@
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diff --git a/src/demandlib/bdew/bdew_data/l25.csv b/src/demandlib/bdew/bdew_data/l25.csv
new file mode 100644
index 0000000..e0bbf43
--- /dev/null
+++ b/src/demandlib/bdew/bdew_data/l25.csv
@@ -0,0 +1,98 @@
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diff --git a/src/demandlib/bdew/bdew_data/p25.csv b/src/demandlib/bdew/bdew_data/p25.csv
new file mode 100644
index 0000000..571340e
--- /dev/null
+++ b/src/demandlib/bdew/bdew_data/p25.csv
@@ -0,0 +1,98 @@
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diff --git a/src/demandlib/bdew/bdew_data/s25.csv b/src/demandlib/bdew/bdew_data/s25.csv
new file mode 100644
index 0000000..ef1fe0c
--- /dev/null
+++ b/src/demandlib/bdew/bdew_data/s25.csv
@@ -0,0 +1,98 @@
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+18:15-18:30,93.312,85.988,77.325,44.004,51.983,48.569,25.933,29.059,25.033,19.380,11.584,9.001,4.892,4.551,4.986,3.680,3.337,3.775,5.996,4.354,5.176,6.730,7.148,6.154,7.645,8.232,8.320,25.086,24.975,29.801,64.013,57.374,68.094,97.414,102.307,93.699
+18:30-18:45,93.228,86.413,75.610,45.602,54.465,47.390,26.961,27.804,25.842,23.373,12.865,9.454,5.641,3.919,4.567,5.015,4.724,4.303,5.173,4.047,5.286,6.760,7.515,6.121,7.305,9.216,8.912,24.814,25.936,29.684,60.440,57.649,67.089,94.338,102.271,89.665
+18:45-19:00,95.265,83.678,74.935,45.156,56.271,47.192,25.596,28.481,26.552,20.247,12.218,9.604,6.386,3.756,4.963,4.608,3.706,4.769,5.999,4.532,5.768,6.241,8.738,6.528,7.887,9.404,9.481,24.401,25.936,29.580,60.000,57.312,66.173,96.000,98.838,87.972
+19:00-19:15,94.052,86.480,74.148,42.162,54.513,46.555,26.751,28.246,27.091,24.321,14.736,10.347,6.635,4.487,5.367,3.191,2.957,4.583,6.545,4.622,5.467,7.510,9.327,7.260,9.202,8.534,9.511,25.956,25.615,30.653,59.453,57.937,67.116,96.878,93.644,89.919
+19:15-19:30,89.455,80.851,73.487,43.935,55.399,46.250,28.855,28.082,28.017,22.777,13.596,10.902,6.749,5.368,6.027,4.442,4.050,4.320,7.362,4.152,5.466,4.690,10.527,7.336,8.345,8.834,9.423,25.335,25.035,30.328,58.821,57.733,64.836,89.658,92.674,89.768
+19:30-19:45,87.454,80.041,71.619,43.351,55.335,44.441,28.321,28.965,27.239,25.620,16.722,11.364,8.579,6.045,5.845,5.347,3.995,4.388,7.615,5.471,6.051,5.911,10.534,7.763,7.161,7.954,9.298,25.850,25.958,28.685,59.613,58.788,64.240,91.506,90.243,87.646
+19:45-20:00,81.705,81.847,70.726,44.896,52.707,43.624,32.512,27.884,28.025,27.967,17.209,12.256,8.536,5.567,5.481,4.717,4.507,5.106,8.398,5.617,5.895,7.590,9.896,7.764,7.378,7.164,8.116,26.784,24.734,29.009,60.013,59.113,65.165,91.100,87.257,86.282
+20:00-20:15,80.924,78.606,73.119,48.432,50.230,45.518,28.923,30.854,28.633,26.672,17.713,12.979,7.006,6.095,6.529,5.764,5.600,5.454,10.072,5.877,5.896,7.622,9.946,7.983,7.021,7.788,7.897,26.463,26.088,29.661,61.087,55.856,67.036,91.478,87.804,89.379
+20:15-20:30,78.360,76.882,70.933,50.351,50.322,43.833,26.617,29.137,28.705,26.881,17.556,13.128,6.569,6.597,7.100,6.161,5.722,5.861,8.020,6.368,6.081,6.722,9.366,7.661,7.478,6.458,8.080,27.923,26.103,29.279,58.610,56.591,65.439,92.279,82.748,86.871
+20:30-20:45,74.365,72.847,70.360,47.599,49.468,44.259,29.553,29.736,28.705,25.299,17.241,13.414,7.689,6.597,6.874,5.709,4.837,5.548,8.347,7.831,6.503,7.108,9.336,7.684,7.133,6.010,7.730,26.545,27.599,28.529,57.817,53.903,64.727,87.823,80.893,87.383
+20:45-21:00,74.881,70.905,69.663,50.486,49.897,43.169,28.540,29.096,29.404,26.451,16.837,13.210,6.878,5.574,6.706,6.200,5.187,5.630,8.636,6.213,6.933,7.349,8.643,7.807,7.676,6.002,7.942,25.130,26.942,27.736,57.109,53.603,64.342,83.835,80.263,85.098
+21:00-21:15,74.250,68.060,68.576,47.387,50.567,43.580,27.866,28.854,29.113,26.066,16.509,13.139,6.390,5.702,6.144,6.010,4.735,6.057,8.532,5.571,6.924,7.791,7.953,7.872,7.747,6.118,7.923,26.109,25.350,28.631,58.150,53.074,62.130,83.270,79.623,80.850
+21:15-21:30,69.530,65.531,65.220,46.496,49.362,43.753,27.926,28.699,28.650,25.879,16.804,12.960,6.721,5.913,6.062,6.525,4.801,5.652,8.780,6.046,7.350,7.744,8.195,7.908,8.300,6.890,8.148,26.066,25.473,28.974,56.942,52.408,60.132,80.956,75.201,78.852
+21:30-21:45,69.426,65.690,64.587,46.077,48.384,44.252,27.559,28.863,28.298,24.630,15.973,12.929,6.222,5.433,5.963,5.625,4.781,5.595,7.828,6.270,7.339,7.222,8.552,7.565,8.329,6.152,7.656,26.788,26.025,29.139,52.793,51.504,59.796,76.576,73.638,77.590
+21:45-22:00,66.094,65.662,63.854,47.567,46.613,44.424,27.717,29.504,28.218,24.221,16.534,13.266,7.237,5.841,6.211,5.682,6.186,6.537,8.171,6.919,7.874,6.642,8.922,7.604,8.002,6.350,7.471,27.308,26.488,28.547,50.931,53.231,58.605,77.709,75.955,77.469
+22:00-22:15,65.828,63.255,61.513,44.984,43.965,43.769,27.213,28.304,26.962,25.163,14.712,13.365,6.473,5.434,5.898,5.869,5.511,6.702,7.965,6.025,7.661,7.145,8.266,7.491,7.541,6.188,7.444,27.192,25.593,28.484,49.798,50.454,55.054,72.921,70.085,73.571
+22:15-22:30,66.042,60.391,59.142,44.572,42.132,42.001,26.704,26.561,26.018,24.762,14.587,13.619,6.525,5.891,5.834,5.403,5.286,5.950,7.540,5.825,6.878,8.368,7.820,7.380,8.153,5.599,7.926,26.120,25.044,27.627,50.198,51.824,53.488,72.526,67.085,69.917
+22:30-22:45,63.792,58.675,56.271,42.524,41.092,41.008,24.470,26.057,26.036,24.715,14.254,13.785,6.819,5.051,5.838,5.352,4.393,6.033,7.684,5.454,6.670,9.286,8.984,7.349,7.772,6.623,7.633,25.542,25.011,26.094,49.361,52.211,51.586,69.570,66.991,68.682
+22:45-23:00,60.288,54.586,53.631,42.281,39.906,40.493,25.081,27.413,25.629,24.216,14.907,13.722,6.853,5.403,5.618,4.679,3.712,5.594,7.179,5.044,6.242,9.663,8.134,7.403,7.653,6.034,7.610,24.512,22.861,25.241,48.365,49.612,50.586,67.163,64.880,66.995
+23:00-23:15,56.704,52.793,52.243,42.718,37.395,39.197,25.710,24.683,24.870,23.533,15.050,13.345,6.352,5.378,5.227,3.796,3.294,5.164,6.500,4.822,5.912,9.135,7.863,7.563,7.964,5.789,8.091,25.968,22.641,24.811,47.604,45.868,49.617,65.102,61.818,63.011
+23:15-23:30,57.783,48.899,49.984,40.892,38.421,38.709,25.174,24.089,24.722,22.388,14.467,12.687,5.884,5.431,5.249,4.317,3.581,4.811,6.127,4.943,5.860,9.110,7.532,7.496,8.034,6.073,7.642,24.283,21.913,23.638,47.851,45.269,47.100,60.793,61.101,60.479
+23:30-23:45,54.702,47.327,48.095,41.010,38.978,37.629,25.896,23.231,24.078,22.069,13.943,12.595,5.759,5.360,5.260,4.419,3.565,4.782,5.881,4.797,5.897,7.852,7.718,7.296,7.437,6.319,7.356,26.284,22.392,23.127,46.306,41.204,45.358,58.269,58.331,59.112
+23:45-00:00,54.335,46.957,47.165,41.442,38.972,37.484,25.069,22.517,24.473,21.900,14.413,12.988,5.440,5.297,6.087,4.479,3.713,4.834,6.912,4.885,6.002,8.275,7.635,7.485,7.401,6.628,7.543,26.512,21.073,23.283,44.454,40.885,44.814,59.398,57.183,59.141
diff --git a/src/demandlib/bdew/elec_slp.py b/src/demandlib/bdew/elec_slp.py
index 8fae971..63db4fa 100644
--- a/src/demandlib/bdew/elec_slp.py
+++ b/src/demandlib/bdew/elec_slp.py
@@ -19,6 +19,41 @@ import pandas as pd
 
 from demandlib.tools import add_weekdays2df
 
+from ._profiles25 import _bdew_datapath
+
+
+def dynamisation_function(timeindex: pd.DatetimeIndex) -> pd.Series:
+    r"""
+    Use the dynamisation function of the BDEW to smoothen the seasonal
+    edges. Functions resolution is daily.
+
+        .. math::
+            F_t = -3,92\cdot10^{-10} \cdot t^4 + 3,2\cdot10^{-7}
+            \cdot t^3– 7,02\cdot10^{-5}\cdot t^2 + 2,1\cdot10^{-3}
+            \cdot t + 1,24
+
+    With `t` the day of the year as a decimal number.
+    """
+
+    # The standard sais that the day of the year (1 through 365)
+    # should be used. However, normalisation works better if we
+    # allow fractional days and start by day 0.
+    day_of_year = pd.Series(
+        [((q + 1) / (24 * 4)) for q in range(len(timeindex))],
+        index=timeindex,
+    )
+
+    # Calculate the smoothing factor of the BDEW dynamic H0 profile
+    # Adjustment of for normalisation: from -3,92 to -3.916649251
+    return pd.Series(
+        -3.916649251 * 10**-10 * day_of_year**4
+        + 3.2 * 10**-7 * day_of_year**3
+        - 7.02 * 10**-5 * day_of_year**2
+        + 0.0021 * day_of_year
+        + 1.24,
+        index=timeindex,
+    )
+
 
 class ElecSlp:
     """Generate electrical standardized load profiles based on the BDEW method.
@@ -46,7 +81,6 @@ class ElecSlp:
             hoy = 8784
         else:
             hoy = 8760
-        self._datapath = os.path.join(os.path.dirname(__file__), "bdew_data")
         self._date_time_index = pd.date_range(
             datetime.datetime(year, 1, 1, 0), periods=hoy * 4, freq="15Min"
         )
@@ -98,7 +132,7 @@ class ElecSlp:
         """
 
         # define file path of slp csv data
-        file_path = os.path.join(self._datapath, "selp_series.csv")
+        file_path = os.path.join(_bdew_datapath, "selp_series.csv")
 
         # Read standard load profile series from csv file
         selp_series = pd.read_csv(file_path)
@@ -165,37 +199,9 @@ class ElecSlp:
         return new_df.div(new_df.sum(axis=0), axis=1)
 
     def create_dynamic_h0_profile(self):
-        r"""
-        Use the dynamisation function of the BDEW to smoothen the seasonal
-        edges. Functions resolution is daily.
-
-            .. math::
-                F_t = -3,92\cdot10^{-10} \cdot t^4 + 3,2\cdot10^{-7}
-                \cdot t^3– 7,02\cdot10^{-5}\cdot t^2 + 2,1\cdot10^{-3}
-                \cdot t + 1,24
-
-        With `t` the day of the year as a decimal number.
-
-        Adjustment of accuracy: from -3,92 to -3.916649251
-        """
-        # Create a Series with the day of the year as decimal number
-        decimal_day = pd.Series(
-            [((q + 1) / (24 * 4)) for q in range(len(self.slp_frame))],
-            index=self.slp_frame.index,
-        )
-
-        # Calculate the smoothing factor of the BDEW dynamic H0 profile
-        smoothing_factor = (
-            -3.916649251 * 10**-10 * decimal_day**4
-            + 3.2 * 10**-7 * decimal_day**3
-            - 7.02 * 10**-5 * decimal_day**2
-            + 0.0021 * decimal_day
-            + 1.24
-        )
-
-        # Multiply the smoothing factor with the default H0 profile
+        # Multiply the smoothing factor with the static H0 profile
         self.slp_frame["h0_dyn"] = self.slp_frame["h0"].mul(
-            smoothing_factor, axis=0
+            dynamisation_function(self.slp_frame.index), axis=0
         )
         return self.slp_frame["h0_dyn"]
 
diff --git a/src/demandlib/tools.py b/src/demandlib/tools.py
index dd8cc11..8e8024c 100644
--- a/src/demandlib/tools.py
+++ b/src/demandlib/tools.py
@@ -3,7 +3,11 @@
 import pandas as pd
 
 
-def add_weekdays2df(time_df, holidays=None, holiday_is_sunday=False):
+def add_weekdays2df(
+    time_df: pd.DataFrame,
+    holidays: dict | list | None = None,
+    holiday_is_sunday: bool = False,
+):
     r"""Giving back a DataFrame containing weekdays and optionally holidays for
      the given year.
 
@@ -14,8 +18,8 @@ def add_weekdays2df(time_df, holidays=None, holiday_is_sunday=False):
 
     Optional Parameters
     -------------------
-    holidays : array with information for every hour of the year, if holiday or
-        not (0: holiday, 1: no holiday)
+    holidays : list or dict containg dates of holidays (see set_holidays_in_df)
+
     holiday_is_sunday : boolean
         If set to True, all holidays (0) will be set to sundays (7).
 
@@ -31,20 +35,42 @@ def add_weekdays2df(time_df, holidays=None, holiday_is_sunday=False):
     time_df["weekday"] = time_df.index.weekday + 1
     time_df["date"] = time_df.index.date
 
+    set_holidays_in_df(time_df, holidays)
+
+    if holiday_is_sunday:
+        time_df.weekday = time_df.weekday.mask(
+            cond=time_df.weekday == 0, other=7
+        )
+
+    return time_df
+
+
+def set_holidays_in_df(time_df: pd.DataFrame, holidays: dict | list) -> None:
+    r"""Giving back a DataFrame containing weekdays and optionally holidays for
+     the given year.
+
+    Parameters
+    ----------
+    time_df : pandas DataFrame
+        DataFrame in which the "weekday" (1..7)
+        should replaced by 0 for holidays
+
+    holidays : list or dict containg dates of holidays. If a dict is chosen,
+        the key should identify the date, dict values are ignored.
+
+    Returns
+    -------
+    pandas.DataFrame : DataFrame with weekdays
+    """
     # Set weekday to Holiday (0) for all holidays
     if holidays is not None:
         if isinstance(holidays, dict):
             holidays = list(holidays.keys())
         time_df["weekday"] = time_df["weekday"].mask(
-            cond=pd.to_datetime(time_df["date"]).isin(
+            cond=pd.to_datetime(time_df.index.date).isin(
                 pd.to_datetime(holidays)
             ),
             other=0,
         )
 
-    if holiday_is_sunday:
-        time_df.weekday = time_df.weekday.mask(
-            cond=time_df.weekday == 0, other=7
-        )
-
-    return time_df
+    return None


Test patch:
diff --git a/tests/test_elec_slp.py b/tests/test_elec_slp.py
index d955c7d..9bd2b9e 100644
--- a/tests/test_elec_slp.py
+++ b/tests/test_elec_slp.py
@@ -128,3 +128,88 @@ def test_changed_seasons():
         round(slp2.get_profiles("g0").iloc[12000:12096].sum().iloc[0], 5)
         == 0.00612
     )
+
+
+def test_slp25():
+    holiday_dict = {
+        datetime.date(2010, 1, 1): "New year",
+        datetime.date(2010, 4, 2): "Good Friday",
+        datetime.date(2010, 4, 5): "Easter Monday",
+        datetime.date(2010, 5, 1): "Labour Day",
+        datetime.date(2010, 5, 13): "Ascension Thursday",
+        datetime.date(2010, 5, 24): "Whit Monday",
+        datetime.date(2010, 10, 3): "Day of German Unity",
+        datetime.date(2010, 12, 25): "Christmas Day",
+        datetime.date(2010, 12, 26): "Second Christmas Day",
+    }
+
+    holiday_list = list(holiday_dict.keys())
+
+    index01m = pd.date_range(  # 1 minute time steps
+        start="2010-01-01 00:00",
+        end="2010-12-31 23:45",
+        freq="1min",
+    )
+
+    index15m = pd.date_range(  # 15 minute time steps
+        start="2010-01-01 00:00",
+        end="2010-12-31 23:45",
+        freq="15min",
+    )
+
+    index60m = pd.date_range(  # 60 minute time steps
+        start="2010-01-01 00:00",
+        end="2010-12-31 23:45",
+        freq="60min",
+    )
+
+    index_2years = pd.date_range(  # 2010 and 2011
+        start="2010-01-01 00:00",
+        end="2011-12-31 23:45",
+        freq="15min",
+    )
+
+    index_shifted_year = pd.date_range(  # 12 month starting June 2010
+        start="2010-06-01 00:00",
+        end="2011-05-31 23:45",
+        freq="15min",
+    )
+
+    with pytest.raises(NotImplementedError):
+        _ = bdew._profiles25.BDEW25Profile(index15m)
+
+    with pytest.raises(NotImplementedError):
+        _ = bdew._profiles25.DynamicBDEW25Profile(index15m)
+
+    for slp_type in [bdew.G25, bdew.H25, bdew.L25, bdew.P25, bdew.S25]:
+        # We allow 1.0 % tollerenace (with holidays)
+        slp_profile = slp_type(index15m, holidays=holiday_dict)
+        assert slp_profile.sum() / 4 == pytest.approx(1e6, rel=0.01)
+
+        # SLP of a shorter index is just the excerpt of the original SLP
+        short_slp = slp_type(index15m[5:1000], holidays=holiday_dict)
+        assert short_slp.equals(slp_profile[5:1000])
+
+        # Higher resolution samples the same SLP, the integral is unchanged
+        slp_resampled = slp_type(index01m, holidays=holiday_list)
+        assert slp_resampled.sum() / 15 == pytest.approx(
+            slp_profile.sum(), rel=0.0001
+        )
+
+        # Lower time resolution averages, so integral is unchanged
+        slp_resampled = slp_type(index60m, holidays=holiday_dict)
+        assert slp_resampled.sum() * 4 == pytest.approx(
+            slp_profile.sum(), rel=0.0001
+        )
+
+        # We allow 2.5 % tollerenace (without holidays)
+        slp_profile = slp_type(index15m)
+        assert slp_profile.sum() / 4 == pytest.approx(1e6, rel=0.025)
+
+        # We allow 2.5 % tollerenace (without holidays)
+        slp_profile = slp_type(index_shifted_year)
+        assert slp_profile.sum() / 4 == pytest.approx(1e6, rel=0.025)
+
+        # We allow 2.5 % tollerenace (without holidays), two years
+        slp_profile = slp_type(index_2years)
+        assert slp_profile.sum() / 4 == pytest.approx(2e6, rel=0.025)