Feature/bdew load profiles 2025

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()

src/demandlib/__init__.py

@@ -1 +1,9 @@
 __version__ = "0.2.2a1"
+
+from . import bdew
+from . import vdi
+
+__all__ = [
+    "bdew",
+    "vdi",
+]

src/demandlib/bdew/__init__.py

@@ -7,5 +7,20 @@
 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",
+]

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"