Move Python package files to new package directories

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>

src/akkudoktoreos/class_akku.py

@@ -0,0 +1,240 @@
+import numpy as np
+
+
+class PVAkku:
+    def __init__(
+        self,
+        kapazitaet_wh=None,
+        hours=None,
+        lade_effizienz=0.88,
+        entlade_effizienz=0.88,
+        max_ladeleistung_w=None,
+        start_soc_prozent=0,
+        min_soc_prozent=0,
+        max_soc_prozent=100,
+    ):
+        # Battery capacity in Wh
+        self.kapazitaet_wh = kapazitaet_wh
+        # Initial state of charge in Wh
+        self.start_soc_prozent = start_soc_prozent
+        self.soc_wh = (start_soc_prozent / 100) * kapazitaet_wh
+        self.hours = (
+            hours if hours is not None else 24
+        )  # Default to 24 hours if not specified
+        self.discharge_array = np.full(self.hours, 1)
+        self.charge_array = np.full(self.hours, 1)
+        # Charge and discharge efficiency
+        self.lade_effizienz = lade_effizienz
+        self.entlade_effizienz = entlade_effizienz
+        self.max_ladeleistung_w = (
+            max_ladeleistung_w if max_ladeleistung_w else self.kapazitaet_wh
+        )
+        self.min_soc_prozent = min_soc_prozent
+        self.max_soc_prozent = max_soc_prozent
+        # Calculate min and max SoC in Wh
+        self.min_soc_wh = (self.min_soc_prozent / 100) * self.kapazitaet_wh
+        self.max_soc_wh = (self.max_soc_prozent / 100) * self.kapazitaet_wh
+
+    def to_dict(self):
+        return {
+            "kapazitaet_wh": self.kapazitaet_wh,
+            "start_soc_prozent": self.start_soc_prozent,
+            "soc_wh": self.soc_wh,
+            "hours": self.hours,
+            "discharge_array": self.discharge_array.tolist(),  # Convert np.array to list
+            "charge_array": self.charge_array.tolist(),
+            "lade_effizienz": self.lade_effizienz,
+            "entlade_effizienz": self.entlade_effizienz,
+            "max_ladeleistung_w": self.max_ladeleistung_w,
+        }
+
+    @classmethod
+    def from_dict(cls, data):
+        # Create a new object with basic data
+        obj = cls(
+            kapazitaet_wh=data["kapazitaet_wh"],
+            hours=data["hours"],
+            lade_effizienz=data["lade_effizienz"],
+            entlade_effizienz=data["entlade_effizienz"],
+            max_ladeleistung_w=data["max_ladeleistung_w"],
+            start_soc_prozent=data["start_soc_prozent"],
+        )
+        # Set arrays
+        obj.discharge_array = np.array(data["discharge_array"])
+        obj.charge_array = np.array(data["charge_array"])
+        obj.soc_wh = data[
+            "soc_wh"
+        ]  # Set current state of charge, which may differ from start_soc_prozent
+
+        return obj
+
+    def reset(self):
+        self.soc_wh = (self.start_soc_prozent / 100) * self.kapazitaet_wh
+        # Ensure soc_wh is within min and max limits
+        self.soc_wh = min(max(self.soc_wh, self.min_soc_wh), self.max_soc_wh)
+
+        self.discharge_array = np.full(self.hours, 1)
+        self.charge_array = np.full(self.hours, 1)
+
+    def set_discharge_per_hour(self, discharge_array):
+        assert len(discharge_array) == self.hours
+        self.discharge_array = np.array(discharge_array)
+
+    def set_charge_per_hour(self, charge_array):
+        assert len(charge_array) == self.hours
+        self.charge_array = np.array(charge_array)
+
+    def ladezustand_in_prozent(self):
+        return (self.soc_wh / self.kapazitaet_wh) * 100
+
+    def energie_abgeben(self, wh, hour):
+        if self.discharge_array[hour] == 0:
+            return 0.0, 0.0  # No energy discharge and no losses
+
+        # Calculate the maximum energy that can be discharged considering min_soc and efficiency
+        max_possible_discharge_wh = (
+            self.soc_wh - self.min_soc_wh
+        ) * self.entlade_effizienz
+        max_possible_discharge_wh = max(
+            max_possible_discharge_wh, 0.0
+        )  # Ensure non-negative
+
+        # Consider the maximum discharge power of the battery
+        max_abgebbar_wh = min(max_possible_discharge_wh, self.max_ladeleistung_w)
+
+        # The actually discharged energy cannot exceed requested energy or maximum discharge
+        tatsaechlich_abgegeben_wh = min(wh, max_abgebbar_wh)
+
+        # Calculate the actual amount withdrawn from the battery (before efficiency loss)
+        if self.entlade_effizienz > 0:
+            tatsaechliche_entnahme_wh = (
+                tatsaechlich_abgegeben_wh / self.entlade_effizienz
+            )
+        else:
+            tatsaechliche_entnahme_wh = 0.0
+
+        # Update the state of charge considering the actual withdrawal
+        self.soc_wh -= tatsaechliche_entnahme_wh
+        # Ensure soc_wh does not go below min_soc_wh
+        self.soc_wh = max(self.soc_wh, self.min_soc_wh)
+
+        # Calculate losses due to efficiency
+        verluste_wh = tatsaechliche_entnahme_wh - tatsaechlich_abgegeben_wh
+
+        # Return the actually discharged energy and the losses
+        return tatsaechlich_abgegeben_wh, verluste_wh
+
+    def energie_laden(self, wh, hour):
+        if hour is not None and self.charge_array[hour] == 0:
+            return 0, 0  # Charging not allowed in this hour
+
+        # If no value for wh is given, use the maximum charging power
+        wh = wh if wh is not None else self.max_ladeleistung_w
+
+        # Relative to the maximum charging power (between 0 and 1)
+        relative_ladeleistung = self.charge_array[hour]
+        effektive_ladeleistung = relative_ladeleistung * self.max_ladeleistung_w
+
+        # Calculate the maximum energy that can be charged considering max_soc and efficiency
+        if self.lade_effizienz > 0:
+            max_possible_charge_wh = (
+                self.max_soc_wh - self.soc_wh
+            ) / self.lade_effizienz
+        else:
+            max_possible_charge_wh = 0.0
+        max_possible_charge_wh = max(max_possible_charge_wh, 0.0)  # Ensure non-negative
+
+        # The actually charged energy cannot exceed requested energy, charging power, or maximum possible charge
+        effektive_lademenge = min(wh, effektive_ladeleistung, max_possible_charge_wh)
+
+        # Energy actually stored in the battery
+        geladene_menge = effektive_lademenge * self.lade_effizienz
+
+        # Update soc_wh
+        self.soc_wh += geladene_menge
+        # Ensure soc_wh does not exceed max_soc_wh
+        self.soc_wh = min(self.soc_wh, self.max_soc_wh)
+
+        # Calculate losses
+        verluste_wh = effektive_lademenge - geladene_menge
+
+        return geladene_menge, verluste_wh
+
+    def aktueller_energieinhalt(self):
+        """
+        This method returns the current remaining energy considering efficiency.
+        It accounts for both charging and discharging efficiency.
+        """
+        # Calculate remaining energy considering discharge efficiency
+        nutzbare_energie = (self.soc_wh - self.min_soc_wh) * self.entlade_effizienz
+        return max(nutzbare_energie, 0.0)
+
+
+if __name__ == "__main__":
+    # Test battery discharge below min_soc
+    print("Test: Discharge below min_soc")
+    akku = PVAkku(
+        kapazitaet_wh=10000,
+        hours=1,
+        start_soc_prozent=50,
+        min_soc_prozent=20,
+        max_soc_prozent=80,
+    )
+    akku.reset()
+    print(f"Initial SoC: {akku.ladezustand_in_prozent()}%")
+
+    # Try to discharge 5000 Wh
+    abgegeben_wh, verlust_wh = akku.energie_abgeben(5000, 0)
+    print(f"Energy discharged: {abgegeben_wh} Wh, Losses: {verlust_wh} Wh")
+    print(f"SoC after discharge: {akku.ladezustand_in_prozent()}%")
+    print(f"Expected min SoC: {akku.min_soc_prozent}%")
+
+    # Test battery charge above max_soc
+    print("\nTest: Charge above max_soc")
+    akku = PVAkku(
+        kapazitaet_wh=10000,
+        hours=1,
+        start_soc_prozent=50,
+        min_soc_prozent=20,
+        max_soc_prozent=80,
+    )
+    akku.reset()
+    print(f"Initial SoC: {akku.ladezustand_in_prozent()}%")
+
+    # Try to charge 5000 Wh
+    geladen_wh, verlust_wh = akku.energie_laden(5000, 0)
+    print(f"Energy charged: {geladen_wh} Wh, Losses: {verlust_wh} Wh")
+    print(f"SoC after charge: {akku.ladezustand_in_prozent()}%")
+    print(f"Expected max SoC: {akku.max_soc_prozent}%")
+
+    # Test charging when battery is at max_soc
+    print("\nTest: Charging when at max_soc")
+    akku = PVAkku(
+        kapazitaet_wh=10000,
+        hours=1,
+        start_soc_prozent=80,
+        min_soc_prozent=20,
+        max_soc_prozent=80,
+    )
+    akku.reset()
+    print(f"Initial SoC: {akku.ladezustand_in_prozent()}%")
+
+    geladen_wh, verlust_wh = akku.energie_laden(5000, 0)
+    print(f"Energy charged: {geladen_wh} Wh, Losses: {verlust_wh} Wh")
+    print(f"SoC after charge: {akku.ladezustand_in_prozent()}%")
+
+    # Test discharging when battery is at min_soc
+    print("\nTest: Discharging when at min_soc")
+    akku = PVAkku(
+        kapazitaet_wh=10000,
+        hours=1,
+        start_soc_prozent=20,
+        min_soc_prozent=20,
+        max_soc_prozent=80,
+    )
+    akku.reset()
+    print(f"Initial SoC: {akku.ladezustand_in_prozent()}%")
+
+    abgegeben_wh, verlust_wh = akku.energie_abgeben(5000, 0)
+    print(f"Energy discharged: {abgegeben_wh} Wh, Losses: {verlust_wh} Wh")
+    print(f"SoC after discharge: {akku.ladezustand_in_prozent()}%")

src/akkudoktoreos/class_ems.py

@@ -0,0 +1,159 @@
+from datetime import datetime
+from typing import Dict, List, Optional, Union
+
+import numpy as np
+
+
+def replace_nan_with_none(
+    data: Union[np.ndarray, dict, list, float],
+) -> Union[List, dict, float, None]:
+    if data is None:
+        return None
+    if isinstance(data, np.ndarray):
+        # Use numpy vectorized approach
+        return np.where(np.isnan(data), None, data).tolist()
+    elif isinstance(data, dict):
+        return {key: replace_nan_with_none(value) for key, value in data.items()}
+    elif isinstance(data, list):
+        return [replace_nan_with_none(element) for element in data]
+    elif isinstance(data, (float, np.floating)) and np.isnan(data):
+        return None
+    else:
+        return data
+
+
+class EnergieManagementSystem:
+    def __init__(
+        self,
+        pv_prognose_wh: Optional[np.ndarray] = None,
+        strompreis_euro_pro_wh: Optional[np.ndarray] = None,
+        einspeiseverguetung_euro_pro_wh: Optional[np.ndarray] = None,
+        eauto: Optional[object] = None,
+        gesamtlast: Optional[np.ndarray] = None,
+        haushaltsgeraet: Optional[object] = None,
+        wechselrichter: Optional[object] = None,
+    ):
+        self.akku = wechselrichter.akku
+        self.gesamtlast = gesamtlast
+        self.pv_prognose_wh = pv_prognose_wh
+        self.strompreis_euro_pro_wh = strompreis_euro_pro_wh
+        self.einspeiseverguetung_euro_pro_wh = einspeiseverguetung_euro_pro_wh
+        self.eauto = eauto
+        self.haushaltsgeraet = haushaltsgeraet
+        self.wechselrichter = wechselrichter
+
+    def set_akku_discharge_hours(self, ds: List[int]) -> None:
+        self.akku.set_discharge_per_hour(ds)
+
+    def set_eauto_charge_hours(self, ds: List[int]) -> None:
+        self.eauto.set_charge_per_hour(ds)
+
+    def set_haushaltsgeraet_start(
+        self, ds: List[int], global_start_hour: int = 0
+    ) -> None:
+        self.haushaltsgeraet.set_startzeitpunkt(ds, global_start_hour=global_start_hour)
+
+    def reset(self) -> None:
+        self.eauto.reset()
+        self.akku.reset()
+
+    def simuliere_ab_jetzt(self) -> dict:
+        jetzt = datetime.now()
+        start_stunde = jetzt.hour
+        return self.simuliere(start_stunde)
+
+    def simuliere(self, start_stunde: int) -> dict:
+        # Ensure arrays have the same length
+        lastkurve_wh = self.gesamtlast
+        assert (
+            len(lastkurve_wh)
+            == len(self.pv_prognose_wh)
+            == len(self.strompreis_euro_pro_wh)
+        ), f"Array sizes do not match: Load Curve = {len(lastkurve_wh)}, PV Forecast = {len(self.pv_prognose_wh)}, Electricity Price = {len(self.strompreis_euro_pro_wh)}"
+
+        # Optimized total hours calculation
+        ende = len(lastkurve_wh)
+        total_hours = ende - start_stunde
+
+        # Pre-allocate arrays for the results, optimized for speed
+        last_wh_pro_stunde = np.zeros(total_hours)
+        netzeinspeisung_wh_pro_stunde = np.zeros(total_hours)
+        netzbezug_wh_pro_stunde = np.zeros(total_hours)
+        kosten_euro_pro_stunde = np.zeros(total_hours)
+        einnahmen_euro_pro_stunde = np.zeros(total_hours)
+        akku_soc_pro_stunde = np.zeros(total_hours)
+        eauto_soc_pro_stunde = np.zeros(total_hours)
+        verluste_wh_pro_stunde = np.zeros(total_hours)
+        haushaltsgeraet_wh_pro_stunde = np.zeros(total_hours)
+
+        # Set initial state
+        akku_soc_pro_stunde[0] = self.akku.ladezustand_in_prozent()
+        if self.eauto:
+            eauto_soc_pro_stunde[0] = self.eauto.ladezustand_in_prozent()
+
+        for stunde in range(start_stunde + 1, ende):
+            stunde_since_now = stunde - start_stunde
+
+            # Accumulate loads and PV generation
+            verbrauch = self.gesamtlast[stunde]
+
+            if self.haushaltsgeraet:
+                ha_load = self.haushaltsgeraet.get_last_fuer_stunde(stunde)
+                verbrauch += ha_load
+                haushaltsgeraet_wh_pro_stunde[stunde_since_now] = ha_load
+
+            # E-Auto handling
+            if self.eauto:
+                geladene_menge_eauto, verluste_eauto = self.eauto.energie_laden(
+                    None, stunde
+                )
+                verbrauch += geladene_menge_eauto
+                verluste_wh_pro_stunde[stunde_since_now] += verluste_eauto
+                eauto_soc_pro_stunde[stunde_since_now] = (
+                    self.eauto.ladezustand_in_prozent()
+                )
+
+            # Process inverter logic
+            erzeugung = self.pv_prognose_wh[stunde]
+            netzeinspeisung, netzbezug, verluste, eigenverbrauch = (
+                self.wechselrichter.energie_verarbeiten(erzeugung, verbrauch, stunde)
+            )
+            netzeinspeisung_wh_pro_stunde[stunde_since_now] = netzeinspeisung
+            netzbezug_wh_pro_stunde[stunde_since_now] = netzbezug
+            verluste_wh_pro_stunde[stunde_since_now] += verluste
+            last_wh_pro_stunde[stunde_since_now] = verbrauch
+
+            # Financial calculations
+            kosten_euro_pro_stunde[stunde_since_now] = (
+                netzbezug * self.strompreis_euro_pro_wh[stunde]
+            )
+            einnahmen_euro_pro_stunde[stunde_since_now] = (
+                netzeinspeisung * self.einspeiseverguetung_euro_pro_wh[stunde]
+            )
+
+            # Akku SOC tracking
+            akku_soc_pro_stunde[stunde_since_now] = self.akku.ladezustand_in_prozent()
+
+        # Total cost and return
+        gesamtkosten_euro = np.sum(kosten_euro_pro_stunde) - np.sum(
+            einnahmen_euro_pro_stunde
+        )
+
+        # Prepare output dictionary
+        out: Dict[str, Union[np.ndarray, float]] = {
+            "Last_Wh_pro_Stunde": last_wh_pro_stunde,
+            "Netzeinspeisung_Wh_pro_Stunde": netzeinspeisung_wh_pro_stunde,
+            "Netzbezug_Wh_pro_Stunde": netzbezug_wh_pro_stunde,
+            "Kosten_Euro_pro_Stunde": kosten_euro_pro_stunde,
+            "akku_soc_pro_stunde": akku_soc_pro_stunde,
+            "Einnahmen_Euro_pro_Stunde": einnahmen_euro_pro_stunde,
+            "Gesamtbilanz_Euro": gesamtkosten_euro,
+            "E-Auto_SoC_pro_Stunde": eauto_soc_pro_stunde,
+            "Gesamteinnahmen_Euro": np.sum(einnahmen_euro_pro_stunde),
+            "Gesamtkosten_Euro": np.sum(kosten_euro_pro_stunde),
+            "Verluste_Pro_Stunde": verluste_wh_pro_stunde,
+            "Gesamt_Verluste": np.sum(verluste_wh_pro_stunde),
+            "Haushaltsgeraet_wh_pro_stunde": haushaltsgeraet_wh_pro_stunde,
+        }
+
+        return replace_nan_with_none(out)

src/akkudoktoreos/class_haushaltsgeraet.py

@@ -0,0 +1,59 @@
+import numpy as np
+
+
+class Haushaltsgeraet:
+    def __init__(self, hours=None, verbrauch_wh=None, dauer_h=None):
+        self.hours = hours  # Total duration for which the planning is done
+        self.verbrauch_wh = (
+            verbrauch_wh  # Total energy consumption of the device in kWh
+        )
+        self.dauer_h = dauer_h  # Duration of use in hours
+        self.lastkurve = np.zeros(self.hours)  # Initialize the load curve with zeros
+
+    def set_startzeitpunkt(self, start_hour, global_start_hour=0):
+        """
+        Sets the start time of the device and generates the corresponding load curve.
+        :param start_hour: The hour at which the device should start.
+        """
+        self.reset()
+
+        # Check if the duration of use is within the available time frame
+        if start_hour + self.dauer_h > self.hours:
+            raise ValueError("The duration of use exceeds the available time frame.")
+        if start_hour < global_start_hour:
+            raise ValueError("The start time is earlier than the available time frame.")
+
+        # Calculate power per hour based on total consumption and duration
+        leistung_pro_stunde = self.verbrauch_wh / self.dauer_h  # Convert to watt-hours
+
+        # Set the power for the duration of use in the load curve array
+        self.lastkurve[start_hour : start_hour + self.dauer_h] = leistung_pro_stunde
+
+    def reset(self):
+        """
+        Resets the load curve.
+        """
+        self.lastkurve = np.zeros(self.hours)
+
+    def get_lastkurve(self):
+        """
+        Returns the current load curve.
+        """
+        return self.lastkurve
+
+    def get_last_fuer_stunde(self, hour):
+        """
+        Returns the load for a specific hour.
+        :param hour: The hour for which the load is queried.
+        :return: The load in watts for the specified hour.
+        """
+        if hour < 0 or hour >= self.hours:
+            raise ValueError("The specified hour is outside the available time frame.")
+
+        return self.lastkurve[hour]
+
+    def spaetestmoeglicher_startzeitpunkt(self):
+        """
+        Returns the latest possible start time at which the device can still run completely.
+        """
+        return self.hours - self.dauer_h

src/akkudoktoreos/class_inverter.py

@@ -0,0 +1,70 @@
+class Wechselrichter:
+    def __init__(self, max_leistung_wh, akku):
+        self.max_leistung_wh = (
+            max_leistung_wh  # Maximum power that the inverter can handle
+        )
+        self.akku = akku  # Connection to a battery object
+
+    def energie_verarbeiten(self, erzeugung, verbrauch, hour):
+        verluste = 0  # Losses during processing
+        netzeinspeisung = 0  # Grid feed-in
+        netzbezug = 0.0  # Grid draw
+        eigenverbrauch = 0.0  # Self-consumption
+
+        if erzeugung >= verbrauch:
+            if verbrauch > self.max_leistung_wh:
+                # If consumption exceeds maximum inverter power
+                verluste += erzeugung - self.max_leistung_wh
+                restleistung_nach_verbrauch = self.max_leistung_wh - verbrauch
+                netzbezug = (
+                    -restleistung_nach_verbrauch
+                )  # Negative indicates feeding into the grid
+                eigenverbrauch = self.max_leistung_wh
+            else:
+                # Remaining power after consumption
+                restleistung_nach_verbrauch = erzeugung - verbrauch
+
+                # Load battery with excess energy
+                geladene_energie, verluste_laden_akku = self.akku.energie_laden(
+                    restleistung_nach_verbrauch, hour
+                )
+                rest_überschuss = restleistung_nach_verbrauch - (
+                    geladene_energie + verluste_laden_akku
+                )
+
+                # Feed-in to the grid based on remaining capacity
+                if rest_überschuss > self.max_leistung_wh - verbrauch:
+                    netzeinspeisung = self.max_leistung_wh - verbrauch
+                    verluste += rest_überschuss - netzeinspeisung
+                else:
+                    netzeinspeisung = rest_überschuss
+
+                verluste += verluste_laden_akku
+                eigenverbrauch = verbrauch  # Self-consumption is equal to the load
+
+        else:
+            benötigte_energie = (
+                verbrauch - erzeugung
+            )  # Energy needed from external sources
+            max_akku_leistung = (
+                self.akku.max_ladeleistung_w
+            )  # Maximum battery discharge power
+
+            # Calculate remaining AC power available
+            rest_ac_leistung = max(self.max_leistung_wh - erzeugung, 0)
+
+            # Discharge energy from the battery based on need
+            if benötigte_energie < rest_ac_leistung:
+                aus_akku, akku_entladeverluste = self.akku.energie_abgeben(
+                    benötigte_energie, hour
+                )
+            else:
+                aus_akku, akku_entladeverluste = self.akku.energie_abgeben(
+                    rest_ac_leistung, hour
+                )
+
+            verluste += akku_entladeverluste  # Include losses from battery discharge
+            netzbezug = benötigte_energie - aus_akku  # Energy drawn from the grid
+            eigenverbrauch = erzeugung + aus_akku  # Total self-consumption
+
+        return netzeinspeisung, netzbezug, verluste, eigenverbrauch

src/akkudoktoreos/class_load.py

@@ -0,0 +1,110 @@
+from datetime import datetime
+
+import numpy as np
+
+# Load the .npz file when the application starts
+
+
+class LoadForecast:
+    def __init__(self, filepath=None, year_energy=None):
+        self.filepath = filepath
+        self.data = None
+        self.data_year_energy = None
+        self.year_energy = year_energy
+        self.load_data()
+
+    def get_daily_stats(self, date_str):
+        """
+        Returns the 24-hour profile with mean and standard deviation for a given date.
+
+        :param date_str: Date as a string in the format "YYYY-MM-DD"
+        :return: An array with shape (2, 24), contains means and standard deviations
+        """
+        # Convert the date string into a datetime object
+        date = self._convert_to_datetime(date_str)
+
+        # Calculate the day of the year (1 to 365)
+        day_of_year = date.timetuple().tm_yday
+
+        # Extract the 24-hour profile for the given date
+        daily_stats = self.data_year_energy[
+            day_of_year - 1
+        ]  # -1 because indexing starts at 0
+        return daily_stats
+
+    def get_hourly_stats(self, date_str, hour):
+        """
+        Returns the mean and standard deviation for a specific hour of a given date.
+
+        :param date_str: Date as a string in the format "YYYY-MM-DD"
+        :param hour: Specific hour (0 to 23)
+        :return: An array with shape (2,), contains mean and standard deviation for the specified hour
+        """
+        # Convert the date string into a datetime object
+        date = self._convert_to_datetime(date_str)
+
+        # Calculate the day of the year (1 to 365)
+        day_of_year = date.timetuple().tm_yday
+
+        # Extract mean and standard deviation for the given hour
+        hourly_stats = self.data_year_energy[
+            day_of_year - 1, :, hour
+        ]  # Access the specific hour
+
+        return hourly_stats
+
+    def get_stats_for_date_range(self, start_date_str, end_date_str):
+        """
+        Returns the means and standard deviations for a date range.
+
+        :param start_date_str: Start date as a string in the format "YYYY-MM-DD"
+        :param end_date_str: End date as a string in the format "YYYY-MM-DD"
+        :return: An array with aggregated data for the date range
+        """
+        start_date = self._convert_to_datetime(start_date_str)
+        end_date = self._convert_to_datetime(end_date_str)
+
+        start_day_of_year = start_date.timetuple().tm_yday
+        end_day_of_year = end_date.timetuple().tm_yday
+
+        # Note that in leap years, the day of the year may need adjustment
+        stats_for_range = self.data_year_energy[
+            start_day_of_year:end_day_of_year
+        ]  # -1 because indexing starts at 0
+        stats_for_range = stats_for_range.swapaxes(1, 0)
+
+        stats_for_range = stats_for_range.reshape(stats_for_range.shape[0], -1)
+        return stats_for_range
+
+    def load_data(self):
+        """Loads data from the specified file."""
+        try:
+            data = np.load(self.filepath)
+            self.data = np.array(
+                list(zip(data["yearly_profiles"], data["yearly_profiles_std"]))
+            )
+            self.data_year_energy = self.data * self.year_energy
+            # pprint(self.data_year_energy)
+        except FileNotFoundError:
+            print(f"Error: File {self.filepath} not found.")
+        except Exception as e:
+            print(f"An error occurred while loading data: {e}")
+
+    def get_price_data(self):
+        """Returns price data (currently not implemented)."""
+        return self.price_data
+
+    def _convert_to_datetime(self, date_str):
+        """Converts a date string to a datetime object."""
+        return datetime.strptime(date_str, "%Y-%m-%d")
+
+
+# Example usage of the class
+if __name__ == "__main__":
+    filepath = r"..\data\load_profiles.npz"  # Adjust the path to the .npz file
+    lf = LoadForecast(filepath=filepath, year_energy=2000)
+    specific_date_prices = lf.get_daily_stats("2024-02-16")  # Adjust date as needed
+    specific_hour_stats = lf.get_hourly_stats(
+        "2024-02-16", 12
+    )  # Adjust date and hour as needed
+    print(specific_hour_stats)

src/akkudoktoreos/class_load_container.py

@@ -0,0 +1,41 @@
+import numpy as np
+
+
+class Gesamtlast:
+    def __init__(self, prediction_hours=24):
+        self.lasten = {}  # Contains names and load arrays for different sources
+        self.prediction_hours = prediction_hours
+
+    def hinzufuegen(self, name, last_array):
+        """
+        Adds an array of loads for a specific source.
+
+        :param name: Name of the load source (e.g., "Household", "Heat Pump")
+        :param last_array: Array of loads, where each entry corresponds to an hour
+        """
+        if len(last_array) != self.prediction_hours:
+            raise ValueError(
+                f"Total load inconsistent lengths in arrays: {name} {len(last_array)}"
+            )
+        self.lasten[name] = last_array
+
+    def gesamtlast_berechnen(self):
+        """
+        Calculates the total load for each hour and returns an array of total loads.
+
+        :return: Array of total loads, where each entry corresponds to an hour
+        """
+        if not self.lasten:
+            return []
+
+        # Assumption: All load arrays have the same length
+        stunden = len(next(iter(self.lasten.values())))
+        gesamtlast_array = [0] * stunden
+
+        for last_array in self.lasten.values():
+            gesamtlast_array = [
+                gesamtlast + stundenlast
+                for gesamtlast, stundenlast in zip(gesamtlast_array, last_array)
+            ]
+
+        return np.array(gesamtlast_array)

src/akkudoktoreos/class_load_corrector.py

@@ -0,0 +1,222 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from sklearn.metrics import mean_squared_error, r2_score
+
+
+class LoadPredictionAdjuster:
+    def __init__(self, measured_data, predicted_data, load_forecast):
+        self.measured_data = measured_data
+        self.predicted_data = predicted_data
+        self.load_forecast = load_forecast
+        self.merged_data = self._merge_data()
+        self.train_data = None
+        self.test_data = None
+        self.weekday_diff = None
+        self.weekend_diff = None
+
+    def _remove_outliers(self, data, threshold=2):
+        # Calculate the Z-Score of the 'Last' data
+        data["Z-Score"] = np.abs(
+            (data["Last"] - data["Last"].mean()) / data["Last"].std()
+        )
+        # Filter the data based on the threshold
+        filtered_data = data[data["Z-Score"] < threshold]
+        return filtered_data.drop(columns=["Z-Score"])
+
+    def _merge_data(self):
+        # Convert the time column in both DataFrames to datetime
+        self.predicted_data["time"] = pd.to_datetime(self.predicted_data["time"])
+        self.measured_data["time"] = pd.to_datetime(self.measured_data["time"])
+
+        # Ensure both time columns have the same timezone
+        if self.measured_data["time"].dt.tz is None:
+            self.measured_data["time"] = self.measured_data["time"].dt.tz_localize(
+                "UTC"
+            )
+
+        self.predicted_data["time"] = (
+            self.predicted_data["time"]
+            .dt.tz_localize("UTC")
+            .dt.tz_convert("Europe/Berlin")
+        )
+        self.measured_data["time"] = self.measured_data["time"].dt.tz_convert(
+            "Europe/Berlin"
+        )
+
+        # Optionally: Remove timezone information if only working locally
+        self.predicted_data["time"] = self.predicted_data["time"].dt.tz_localize(None)
+        self.measured_data["time"] = self.measured_data["time"].dt.tz_localize(None)
+
+        # Now you can perform the merge
+        merged_data = pd.merge(
+            self.measured_data, self.predicted_data, on="time", how="inner"
+        )
+        print(merged_data)
+        merged_data["Hour"] = merged_data["time"].dt.hour
+        merged_data["DayOfWeek"] = merged_data["time"].dt.dayofweek
+        return merged_data
+
+    def calculate_weighted_mean(self, train_period_weeks=9, test_period_weeks=1):
+        self.merged_data = self._remove_outliers(self.merged_data)
+        train_end_date = self.merged_data["time"].max() - pd.Timedelta(
+            weeks=test_period_weeks
+        )
+        train_start_date = train_end_date - pd.Timedelta(weeks=train_period_weeks)
+
+        test_start_date = train_end_date + pd.Timedelta(hours=1)
+        test_end_date = (
+            test_start_date
+            + pd.Timedelta(weeks=test_period_weeks)
+            - pd.Timedelta(hours=1)
+        )
+
+        self.train_data = self.merged_data[
+            (self.merged_data["time"] >= train_start_date)
+            & (self.merged_data["time"] <= train_end_date)
+        ]
+
+        self.test_data = self.merged_data[
+            (self.merged_data["time"] >= test_start_date)
+            & (self.merged_data["time"] <= test_end_date)
+        ]
+
+        self.train_data["Difference"] = (
+            self.train_data["Last"] - self.train_data["Last Pred"]
+        )
+
+        weekdays_train_data = self.train_data[self.train_data["DayOfWeek"] < 5]
+        weekends_train_data = self.train_data[self.train_data["DayOfWeek"] >= 5]
+
+        self.weekday_diff = (
+            weekdays_train_data.groupby("Hour").apply(self._weighted_mean_diff).dropna()
+        )
+        self.weekend_diff = (
+            weekends_train_data.groupby("Hour").apply(self._weighted_mean_diff).dropna()
+        )
+
+    def _weighted_mean_diff(self, data):
+        train_end_date = self.train_data["time"].max()
+        weights = 1 / (train_end_date - data["time"]).dt.days.replace(0, np.nan)
+        weighted_mean = (data["Difference"] * weights).sum() / weights.sum()
+        return weighted_mean
+
+    def adjust_predictions(self):
+        self.train_data["Adjusted Pred"] = self.train_data.apply(
+            self._adjust_row, axis=1
+        )
+        self.test_data["Adjusted Pred"] = self.test_data.apply(self._adjust_row, axis=1)
+
+    def _adjust_row(self, row):
+        if row["DayOfWeek"] < 5:
+            return row["Last Pred"] + self.weekday_diff.get(row["Hour"], 0)
+        else:
+            return row["Last Pred"] + self.weekend_diff.get(row["Hour"], 0)
+
+    def plot_results(self):
+        self._plot_data(self.train_data, "Training")
+        self._plot_data(self.test_data, "Testing")
+
+    def _plot_data(self, data, data_type):
+        plt.figure(figsize=(14, 7))
+        plt.plot(
+            data["time"], data["Last"], label=f"Actual Last - {data_type}", color="blue"
+        )
+        plt.plot(
+            data["time"],
+            data["Last Pred"],
+            label=f"Predicted Last - {data_type}",
+            color="red",
+            linestyle="--",
+        )
+        plt.plot(
+            data["time"],
+            data["Adjusted Pred"],
+            label=f"Adjusted Predicted Last - {data_type}",
+            color="green",
+            linestyle=":",
+        )
+        plt.xlabel("Time")
+        plt.ylabel("Load")
+        plt.title(f"Actual vs Predicted vs Adjusted Predicted Load ({data_type} Data)")
+        plt.legend()
+        plt.grid(True)
+        plt.show()
+
+    def evaluate_model(self):
+        mse = mean_squared_error(
+            self.test_data["Last"], self.test_data["Adjusted Pred"]
+        )
+        r2 = r2_score(self.test_data["Last"], self.test_data["Adjusted Pred"])
+        print(f"Mean Squared Error: {mse}")
+        print(f"R-squared: {r2}")
+
+    def predict_next_hours(self, hours_ahead):
+        last_date = self.merged_data["time"].max()
+        future_dates = [
+            last_date + pd.Timedelta(hours=i) for i in range(1, hours_ahead + 1)
+        ]
+        future_df = pd.DataFrame({"time": future_dates})
+        future_df["Hour"] = future_df["time"].dt.hour
+        future_df["DayOfWeek"] = future_df["time"].dt.dayofweek
+        future_df["Last Pred"] = future_df["time"].apply(self._forecast_next_hours)
+        future_df["Adjusted Pred"] = future_df.apply(self._adjust_row, axis=1)
+        return future_df
+
+    def _forecast_next_hours(self, timestamp):
+        date_str = timestamp.strftime("%Y-%m-%d")
+        hour = timestamp.hour
+        daily_forecast = self.load_forecast.get_daily_stats(date_str)
+        return daily_forecast[0][hour] if hour < len(daily_forecast[0]) else np.nan
+
+
+# if __name__ == '__main__':
+#     estimator = LastEstimator()
+#     start_date = "2024-06-01"
+#     end_date = "2024-08-01"
+#     last_df = estimator.get_last(start_date, end_date)
+
+#     selected_columns = last_df[['timestamp', 'Last']]
+#     selected_columns['time'] = pd.to_datetime(selected_columns['timestamp']).dt.floor('H')
+#     selected_columns['Last'] = pd.to_numeric(selected_columns['Last'], errors='coerce')
+
+#     # Drop rows with NaN values
+#     cleaned_data = selected_columns.dropna()
+
+#     print(cleaned_data)
+#     # Create an instance of LoadForecast
+#     lf = LoadForecast(filepath=r'.\load_profiles.npz', year_energy=6000*1000)
+
+#     # Initialize an empty DataFrame to hold the forecast data
+#     forecast_list = []
+
+#     # Loop through each day in the date range
+#     for single_date in pd.date_range(cleaned_data['time'].min().date(), cleaned_data['time'].max().date()):
+#         date_str = single_date.strftime('%Y-%m-%d')
+#         daily_forecast = lf.get_daily_stats(date_str)
+#         mean_values = daily_forecast[0]  # Extract the mean values
+#         hours = [single_date + pd.Timedelta(hours=i) for i in range(24)]
+#         daily_forecast_df = pd.DataFrame({'time': hours, 'Last Pred': mean_values})
+#         forecast_list.append(daily_forecast_df)
+
+#     # Concatenate all daily forecasts into a single DataFrame
+#     forecast_df = pd.concat(forecast_list, ignore_index=True)
+
+#     # Create an instance of the LoadPredictionAdjuster class
+#     adjuster = LoadPredictionAdjuster(cleaned_data, forecast_df, lf)
+
+#     # Calculate the weighted mean differences
+#     adjuster.calculate_weighted_mean()
+
+#     # Adjust the predictions
+#     adjuster.adjust_predictions()
+
+#     # Plot the results
+#     adjuster.plot_results()
+
+#     # Evaluate the model
+#     adjuster.evaluate_model()
+
+#     # Predict the next x hours
+#     future_predictions = adjuster.predict_next_hours(48)
+#     print(future_predictions)

src/akkudoktoreos/class_optimize.py

@@ -0,0 +1,351 @@
+import os
+import random
+import sys
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+from deap import algorithms, base, creator, tools
+
+from modules.class_akku import PVAkku
+from modules.class_ems import EnergieManagementSystem
+from modules.class_haushaltsgeraet import Haushaltsgeraet
+from modules.class_inverter import Wechselrichter
+from modules.visualize import visualisiere_ergebnisse
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from config import moegliche_ladestroeme_in_prozent
+
+
+def isfloat(num: Any) -> bool:
+    """Check if a given input can be converted to float."""
+    try:
+        float(num)
+        return True
+    except ValueError:
+        return False
+
+
+class optimization_problem:
+    def __init__(
+        self,
+        prediction_hours: int = 24,
+        strafe: float = 10,
+        optimization_hours: int = 24,
+        verbose: bool = False,
+        fixed_seed: Optional[int] = None,
+    ):
+        """Initialize the optimization problem with the required parameters."""
+        self.prediction_hours = prediction_hours
+        self.strafe = strafe
+        self.opti_param = None
+        self.fixed_eauto_hours = prediction_hours - optimization_hours
+        self.possible_charge_values = moegliche_ladestroeme_in_prozent
+        self.verbose = verbose
+        self.fix_seed = fixed_seed
+
+        # Set a fixed seed for random operations if provided
+        if fixed_seed is not None:
+            random.seed(fixed_seed)
+
+    def split_individual(
+        self, individual: List[float]
+    ) -> Tuple[List[int], List[float], Optional[int]]:
+        """
+        Split the individual solution into its components:
+        1. Discharge hours (binary),
+        2. Electric vehicle charge hours (float),
+        3. Dishwasher start time (integer if applicable).
+        """
+        discharge_hours_bin = individual[: self.prediction_hours]
+        eautocharge_hours_float = individual[
+            self.prediction_hours : self.prediction_hours * 2
+        ]
+        spuelstart_int = (
+            individual[-1]
+            if self.opti_param and self.opti_param.get("haushaltsgeraete", 0) > 0
+            else None
+        )
+        return discharge_hours_bin, eautocharge_hours_float, spuelstart_int
+
+    def setup_deap_environment(
+        self, opti_param: Dict[str, Any], start_hour: int
+    ) -> None:
+        """
+        Set up the DEAP environment with fitness and individual creation rules.
+        """
+        self.opti_param = opti_param
+
+        # Remove existing FitnessMin and Individual classes from creator if present
+        for attr in ["FitnessMin", "Individual"]:
+            if attr in creator.__dict__:
+                del creator.__dict__[attr]
+
+        # Create new FitnessMin and Individual classes
+        creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
+        creator.create("Individual", list, fitness=creator.FitnessMin)
+
+        # Initialize toolbox with attributes and operations
+        self.toolbox = base.Toolbox()
+        self.toolbox.register("attr_bool", random.randint, 0, 1)
+        self.toolbox.register("attr_float", random.uniform, 0, 1)
+        self.toolbox.register("attr_int", random.randint, start_hour, 23)
+
+        # Register individual creation method based on household appliance parameter
+        if opti_param["haushaltsgeraete"] > 0:
+            self.toolbox.register(
+                "individual",
+                lambda: creator.Individual(
+                    [self.toolbox.attr_bool() for _ in range(self.prediction_hours)]
+                    + [self.toolbox.attr_float() for _ in range(self.prediction_hours)]
+                    + [self.toolbox.attr_int()]
+                ),
+            )
+        else:
+            self.toolbox.register(
+                "individual",
+                lambda: creator.Individual(
+                    [self.toolbox.attr_bool() for _ in range(self.prediction_hours)]
+                    + [self.toolbox.attr_float() for _ in range(self.prediction_hours)]
+                ),
+            )
+
+        # Register population, mating, mutation, and selection functions
+        self.toolbox.register(
+            "population", tools.initRepeat, list, self.toolbox.individual
+        )
+        self.toolbox.register("mate", tools.cxTwoPoint)
+        self.toolbox.register("mutate", tools.mutFlipBit, indpb=0.1)
+        self.toolbox.register("select", tools.selTournament, tournsize=3)
+
+    def evaluate_inner(
+        self, individual: List[float], ems: EnergieManagementSystem, start_hour: int
+    ) -> Dict[str, Any]:
+        """
+        Internal evaluation function that simulates the energy management system (EMS)
+        using the provided individual solution.
+        """
+        ems.reset()
+        discharge_hours_bin, eautocharge_hours_float, spuelstart_int = (
+            self.split_individual(individual)
+        )
+        if self.opti_param.get("haushaltsgeraete", 0) > 0:
+            ems.set_haushaltsgeraet_start(spuelstart_int, global_start_hour=start_hour)
+
+        ems.set_akku_discharge_hours(discharge_hours_bin)
+        eautocharge_hours_float[self.prediction_hours - self.fixed_eauto_hours :] = [
+            0.0
+        ] * self.fixed_eauto_hours
+        ems.set_eauto_charge_hours(eautocharge_hours_float)
+        return ems.simuliere(start_hour)
+
+    def evaluate(
+        self,
+        individual: List[float],
+        ems: EnergieManagementSystem,
+        parameter: Dict[str, Any],
+        start_hour: int,
+        worst_case: bool,
+    ) -> Tuple[float]:
+        """
+        Evaluate the fitness of an individual solution based on the simulation results.
+        """
+        try:
+            o = self.evaluate_inner(individual, ems, start_hour)
+        except Exception:
+            return (100000.0,)  # Return a high penalty in case of an exception
+
+        gesamtbilanz = o["Gesamtbilanz_Euro"] * (-1.0 if worst_case else 1.0)
+        discharge_hours_bin, eautocharge_hours_float, _ = self.split_individual(
+            individual
+        )
+        max_ladeleistung = np.max(moegliche_ladestroeme_in_prozent)
+
+        # Penalty for not discharging
+        gesamtbilanz += sum(
+            0.01 for i in range(self.prediction_hours) if discharge_hours_bin[i] == 0.0
+        )
+
+        # Penalty for charging the electric vehicle during restricted hours
+        gesamtbilanz += sum(
+            self.strafe
+            for i in range(
+                self.prediction_hours - self.fixed_eauto_hours, self.prediction_hours
+            )
+            if eautocharge_hours_float[i] != 0.0
+        )
+
+        # Penalty for exceeding maximum charge power
+        gesamtbilanz += sum(
+            self.strafe * 10
+            for ladeleistung in eautocharge_hours_float
+            if ladeleistung > max_ladeleistung
+        )
+
+        # Penalty for not meeting the minimum SOC (State of Charge) requirement
+        if parameter["eauto_min_soc"] - ems.eauto.ladezustand_in_prozent() <= 0.0:
+            gesamtbilanz += sum(
+                self.strafe
+                for ladeleistung in eautocharge_hours_float
+                if ladeleistung != 0.0
+            )
+
+        individual.extra_data = (
+            o["Gesamtbilanz_Euro"],
+            o["Gesamt_Verluste"],
+            parameter["eauto_min_soc"] - ems.eauto.ladezustand_in_prozent(),
+        )
+
+        # Adjust total balance with battery value and penalties for unmet SOC
+        restwert_akku = (
+            ems.akku.aktueller_energieinhalt() * parameter["preis_euro_pro_wh_akku"]
+        )
+        gesamtbilanz += (
+            max(
+                0,
+                (parameter["eauto_min_soc"] - ems.eauto.ladezustand_in_prozent())
+                * self.strafe,
+            )
+            - restwert_akku
+        )
+
+        return (gesamtbilanz,)
+
+    def optimize(
+        self, start_solution: Optional[List[float]] = None
+    ) -> Tuple[Any, Dict[str, List[Any]]]:
+        """Run the optimization process using a genetic algorithm."""
+        population = self.toolbox.population(n=300)
+        hof = tools.HallOfFame(1)
+        stats = tools.Statistics(lambda ind: ind.fitness.values)
+        stats.register("min", np.min)
+
+        if self.verbose:
+            print("Start optimize:", start_solution)
+
+        # Insert the start solution into the population if provided
+        if start_solution not in [None, -1]:
+            for _ in range(3):
+                population.insert(0, creator.Individual(start_solution))
+
+        # Run the evolutionary algorithm
+        algorithms.eaMuPlusLambda(
+            population,
+            self.toolbox,
+            mu=100,
+            lambda_=200,
+            cxpb=0.5,
+            mutpb=0.3,
+            ngen=400,
+            stats=stats,
+            halloffame=hof,
+            verbose=self.verbose,
+        )
+
+        member = {"bilanz": [], "verluste": [], "nebenbedingung": []}
+        for ind in population:
+            if hasattr(ind, "extra_data"):
+                extra_value1, extra_value2, extra_value3 = ind.extra_data
+                member["bilanz"].append(extra_value1)
+                member["verluste"].append(extra_value2)
+                member["nebenbedingung"].append(extra_value3)
+
+        return hof[0], member
+
+    def optimierung_ems(
+        self,
+        parameter: Optional[Dict[str, Any]] = None,
+        start_hour: Optional[int] = None,
+        worst_case: bool = False,
+        startdate: Optional[Any] = None,  # startdate is not used!
+    ) -> Dict[str, Any]:
+        """
+        Perform EMS (Energy Management System) optimization and visualize results.
+        """
+        einspeiseverguetung_euro_pro_wh = np.full(
+            self.prediction_hours, parameter["einspeiseverguetung_euro_pro_wh"]
+        )
+
+        # Initialize PV and EV batteries
+        akku = PVAkku(
+            kapazitaet_wh=parameter["pv_akku_cap"],
+            hours=self.prediction_hours,
+            start_soc_prozent=parameter["pv_soc"],
+            min_soc_prozent=parameter["min_soc_prozent"],
+            max_ladeleistung_w=5000,
+        )
+        akku.set_charge_per_hour(np.full(self.prediction_hours, 1))
+
+        eauto = PVAkku(
+            kapazitaet_wh=parameter["eauto_cap"],
+            hours=self.prediction_hours,
+            lade_effizienz=parameter["eauto_charge_efficiency"],
+            entlade_effizienz=1.0,
+            max_ladeleistung_w=parameter["eauto_charge_power"],
+            start_soc_prozent=parameter["eauto_soc"],
+        )
+        eauto.set_charge_per_hour(np.full(self.prediction_hours, 1))
+
+        # Initialize household appliance if applicable
+        spuelmaschine = (
+            Haushaltsgeraet(
+                hours=self.prediction_hours,
+                verbrauch_wh=parameter["haushaltsgeraet_wh"],
+                dauer_h=parameter["haushaltsgeraet_dauer"],
+            )
+            if parameter["haushaltsgeraet_dauer"] > 0
+            else None
+        )
+
+        # Initialize the inverter and energy management system
+        wr = Wechselrichter(10000, akku)
+        ems = EnergieManagementSystem(
+            gesamtlast=parameter["gesamtlast"],
+            pv_prognose_wh=parameter["pv_forecast"],
+            strompreis_euro_pro_wh=parameter["strompreis_euro_pro_wh"],
+            einspeiseverguetung_euro_pro_wh=einspeiseverguetung_euro_pro_wh,
+            eauto=eauto,
+            haushaltsgeraet=spuelmaschine,
+            wechselrichter=wr,
+        )
+
+        # Setup the DEAP environment and optimization process
+        self.setup_deap_environment(
+            {"haushaltsgeraete": 1 if spuelmaschine else 0}, start_hour
+        )
+        self.toolbox.register(
+            "evaluate",
+            lambda ind: self.evaluate(ind, ems, parameter, start_hour, worst_case),
+        )
+        start_solution, extra_data = self.optimize(parameter["start_solution"])
+
+        # Perform final evaluation on the best solution
+        o = self.evaluate_inner(start_solution, ems, start_hour)
+        discharge_hours_bin, eautocharge_hours_float, spuelstart_int = (
+            self.split_individual(start_solution)
+        )
+
+        # Visualize the results
+        visualisiere_ergebnisse(
+            parameter["gesamtlast"],
+            parameter["pv_forecast"],
+            parameter["strompreis_euro_pro_wh"],
+            o,
+            discharge_hours_bin,
+            eautocharge_hours_float,
+            parameter["temperature_forecast"],
+            start_hour,
+            self.prediction_hours,
+            einspeiseverguetung_euro_pro_wh,
+            extra_data=extra_data,
+        )
+
+        # Return final results as a dictionary
+        return {
+            "discharge_hours_bin": discharge_hours_bin,
+            "eautocharge_hours_float": eautocharge_hours_float,
+            "result": o,
+            "eauto_obj": ems.eauto.to_dict(),
+            "start_solution": start_solution,
+            "spuelstart": spuelstart_int,
+            "simulation_data": o,
+        }

src/akkudoktoreos/class_pv_forecast.py

@@ -0,0 +1,247 @@
+import hashlib
+import json
+import os
+from datetime import datetime
+from pprint import pprint
+
+import numpy as np
+import pandas as pd
+import requests
+from dateutil import parser
+
+
+class ForecastData:
+    def __init__(
+        self,
+        date_time,
+        dc_power,
+        ac_power,
+        windspeed_10m=None,
+        temperature=None,
+        ac_power_measurement=None,
+    ):
+        self.date_time = date_time
+        self.dc_power = dc_power
+        self.ac_power = ac_power
+        self.windspeed_10m = windspeed_10m
+        self.temperature = temperature
+        self.ac_power_measurement = ac_power_measurement
+
+    def get_date_time(self):
+        return self.date_time
+
+    def get_dc_power(self):
+        return self.dc_power
+
+    def ac_power_measurement(self):
+        return self.ac_power_measurement
+
+    def get_ac_power(self):
+        if self.ac_power_measurement is not None:
+            return self.ac_power_measurement
+        else:
+            return self.ac_power
+
+    def get_windspeed_10m(self):
+        return self.windspeed_10m
+
+    def get_temperature(self):
+        return self.temperature
+
+
+class PVForecast:
+    def __init__(self, filepath=None, url=None, cache_dir="cache", prediction_hours=48):
+        self.meta = {}
+        self.forecast_data = []
+        self.cache_dir = cache_dir
+        self.prediction_hours = prediction_hours
+        self.current_measurement = None
+
+        if not os.path.exists(self.cache_dir):
+            os.makedirs(self.cache_dir)
+        if filepath:
+            self.load_data_from_file(filepath)
+        elif url:
+            self.load_data_with_caching(url)
+
+        if len(self.forecast_data) < self.prediction_hours:
+            raise ValueError(
+                f"Die Vorhersage muss mindestens {self.prediction_hours} Stunden umfassen, aber es wurden nur {len(self.forecast_data)} Stunden vorhergesagt."
+            )
+
+    def update_ac_power_measurement(
+        self, date_time=None, ac_power_measurement=None
+    ) -> bool:
+        found = False
+        input_date_hour = date_time.replace(minute=0, second=0, microsecond=0)
+
+        for forecast in self.forecast_data:
+            forecast_date_hour = parser.parse(forecast.date_time).replace(
+                minute=0, second=0, microsecond=0
+            )
+            if forecast_date_hour == input_date_hour:
+                forecast.ac_power_measurement = ac_power_measurement
+                found = True
+                break
+        return found
+
+    def process_data(self, data):
+        self.meta = data.get("meta", {})
+        all_values = data.get("values", [])
+
+        for i in range(
+            len(all_values[0])
+        ):  # Annahme, dass alle Listen gleich lang sind
+            sum_dc_power = sum(values[i]["dcPower"] for values in all_values)
+            sum_ac_power = sum(values[i]["power"] for values in all_values)
+
+            # Zeige die ursprünglichen und berechneten Zeitstempel an
+            original_datetime = all_values[0][i].get("datetime")
+            # print(original_datetime," ",sum_dc_power," ",all_values[0][i]['dcPower'])
+            dt = datetime.strptime(original_datetime, "%Y-%m-%dT%H:%M:%S.%f%z")
+            dt = dt.replace(tzinfo=None)
+            # iso_datetime = parser.parse(original_datetime).isoformat()  # Konvertiere zu ISO-Format
+            # print()
+            # Optional: 2 Stunden abziehen, um die Zeitanpassung zu testen
+            # adjusted_datetime = parser.parse(original_datetime) - timedelta(hours=2)
+            # print(f"Angepasste Zeitstempel: {adjusted_datetime.isoformat()}")
+
+            forecast = ForecastData(
+                date_time=dt,  # Verwende angepassten Zeitstempel
+                dc_power=sum_dc_power,
+                ac_power=sum_ac_power,
+                windspeed_10m=all_values[0][i].get("windspeed_10m"),
+                temperature=all_values[0][i].get("temperature"),
+            )
+
+            self.forecast_data.append(forecast)
+
+    def load_data_from_file(self, filepath):
+        with open(filepath, "r") as file:
+            data = json.load(file)
+            self.process_data(data)
+
+    def load_data_from_url(self, url):
+        response = requests.get(url)
+        if response.status_code == 200:
+            data = response.json()
+            pprint(data)
+            self.process_data(data)
+        else:
+            print(
+                f"Failed to load data from {url}. Status Code: {response.status_code}"
+            )
+            self.load_data_from_url(url)
+
+    def load_data_with_caching(self, url):
+        date = datetime.now().strftime("%Y-%m-%d")
+
+        cache_file = os.path.join(
+            self.cache_dir, self.generate_cache_filename(url, date)
+        )
+        if os.path.exists(cache_file):
+            with open(cache_file, "r") as file:
+                data = json.load(file)
+                print("Loading data from cache.")
+        else:
+            response = requests.get(url)
+            if response.status_code == 200:
+                data = response.json()
+                with open(cache_file, "w") as file:
+                    json.dump(data, file)
+                print("Data fetched from URL and cached.")
+            else:
+                print(
+                    f"Failed to load data from {url}. Status Code: {response.status_code}"
+                )
+                return
+        self.process_data(data)
+
+    def generate_cache_filename(self, url, date):
+        cache_key = hashlib.sha256(f"{url}{date}".encode("utf-8")).hexdigest()
+        return f"cache_{cache_key}.json"
+
+    def get_forecast_data(self):
+        return self.forecast_data
+
+    def get_temperature_forecast_for_date(self, input_date_str):
+        input_date = datetime.strptime(input_date_str, "%Y-%m-%d")
+        daily_forecast_obj = [
+            data
+            for data in self.forecast_data
+            if parser.parse(data.get_date_time()).date() == input_date.date()
+        ]
+        daily_forecast = []
+        for d in daily_forecast_obj:
+            daily_forecast.append(d.get_temperature())
+
+        return np.array(daily_forecast)
+
+    def get_pv_forecast_for_date_range(self, start_date_str, end_date_str):
+        start_date = datetime.strptime(start_date_str, "%Y-%m-%d").date()
+        end_date = datetime.strptime(end_date_str, "%Y-%m-%d").date()
+        date_range_forecast = []
+
+        for data in self.forecast_data:
+            data_date = (
+                data.get_date_time().date()
+            )  # parser.parse(data.get_date_time()).date()
+            if start_date <= data_date <= end_date:
+                date_range_forecast.append(data)
+                print(data.get_date_time(), " ", data.get_ac_power())
+
+        ac_power_forecast = np.array(
+            [data.get_ac_power() for data in date_range_forecast]
+        )
+
+        return np.array(ac_power_forecast)[: self.prediction_hours]
+
+    def get_temperature_for_date_range(self, start_date_str, end_date_str):
+        start_date = datetime.strptime(start_date_str, "%Y-%m-%d").date()
+        end_date = datetime.strptime(end_date_str, "%Y-%m-%d").date()
+        date_range_forecast = []
+
+        for data in self.forecast_data:
+            data_date = data.get_date_time().date()
+            if start_date <= data_date <= end_date:
+                date_range_forecast.append(data)
+
+        temperature_forecast = [data.get_temperature() for data in date_range_forecast]
+        return np.array(temperature_forecast)[: self.prediction_hours]
+
+    def get_forecast_dataframe(self):
+        # Wandelt die Vorhersagedaten in ein Pandas DataFrame um
+        data = [
+            {
+                "date_time": f.get_date_time(),
+                "dc_power": f.get_dc_power(),
+                "ac_power": f.get_ac_power(),
+                "windspeed_10m": f.get_windspeed_10m(),
+                "temperature": f.get_temperature(),
+            }
+            for f in self.forecast_data
+        ]
+
+        # Erstelle ein DataFrame
+        df = pd.DataFrame(data)
+        return df
+
+    def print_ac_power_and_measurement(self):
+        """Druckt die DC-Leistung und den Messwert für jede Stunde."""
+        for forecast in self.forecast_data:
+            date_time = forecast.date_time
+            print(
+                f"Zeit: {date_time}, DC: {forecast.dc_power}, AC: {forecast.ac_power}, Messwert: {forecast.ac_power_measurement}, AC GET: {forecast.get_ac_power()}"
+            )
+
+
+# Beispiel für die Verwendung der Klasse
+if __name__ == "__main__":
+    forecast = PVForecast(
+        prediction_hours=24,
+        url="https://api.akkudoktor.net/forecast?lat=52.52&lon=13.405&power=5000&azimuth=-10&tilt=7&powerInvertor=10000&horizont=20,27,22,20&power=4800&azimuth=-90&tilt=7&powerInvertor=10000&horizont=30,30,30,50&power=1400&azimuth=-40&tilt=60&powerInvertor=2000&horizont=60,30,0,30&power=1600&azimuth=5&tilt=45&powerInvertor=1400&horizont=45,25,30,60&past_days=5&cellCoEff=-0.36&inverterEfficiency=0.8&albedo=0.25&timezone=Europe%2FBerlin&hourly=relativehumidity_2m%2Cwindspeed_10m",
+    )
+    forecast.update_ac_power_measurement(
+        date_time=datetime.now(), ac_power_measurement=1000
+    )
+    forecast.print_ac_power_and_measurement()

src/akkudoktoreos/class_soc_calc.py

@@ -0,0 +1,331 @@
+from datetime import datetime, timedelta
+
+import mariadb
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+class BatteryDataProcessor:
+    def __init__(
+        self,
+        config,
+        voltage_high_threshold,
+        voltage_low_threshold,
+        current_low_threshold,
+        gap,
+        battery_capacity_ah,
+    ):
+        self.config = config
+        self.voltage_high_threshold = voltage_high_threshold
+        self.voltage_low_threshold = voltage_low_threshold
+        self.current_low_threshold = current_low_threshold
+        self.gap = gap
+        self.battery_capacity_ah = battery_capacity_ah
+        self.conn = None
+        self.data = None
+
+    def connect_db(self):
+        self.conn = mariadb.connect(**self.config)
+        self.cursor = self.conn.cursor()
+
+    def disconnect_db(self):
+        if self.conn:
+            self.cursor.close()
+            self.conn.close()
+
+    def fetch_data(self, start_time):
+        query = """
+        SELECT timestamp, data, topic
+        FROM pip
+        WHERE timestamp >= %s AND (topic = 'battery_current' OR topic = 'battery_voltage')
+        ORDER BY timestamp
+        """
+        self.cursor.execute(query, (start_time,))
+        rows = self.cursor.fetchall()
+        self.data = pd.DataFrame(rows, columns=["timestamp", "data", "topic"])
+        self.data["timestamp"] = pd.to_datetime(self.data["timestamp"])
+        self.data["data"] = self.data["data"].astype(float)
+
+    def process_data(self):
+        self.data.drop_duplicates(subset=["timestamp", "topic"], inplace=True)
+
+        data_pivot = self.data.pivot(index="timestamp", columns="topic", values="data")
+        data_pivot = data_pivot.resample("1T").mean().interpolate()
+        data_pivot.columns.name = None
+        data_pivot.reset_index(inplace=True)
+        self.data = data_pivot
+
+    def group_points(self, df):
+        df = df.sort_values("timestamp")
+        groups = []
+        group = []
+        last_time = None
+
+        for _, row in df.iterrows():
+            if last_time is None or (row["timestamp"] - last_time) <= pd.Timedelta(
+                minutes=self.gap
+            ):
+                group.append(row)
+            else:
+                groups.append(group)
+                group = [row]
+            last_time = row["timestamp"]
+
+        if group:
+            groups.append(group)
+
+        last_points = [group[-1] for group in groups]
+        return last_points
+
+    def find_soc_points(self):
+        condition_soc_100 = (
+            self.data["battery_voltage"] >= self.voltage_high_threshold
+        ) & (self.data["battery_current"].abs() <= self.current_low_threshold)
+        condition_soc_0 = (
+            self.data["battery_voltage"] <= self.voltage_low_threshold
+        ) & (self.data["battery_current"].abs() <= self.current_low_threshold)
+
+        times_soc_100_all = self.data[condition_soc_100][
+            ["timestamp", "battery_voltage", "battery_current"]
+        ]
+        times_soc_0_all = self.data[condition_soc_0][
+            ["timestamp", "battery_voltage", "battery_current"]
+        ]
+
+        last_points_100 = self.group_points(times_soc_100_all)
+        last_points_0 = self.group_points(times_soc_0_all)
+
+        last_points_100_df = pd.DataFrame(last_points_100)
+        last_points_0_df = pd.DataFrame(last_points_0)
+
+        return last_points_100_df, last_points_0_df
+
+    def calculate_resetting_soc(self, last_points_100_df, last_points_0_df):
+        soc_values = []
+        integration_results = []
+        reset_points = pd.concat([last_points_100_df, last_points_0_df]).sort_values(
+            "timestamp"
+        )
+
+        # Initialisieren der SoC-Liste
+        self.data["calculated_soc"] = np.nan
+
+        for i in range(len(reset_points)):
+            start_point = reset_points.iloc[i]
+            if i < len(reset_points) - 1:
+                end_point = reset_points.iloc[i + 1]
+            else:
+                end_point = self.data.iloc[
+                    -1
+                ]  # Verwenden des letzten Datensatzes als Endpunkt
+
+            if start_point["timestamp"] in last_points_100_df["timestamp"].values:
+                initial_soc = 100
+            elif start_point["timestamp"] in last_points_0_df["timestamp"].values:
+                initial_soc = 0
+
+            cut_data = self.data[
+                (self.data["timestamp"] >= start_point["timestamp"])
+                & (self.data["timestamp"] <= end_point["timestamp"])
+            ].copy()
+            cut_data["time_diff_hours"] = (
+                cut_data["timestamp"].diff().dt.total_seconds() / 3600
+            )
+            cut_data.dropna(subset=["time_diff_hours"], inplace=True)
+
+            calculated_soc = initial_soc
+            calculated_soc_list = [calculated_soc]
+            integrated_current = 0
+
+            for j in range(1, len(cut_data)):
+                current = cut_data.iloc[j]["battery_current"]
+                delta_t = cut_data.iloc[j]["time_diff_hours"]
+                delta_soc = (
+                    (current * delta_t) / self.battery_capacity_ah * 100
+                )  # Convert to percentage
+
+                calculated_soc += delta_soc
+                calculated_soc = min(max(calculated_soc, 0), 100)  # Clip to 0-100%
+                calculated_soc_list.append(calculated_soc)
+
+                # Integration des Stroms aufaddieren
+                integrated_current += current * delta_t
+
+            cut_data["calculated_soc"] = calculated_soc_list
+            soc_values.append(cut_data[["timestamp", "calculated_soc"]])
+
+            integration_results.append(
+                {
+                    "start_time": start_point["timestamp"],
+                    "end_time": end_point["timestamp"],
+                    "integrated_current": integrated_current,
+                    "start_soc": initial_soc,
+                    "end_soc": calculated_soc_list[-1],
+                }
+            )
+
+        soc_df = (
+            pd.concat(soc_values)
+            .drop_duplicates(subset=["timestamp"])
+            .reset_index(drop=True)
+        )
+        return soc_df, integration_results
+
+    def calculate_soh(self, integration_results):
+        soh_values = []
+
+        for result in integration_results:
+            delta_soc = abs(
+                result["start_soc"] - result["end_soc"]
+            )  # Use the actual change in SoC
+            if delta_soc > 0:  # Avoid division by zero
+                effective_capacity_ah = result["integrated_current"]
+                soh = (effective_capacity_ah / self.battery_capacity_ah) * 100
+                soh_values.append({"timestamp": result["end_time"], "soh": soh})
+
+        soh_df = pd.DataFrame(soh_values)
+        return soh_df
+
+    def delete_existing_soc_entries(self, soc_df):
+        delete_query = """
+        DELETE FROM pip
+        WHERE timestamp = %s AND topic = 'calculated_soc'
+        """
+        timestamps = [
+            (row["timestamp"].strftime("%Y-%m-%d %H:%M:%S"),)
+            for _, row in soc_df.iterrows()
+            if pd.notna(row["timestamp"])
+        ]
+
+        self.cursor.executemany(delete_query, timestamps)
+        self.conn.commit()
+
+    def update_database_with_soc(self, soc_df):
+        # Löschen der vorhandenen Einträge mit demselben Topic und Datum
+        self.delete_existing_soc_entries(soc_df)
+
+        # Resample `soc_df` auf 5-Minuten-Intervalle und berechnen des Mittelwerts
+        soc_df.set_index("timestamp", inplace=True)
+        soc_df_resampled = soc_df.resample("5T").mean().dropna().reset_index()
+        # soc_df_resampled['timestamp'] = soc_df_resampled['timestamp'].apply(lambda x: x.strftime('%Y-%m-%d %H:%M:%S'))
+        print(soc_df_resampled)
+
+        # Einfügen der berechneten SoC-Werte in die Datenbank
+        insert_query = """
+        INSERT INTO pip (timestamp, data, topic)
+        VALUES (%s, %s, 'calculated_soc')
+        """
+        for _, row in soc_df_resampled.iterrows():
+            print(row)
+            print(row["timestamp"])
+            record = (
+                row["timestamp"].strftime("%Y-%m-%d %H:%M:%S"),
+                row["calculated_soc"],
+            )
+            try:
+                self.cursor.execute(insert_query, record)
+            except mariadb.OperationalError as e:
+                print(f"Error inserting record {record}: {e}")
+
+        self.conn.commit()
+
+    def plot_data(self, last_points_100_df, last_points_0_df, soc_df):
+        plt.figure(figsize=(14, 10))
+
+        plt.subplot(4, 1, 1)
+        plt.plot(
+            self.data["timestamp"],
+            self.data["battery_voltage"],
+            label="Battery Voltage",
+            color="blue",
+        )
+        plt.scatter(
+            last_points_100_df["timestamp"],
+            last_points_100_df["battery_voltage"],
+            color="green",
+            marker="o",
+            label="100% SoC Points",
+        )
+        # plt.scatter(last_points_0_df['timestamp'], last_points_0_df['battery_voltage'], color='red', marker='x', label='0% SoC Points')
+        plt.xlabel("Timestamp")
+        plt.ylabel("Voltage (V)")
+        plt.legend()
+        plt.title("Battery Voltage over Time")
+
+        plt.subplot(4, 1, 2)
+        plt.plot(
+            self.data["timestamp"],
+            self.data["battery_current"],
+            label="Battery Current",
+            color="orange",
+        )
+        plt.scatter(
+            last_points_100_df["timestamp"],
+            last_points_100_df["battery_current"],
+            color="green",
+            marker="o",
+            label="100% SoC Points",
+        )
+        # plt.scatter(last_points_0_df['timestamp'], last_points_0_df['battery_current'], color='red', marker='x', label='0% SoC Points')
+        plt.xlabel("Timestamp")
+        plt.ylabel("Current (A)")
+        plt.legend()
+        plt.title("Battery Current over Time")
+
+        plt.subplot(4, 1, 3)
+        plt.plot(
+            soc_df["timestamp"], soc_df["calculated_soc"], label="SoC", color="purple"
+        )
+        plt.xlabel("Timestamp")
+        plt.ylabel("SoC (%)")
+        plt.legend()
+        plt.title("State of Charge (SoC) over Time")
+
+        # plt.subplot(4, 1, 4)
+        # plt.plot(soh_df['timestamp'], soh_df['soh'], label='SoH', color='brown')
+        # plt.xlabel('Timestamp')
+        # plt.ylabel('SoH (%)')
+        # plt.legend()
+        # plt.title('State of Health (SoH) over Time')
+
+        plt.tight_layout()
+        plt.show()
+
+
+if __name__ == "__main__":
+    # MariaDB Verbindungsdetails
+    config = {}
+
+    # Parameter festlegen
+    voltage_high_threshold = 55.4  # 100% SoC
+    voltage_low_threshold = 46.5  # 0% SoC
+    current_low_threshold = 2  # Niedriger Strom für beide Zustände
+    gap = 30  # Zeitlücke in Minuten zum  Gruppieren von Maxima/Minima
+    bat_capacity = 33 * 1000 / 48
+
+    # Zeitpunkt X definieren
+    zeitpunkt_x = (datetime.now() - timedelta(weeks=100)).strftime("%Y-%m-%d %H:%M:%S")
+
+    # BatteryDataProcessor instanziieren und verwenden
+    processor = BatteryDataProcessor(
+        config,
+        voltage_high_threshold,
+        voltage_low_threshold,
+        current_low_threshold,
+        gap,
+        bat_capacity,
+    )
+    processor.connect_db()
+    processor.fetch_data(zeitpunkt_x)
+    processor.process_data()
+    last_points_100_df, last_points_0_df = processor.find_soc_points()
+    soc_df, integration_results = processor.calculate_resetting_soc(
+        last_points_100_df, last_points_0_df
+    )
+    # soh_df = processor.calculate_soh(integration_results)
+    processor.update_database_with_soc(soc_df)
+
+    processor.plot_data(last_points_100_df, last_points_0_df, soc_df)
+
+    processor.disconnect_db()

src/akkudoktoreos/class_sommerzeit.py

@@ -0,0 +1,28 @@
+import datetime
+
+import pytz
+
+
+def ist_dst_wechsel(tag, timezone="Europe/Berlin"):
+    """Checks if Daylight Saving Time (DST) starts or ends on a given day."""
+    tz = pytz.timezone(timezone)
+    # Get the current day and the next day
+    current_day = datetime.datetime(tag.year, tag.month, tag.day)
+    next_day = current_day + datetime.timedelta(days=1)
+
+    # Localize the days in the given timezone
+    current_day_localized = tz.localize(current_day, is_dst=None)
+    next_day_localized = tz.localize(next_day, is_dst=None)
+
+    # Check if the UTC offsets are different (indicating a DST change)
+    dst_change = current_day_localized.dst() != next_day_localized.dst()
+
+    return dst_change
+
+
+# # Example usage
+# start_date = datetime.datetime(2024, 3, 31)  # Date of the DST change
+# if ist_dst_wechsel(start_date):
+#     prediction_hours = 23  # Adjust to 23 hours for DST change days
+# else:
+#     prediction_hours = 24  # Default value for days without DST change

src/akkudoktoreos/class_strompreis.py

@@ -0,0 +1,141 @@
+import hashlib
+import json
+import os
+from datetime import datetime, timedelta
+
+import numpy as np
+import pytz
+import requests
+
+# Example: Converting a UTC timestamp to local time
+utc_time = datetime.strptime("2024-03-28T01:00:00.000Z", "%Y-%m-%dT%H:%M:%S.%fZ")
+utc_time = utc_time.replace(tzinfo=pytz.utc)
+
+# Replace 'Europe/Berlin' with your own timezone
+local_time = utc_time.astimezone(pytz.timezone("Europe/Berlin"))
+print(local_time)
+
+
+def repeat_to_shape(array, target_shape):
+    # Check if the array fits the target shape
+    if len(target_shape) != array.ndim:
+        raise ValueError(
+            "Array and target shape must have the same number of dimensions"
+        )
+
+    # Number of repetitions per dimension
+    repeats = tuple(target_shape[i] // array.shape[i] for i in range(array.ndim))
+
+    # Use np.tile to expand the array
+    expanded_array = np.tile(array, repeats)
+    return expanded_array
+
+
+class HourlyElectricityPriceForecast:
+    def __init__(
+        self, source, cache_dir="cache", charges=0.000228, prediction_hours=24
+    ):  # 228
+        self.cache_dir = cache_dir
+        os.makedirs(self.cache_dir, exist_ok=True)
+        self.cache_time_file = os.path.join(self.cache_dir, "cache_timestamp.txt")
+        self.prices = self.load_data(source)
+        self.charges = charges
+        self.prediction_hours = prediction_hours
+
+    def load_data(self, source):
+        cache_filename = self.get_cache_filename(source)
+        if source.startswith("http"):
+            if os.path.exists(cache_filename) and not self.is_cache_expired():
+                print("Loading data from cache...")
+                with open(cache_filename, "r") as file:
+                    json_data = json.load(file)
+            else:
+                print("Loading data from the URL...")
+                response = requests.get(source)
+                if response.status_code == 200:
+                    json_data = response.json()
+                    with open(cache_filename, "w") as file:
+                        json.dump(json_data, file)
+                    self.update_cache_timestamp()
+                else:
+                    raise Exception(f"Error fetching data: {response.status_code}")
+        else:
+            with open(source, "r") as file:
+                json_data = json.load(file)
+        return json_data["values"]
+
+    def get_cache_filename(self, url):
+        hash_object = hashlib.sha256(url.encode())
+        hex_dig = hash_object.hexdigest()
+        return os.path.join(self.cache_dir, f"cache_{hex_dig}.json")
+
+    def is_cache_expired(self):
+        if not os.path.exists(self.cache_time_file):
+            return True
+        with open(self.cache_time_file, "r") as file:
+            timestamp_str = file.read()
+            last_cache_time = datetime.strptime(timestamp_str, "%Y-%m-%d %H:%M:%S")
+        return datetime.now() - last_cache_time > timedelta(hours=1)
+
+    def update_cache_timestamp(self):
+        with open(self.cache_time_file, "w") as file:
+            file.write(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
+
+    def get_price_for_date(self, date_str):
+        """Returns all prices for the specified date, including the price from 00:00 of the previous day."""
+        # Convert date string to datetime object
+        date_obj = datetime.strptime(date_str, "%Y-%m-%d")
+
+        # Calculate the previous day
+        previous_day = date_obj - timedelta(days=1)
+        previous_day_str = previous_day.strftime("%Y-%m-%d")
+
+        # Extract the price from 00:00 of the previous day
+        last_price_of_previous_day = [
+            entry["marketpriceEurocentPerKWh"] + self.charges
+            for entry in self.prices
+            if previous_day_str in entry["end"]
+        ][-1]
+
+        # Extract all prices for the specified date
+        date_prices = [
+            entry["marketpriceEurocentPerKWh"] + self.charges
+            for entry in self.prices
+            if date_str in entry["end"]
+        ]
+        print(f"getPrice: {len(date_prices)}")
+
+        # Add the last price of the previous day at the start of the list
+        if len(date_prices) == 23:
+            date_prices.insert(0, last_price_of_previous_day)
+
+        return np.array(date_prices) / (1000.0 * 100.0) + self.charges
+
+    def get_price_for_daterange(self, start_date_str, end_date_str):
+        """Returns all prices between the start and end dates."""
+        print(start_date_str)
+        print(end_date_str)
+        start_date_utc = datetime.strptime(start_date_str, "%Y-%m-%d").replace(
+            tzinfo=pytz.utc
+        )
+        end_date_utc = datetime.strptime(end_date_str, "%Y-%m-%d").replace(
+            tzinfo=pytz.utc
+        )
+        start_date = start_date_utc.astimezone(pytz.timezone("Europe/Berlin"))
+        end_date = end_date_utc.astimezone(pytz.timezone("Europe/Berlin"))
+
+        price_list = []
+
+        while start_date < end_date:
+            date_str = start_date.strftime("%Y-%m-%d")
+            daily_prices = self.get_price_for_date(date_str)
+
+            if daily_prices.size == 24:
+                price_list.extend(daily_prices)
+            start_date += timedelta(days=1)
+
+        # If prediction hours are greater than 0, reshape the price list
+        if self.prediction_hours > 0:
+            price_list = repeat_to_shape(np.array(price_list), (self.prediction_hours,))
+
+        return price_list

src/akkudoktoreos/config.py

@@ -0,0 +1,34 @@
+from datetime import datetime, timedelta
+
+prediction_hours = 48
+optimization_hours = 24
+strafe = 10
+moegliche_ladestroeme_in_prozent = [
+    0.0,
+    6.0 / 16.0,
+    7.0 / 16.0,
+    8.0 / 16.0,
+    9.0 / 16.0,
+    10.0 / 16.0,
+    11.0 / 16.0,
+    12.0 / 16.0,
+    13.0 / 16.0,
+    14.0 / 16.0,
+    15.0 / 16.0,
+    1.0,
+]
+
+
+def get_start_enddate(prediction_hours=48, startdate=None):
+    ############
+    # Parameter
+    ############
+    if startdate is None:
+        date = (datetime.now().date() + timedelta(hours=prediction_hours)).strftime(
+            "%Y-%m-%d"
+        )
+        date_now = datetime.now().strftime("%Y-%m-%d")
+    else:
+        date = (startdate + timedelta(hours=prediction_hours)).strftime("%Y-%m-%d")
+        date_now = startdate.strftime("%Y-%m-%d")
+    return date_now, date

src/akkudoktoreos/heatpump.py

@@ -0,0 +1,142 @@
+import logging
+from typing import List, Sequence
+
+
+class Heatpump:
+    MAX_HEATOUTPUT = 5000
+    """Maximum heating power in watts"""
+
+    BASE_HEATPOWER = 235.0
+    """Base heating power value"""
+
+    TEMPERATURE_COEFFICIENT = -11.645
+    """Coefficient for temperature"""
+
+    COP_BASE = 3.0
+    """Base COP value"""
+
+    COP_COEFFICIENT = 0.1
+    """COP increase per degree"""
+
+    def __init__(self, max_heat_output, prediction_hours):
+        self.max_heat_output = max_heat_output
+        self.prediction_hours = prediction_hours
+        self.log = logging.getLogger(__name__)
+
+    def __check_outside_temperature_range__(self, temp_celsius: float) -> bool:
+        """Check if temperature is in valid range between -100 and 100 degree Celsius.
+
+        Args:
+            temp_celsius: Temperature in degree Celsius
+
+        Returns:
+            bool: True if in range
+        """
+        return temp_celsius > -100 and temp_celsius < 100
+
+    def calculate_cop(self, outside_temperature_celsius: float) -> float:
+        """Calculate the coefficient of performance (COP) based on outside temperature. Supported
+        temperate range -100 degree Celsius to 100 degree Celsius.
+
+        Args:
+            outside_temperature_celsius: Outside temperature in degree Celsius
+
+        Raise:
+            ValueError: If outside temperature isn't range.
+
+        Return:
+            cop: Calculated COP based on temperature
+        """
+        # TODO: Support for other temperature units (e.g Fahrenheit, Kelvin)
+        # Check for sensible temperature values
+        if self.__check_outside_temperature_range__(outside_temperature_celsius):
+            cop = self.COP_BASE + (outside_temperature_celsius * self.COP_COEFFICIENT)
+            return max(cop, 1)
+        else:
+            err_msg = f"Outside temperature '{outside_temperature_celsius}' not in range (min: -100 Celsius, max: 100 Celsius) "
+            self.log.error(err_msg)
+            raise ValueError(err_msg)
+
+    def calculate_heating_output(self, outside_temperature_celsius: float) -> float:
+        """Calculate the heating output in Watts based on outside temperature in degree Celsius.
+        Temperature range must be between -100 and 100 degree Celsius.
+
+        Args:
+            outside_temperature_celsius: Outside temperature in degree Celsius
+
+        Raises:
+            ValueError: Raised if outside temperature isn't in described range.
+
+        Returns:
+            heating output: Calculated heating output in Watts.
+        """
+        if self.__check_outside_temperature_range__(outside_temperature_celsius):
+            heat_output = (
+                (
+                    self.BASE_HEATPOWER
+                    + outside_temperature_celsius * self.TEMPERATURE_COEFFICIENT
+                )
+                * 1000
+            ) / 24.0
+            return min(self.max_heat_output, heat_output)
+        else:
+            err_msg = f"Outside temperature '{outside_temperature_celsius}' not in range (min: -100 Celsius, max: 100 Celsius) "
+            self.log.error(err_msg)
+            raise ValueError(err_msg)
+
+    def calculate_heat_power(self, outside_temperature_celsius: float) -> float:
+        """Calculate electrical power based on outside temperature (degree Celsius).
+
+        Args:
+            outside_temperature_celsius: Temperature in range -100 to 100 degree Celsius.
+
+        Raises:
+            ValueError: Raised if temperature isn't in described range
+
+        Returns:
+            power: Calculated electrical power in Watt.
+        """
+        if self.__check_outside_temperature_range__(outside_temperature_celsius):
+            return (
+                1164
+                - 77.8 * outside_temperature_celsius
+                + 1.62 * outside_temperature_celsius**2.0
+            )
+        else:
+            err_msg = f"Outside temperature '{outside_temperature_celsius}' not in range (min: -100 Celsius, max: 100 Celsius) "
+            self.log.error(err_msg)
+            raise ValueError(err_msg)
+
+    def simulate_24h(self, temperatures: Sequence[float]) -> List[float]:
+        """Simulate power data for 24 hours based on provided temperatures."""
+        power_data: List[float] = []
+
+        if len(temperatures) != self.prediction_hours:
+            raise ValueError(
+                f"The temperature array must contain exactly {self.prediction_hours} entries, one for each hour of the day."
+            )
+
+        for temp in temperatures:
+            power = self.calculate_heat_power(temp)
+            power_data.append(power)
+        return power_data
+
+
+# Example usage of the class
+if __name__ == "__main__":
+    max_heizleistung = 5000  # 5 kW heating power
+    start_innentemperatur = 15  # Initial indoor temperature
+    isolationseffizienz = 0.8  # Insulation efficiency
+    gewuenschte_innentemperatur = 20  # Desired indoor temperature
+    wp = Heatpump(max_heizleistung, 24)  # Initialize heat pump with prediction hours
+
+    # Print COP for various outside temperatures
+    print(wp.calculate_cop(-10), " ", wp.calculate_cop(0), " ", wp.calculate_cop(10))
+
+    # 24 hours of outside temperatures (example values)
+    temperaturen = [ 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -5, -2, 5, ]  # fmt: skip
+
+    # Calculate the 24-hour power data
+    leistungsdaten = wp.simulate_24h(temperaturen)
+
+    print(leistungsdaten)

src/akkudoktoreos/visualize.py

@@ -0,0 +1,292 @@
+import datetime
+
+# Set the backend for matplotlib to Agg
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.backends.backend_pdf import PdfPages
+
+from modules.class_sommerzeit import ist_dst_wechsel
+
+matplotlib.use("Agg")
+
+
+def visualisiere_ergebnisse(
+    gesamtlast,
+    pv_forecast,
+    strompreise,
+    ergebnisse,
+    discharge_hours,
+    laden_moeglich,
+    temperature,
+    start_hour,
+    prediction_hours,
+    einspeiseverguetung_euro_pro_wh,
+    filename="visualization_results.pdf",
+    extra_data=None,
+):
+    #####################
+    # 24-hour visualization
+    #####################
+    with PdfPages(filename) as pdf:
+        # Load and PV generation
+        plt.figure(figsize=(14, 14))
+        plt.subplot(3, 3, 1)
+        hours = np.arange(0, prediction_hours)
+
+        gesamtlast_array = np.array(gesamtlast)
+        # Plot individual loads
+        plt.plot(hours, gesamtlast_array, label="Load (Wh)", marker="o")
+
+        # Calculate and plot total load
+        plt.plot(
+            hours,
+            gesamtlast_array,
+            label="Total Load (Wh)",
+            marker="o",
+            linewidth=2,
+            linestyle="--",
+        )
+        plt.xlabel("Hour")
+        plt.ylabel("Load (Wh)")
+        plt.title("Load Profiles")
+        plt.grid(True)
+        plt.legend()
+
+        # Electricity prices
+        hours_p = np.arange(0, len(strompreise))
+        plt.subplot(3, 2, 2)
+        plt.plot(
+            hours_p,
+            strompreise,
+            label="Electricity Price (€/Wh)",
+            color="purple",
+            marker="s",
+        )
+        plt.title("Electricity Prices")
+        plt.xlabel("Hour of the Day")
+        plt.ylabel("Price (€/Wh)")
+        plt.legend()
+        plt.grid(True)
+
+        # PV forecast
+        plt.subplot(3, 2, 3)
+        plt.plot(hours, pv_forecast, label="PV Generation (Wh)", marker="x")
+        plt.title("PV Forecast")
+        plt.xlabel("Hour of the Day")
+        plt.ylabel("Wh")
+        plt.legend()
+        plt.grid(True)
+
+        # Feed-in remuneration
+        plt.subplot(3, 2, 4)
+        plt.plot(
+            hours,
+            einspeiseverguetung_euro_pro_wh,
+            label="Remuneration (€/Wh)",
+            marker="x",
+        )
+        plt.title("Remuneration")
+        plt.xlabel("Hour of the Day")
+        plt.ylabel("€/Wh")
+        plt.legend()
+        plt.grid(True)
+
+        # Temperature forecast
+        plt.subplot(3, 2, 5)
+        plt.title("Temperature Forecast (°C)")
+        plt.plot(hours, temperature, label="Temperature (°C)", marker="x")
+        plt.xlabel("Hour of the Day")
+        plt.ylabel("°C")
+        plt.legend()
+        plt.grid(True)
+
+        pdf.savefig()  # Save the current figure state to the PDF
+        plt.close()  # Close the current figure to free up memory
+
+        #####################
+        # Start hour visualization
+        #####################
+
+        plt.figure(figsize=(14, 10))
+
+        if ist_dst_wechsel(datetime.datetime.now()):
+            hours = np.arange(start_hour, prediction_hours - 1)
+        else:
+            hours = np.arange(start_hour, prediction_hours)
+
+        # Energy flow, grid feed-in, and grid consumption
+        plt.subplot(3, 2, 1)
+        plt.plot(hours, ergebnisse["Last_Wh_pro_Stunde"], label="Load (Wh)", marker="o")
+        plt.plot(
+            hours,
+            ergebnisse["Haushaltsgeraet_wh_pro_stunde"],
+            label="Household Device (Wh)",
+            marker="o",
+        )
+        plt.plot(
+            hours,
+            ergebnisse["Netzeinspeisung_Wh_pro_Stunde"],
+            label="Grid Feed-in (Wh)",
+            marker="x",
+        )
+        plt.plot(
+            hours,
+            ergebnisse["Netzbezug_Wh_pro_Stunde"],
+            label="Grid Consumption (Wh)",
+            marker="^",
+        )
+        plt.plot(
+            hours, ergebnisse["Verluste_Pro_Stunde"], label="Losses (Wh)", marker="^"
+        )
+        plt.title("Energy Flow per Hour")
+        plt.xlabel("Hour")
+        plt.ylabel("Energy (Wh)")
+        plt.legend()
+
+        # State of charge for batteries
+        plt.subplot(3, 2, 2)
+        plt.plot(
+            hours, ergebnisse["akku_soc_pro_stunde"], label="PV Battery (%)", marker="x"
+        )
+        plt.plot(
+            hours,
+            ergebnisse["E-Auto_SoC_pro_Stunde"],
+            label="E-Car Battery (%)",
+            marker="x",
+        )
+        plt.legend(
+            loc="upper left", bbox_to_anchor=(1, 1)
+        )  # Place legend outside the plot
+        plt.grid(True, which="both", axis="x")  # Grid for every hour
+
+        ax1 = plt.subplot(3, 2, 3)
+        for hour, value in enumerate(discharge_hours):
+            ax1.axvspan(
+                hour,
+                hour + 1,
+                color="red",
+                ymax=value,
+                alpha=0.3,
+                label="Discharge Possibility" if hour == 0 else "",
+            )
+        for hour, value in enumerate(laden_moeglich):
+            ax1.axvspan(
+                hour,
+                hour + 1,
+                color="green",
+                ymax=value,
+                alpha=0.3,
+                label="Charging Possibility" if hour == 0 else "",
+            )
+        ax1.legend(loc="upper left")
+        ax1.set_xlim(0, prediction_hours)
+
+        pdf.savefig()  # Save the current figure state to the PDF
+        plt.close()  # Close the current figure to free up memory
+
+        # Financial overview
+        fig, axs = plt.subplots(1, 2, figsize=(14, 10))  # Create a 1x2 grid of subplots
+        total_costs = ergebnisse["Gesamtkosten_Euro"]
+        total_revenue = ergebnisse["Gesamteinnahmen_Euro"]
+        total_balance = ergebnisse["Gesamtbilanz_Euro"]
+        losses = ergebnisse["Gesamt_Verluste"]
+
+        # Costs and revenues per hour on the first axis (axs[0])
+        axs[0].plot(
+            hours,
+            ergebnisse["Kosten_Euro_pro_Stunde"],
+            label="Costs (Euro)",
+            marker="o",
+            color="red",
+        )
+        axs[0].plot(
+            hours,
+            ergebnisse["Einnahmen_Euro_pro_Stunde"],
+            label="Revenue (Euro)",
+            marker="x",
+            color="green",
+        )
+        axs[0].set_title("Financial Balance per Hour")
+        axs[0].set_xlabel("Hour")
+        axs[0].set_ylabel("Euro")
+        axs[0].legend()
+        axs[0].grid(True)
+
+        # Summary of finances on the second axis (axs[1])
+        labels = ["Total Costs [€]", "Total Revenue [€]", "Total Balance [€]"]
+        values = [total_costs, total_revenue, total_balance]
+        colors = ["red" if value > 0 else "green" for value in values]
+        axs[1].bar(labels, values, color=colors)
+        axs[1].set_title("Financial Overview")
+        axs[1].set_ylabel("Euro")
+
+        # Second axis (ax2) for losses, shared with axs[1]
+        ax2 = axs[1].twinx()
+        ax2.bar("Total Losses", losses, color="blue")
+        ax2.set_ylabel("Losses [Wh]", color="blue")
+        ax2.tick_params(axis="y", labelcolor="blue")
+
+        pdf.savefig()  # Save the complete figure to the PDF
+        plt.close()  # Close the figure
+
+        # Additional data visualization if provided
+        if extra_data is not None:
+            plt.figure(figsize=(14, 10))
+            plt.subplot(1, 2, 1)
+            f1 = np.array(extra_data["verluste"])
+            f2 = np.array(extra_data["bilanz"])
+            n1 = np.array(extra_data["nebenbedingung"])
+            scatter = plt.scatter(f1, f2, c=n1, cmap="viridis")
+
+            # Add color legend
+            plt.colorbar(scatter, label="Constraint")
+
+            pdf.savefig()  # Save the complete figure to the PDF
+            plt.close()  # Close the figure
+
+            plt.figure(figsize=(14, 10))
+            filtered_losses = np.array(
+                [
+                    v
+                    for v, n in zip(
+                        extra_data["verluste"], extra_data["nebenbedingung"]
+                    )
+                    if n < 0.01
+                ]
+            )
+            filtered_balance = np.array(
+                [
+                    b
+                    for b, n in zip(extra_data["bilanz"], extra_data["nebenbedingung"])
+                    if n < 0.01
+                ]
+            )
+            if filtered_losses.size != 0:
+                best_loss = min(filtered_losses)
+                worst_loss = max(filtered_losses)
+                best_balance = min(filtered_balance)
+                worst_balance = max(filtered_balance)
+
+                data = [filtered_losses, filtered_balance]
+                labels = ["Losses", "Balance"]
+                # Create plots
+                fig, axs = plt.subplots(
+                    1, 2, figsize=(10, 6), sharey=False
+                )  # Two subplots, separate y-axes
+
+                # First violin plot for losses
+                axs[0].violinplot(data[0], showmeans=True, showmedians=True)
+                axs[0].set_title("Losses")
+                axs[0].set_xticklabels(["Losses"])
+
+                # Second violin plot for balance
+                axs[1].violinplot(data[1], showmeans=True, showmedians=True)
+                axs[1].set_title("Balance")
+                axs[1].set_xticklabels(["Balance"])
+
+                # Fine-tuning
+                plt.tight_layout()
+
+            pdf.savefig()  # Save the current figure state to the PDF
+            plt.close()  # Close the figure