fix: automatic optimization (#596)

This fix implements the long term goal to have the EOS server run optimization (or
energy management) on regular intervals automatically. Thus clients can request
the current energy management plan at any time and it is updated on regular
intervals without interaction by the client.

This fix started out to "only" make automatic optimization (or energy management)
runs working. It turned out there are several endpoints that in some way
update predictions or run the optimization. To lock against such concurrent attempts
the code had to be refactored to allow control of execution. During refactoring it
became clear that some classes and files are named without a proper reference
to their usage. Thus not only refactoring but also renaming became necessary.
The names are still not the best, but I hope they are more intuitive.

The fix includes several bug fixes that are not directly related to the automatic optimization
but are necessary to keep EOS running properly to do the automatic optimization and
to test and document the changes.

This is a breaking change as the configuration structure changed once again and
the server API was also enhanced and streamlined. The server API that is used by
Andreas and Jörg in their videos has not changed.

* fix: automatic optimization

  Allow optimization to automatically run on configured intervals gathering all
  optimization parameters from configuration and predictions. The automatic run
  can be configured to only run prediction updates skipping the optimization.
  Extend documentaion to also cover automatic optimization. Lock automatic runs
  against runs initiated by the /optimize or other endpoints. Provide new
  endpoints to retrieve the energy management plan and the genetic solution
  of the latest automatic optimization run. Offload energy management to thread
  pool executor to keep the app more responsive during the CPU heavy optimization
  run.

* fix: EOS servers recognize environment variables on startup

  Force initialisation of EOS configuration on server startup to assure
  all sources of EOS configuration are properly set up and read. Adapt
  server tests and configuration tests to also test for environment
  variable configuration.

* fix: Remove 0.0.0.0 to localhost translation under Windows

  EOS imposed a 0.0.0.0 to localhost translation under Windows for
  convenience. This caused some trouble in user configurations. Now, as the
  default IP address configuration is 127.0.0.1, the user is responsible
  for to set up the correct Windows compliant IP address.

* fix: allow names for hosts additional to IP addresses

* fix: access pydantic model fields by class

  Access by instance is deprecated.

* fix: down sampling key_to_array

* fix: make cache clear endpoint clear all cache files

  Make /v1/admin/cache/clear clear all cache files. Before it only cleared
  expired cache files by default. Add new endpoint /v1/admin/clear-expired
  to only clear expired cache files.

* fix: timezonefinder returns Europe/Paris instead of Europe/Berlin

  timezonefinder 8.10 got more inaccurate for timezones in europe as there is
  a common timezone. Use new package tzfpy instead which is still returning
  Europe/Berlin if you are in Germany. tzfpy also claims to be faster than
  timezonefinder.

* fix: provider settings configuration

  Provider configuration used to be a union holding the settings for several
  providers. Pydantic union handling does not always find the correct type
  for a provider setting. This led to exceptions in specific configurations.
  Now provider settings are explicit comfiguration items for each possible
  provider. This is a breaking change as the configuration structure was
  changed.

* fix: ClearOutside weather prediction irradiance calculation

  Pvlib needs a pandas time index. Convert time index.

* fix: test config file priority

  Do not use config_eos fixture as this fixture already creates a config file.

* fix: optimization sample request documentation

  Provide all data in documentation of optimization sample request.

* fix: gitlint blocking pip dependency resolution

  Replace gitlint by commitizen. Gitlint is not actively maintained anymore.
  Gitlint dependencies blocked pip from dependency resolution.

* fix: sync pre-commit config to actual dependency requirements

  .pre-commit-config.yaml was out of sync, also requirements-dev.txt.

* fix: missing babel in requirements.txt

  Add babel to requirements.txt

* feat: setup default device configuration for automatic optimization

  In case the parameters for automatic optimization are not fully defined a
  default configuration is setup to allow the automatic energy management
  run. The default configuration may help the user to correctly define
  the device configuration.

* feat: allow configuration of genetic algorithm parameters

  The genetic algorithm parameters for number of individuals, number of
  generations, the seed and penalty function parameters are now avaliable
  as configuration options.

* feat: allow configuration of home appliance time windows

  The time windows a home appliance is allowed to run are now configurable
  by the configuration (for /v1 API) and also by the home appliance parameters
  (for the classic /optimize API). If there is no such configuration the
  time window defaults to optimization hours, which was the standard before
  the change. Documentation on how to configure time windows is added.

* feat: standardize mesaurement keys for battery/ ev SoC measurements

  The standardized measurement keys to report battery SoC to the device
  simulations can now be retrieved from the device configuration as a
  read-only config option.

* feat: feed in tariff prediction

  Add feed in tarif predictions needed for automatic optimization. The feed in
  tariff can be retrieved as fixed feed in tarif or can be imported. Also add
  tests for the different feed in tariff providers. Extend documentation to
  cover the feed in tariff providers.

* feat: add energy management plan based on S2 standard instructions

  EOS can generate an energy management plan as a list of simple instructions.
  May be retrieved by the /v1/energy-management/plan endpoint. The instructions
  loosely follow the S2 energy management standard.

* feat: make measurement keys configurable by EOS configuration.

  The fixed measurement keys are replaced by configurable measurement keys.

* feat: make pendulum DateTime, Date, Duration types usable for pydantic models

  Use pydantic_extra_types.pendulum_dt to get pydantic pendulum types. Types are
  added to the datetimeutil utility. Remove custom made pendulum adaptations
  from EOS pydantic module. Make EOS modules use the pydantic pendulum types
  managed by the datetimeutil module instead of the core pendulum types.

* feat: Add Time, TimeWindow, TimeWindowSequence and to_time to datetimeutil.

  The time windows are are added to support home appliance time window
  configuration. All time classes are also pydantic models. Time is the base
  class for time definition derived from pendulum.Time.

* feat: Extend DataRecord by configurable field like data.

  Configurable field like data was added to support the configuration of
  measurement records.

* feat: Add additional information to health information

  Version information is added to the health endpoints of eos and eosDash.
  The start time of the last optimization and the latest run time of the energy
  management is added to the EOS health information.

* feat: add pydantic merge model tests

* feat: add plan tab to EOSdash

  The plan tab displays the current energy management instructions.

* feat: add predictions tab to EOSdash

  The predictions tab displays the current predictions.

* feat: add cache management to EOSdash admin tab

  The admin tab is extended by a section for cache management. It allows to
  clear the cache.

* feat: add about tab to EOSdash

  The about tab resembles the former hello tab and provides extra information.

* feat: Adapt changelog and prepare for release management

  Release management using commitizen is added. The changelog file is adapted and
  teh changelog and a description for release management is added in the
  documentation.

* feat(doc): Improve install and devlopment documentation

  Provide a more concise installation description in Readme.md and add extra
  installation page and development page to documentation.

* chore: Use memory cache for interpolation instead of dict in inverter

  Decorate calculate_self_consumption() with @cachemethod_until_update to cache
  results in memory during an energy management/ optimization run. Replacement
  of dict type caching in inverter is now possible because all optimization
  runs are properly locked and the memory cache CacheUntilUpdateStore is properly
  cleared at the start of any energy management/ optimization operation.

* chore: refactor genetic

  Refactor the genetic algorithm modules for enhanced module structure and better
  readability. Removed unnecessary and overcomplex devices singleton. Also
  split devices configuration from genetic algorithm parameters to allow further
  development independently from genetic algorithm parameter format. Move
  charge rates configuration for electric vehicles from optimization to devices
  configuration to allow to have different charge rates for different cars in
  the future.

* chore: Rename memory cache to CacheEnergyManagementStore

  The name better resembles the task of the cache to chache function and method
  results for an energy management run. Also the decorator functions are renamed
  accordingly: cachemethod_energy_management, cache_energy_management

* chore: use class properties for config/ems/prediction mixin classes

* chore: skip debug logs from mathplotlib

  Mathplotlib is very noisy in debug mode.

* chore: automatically sync bokeh js to bokeh python package

  bokeh was updated to 3.8.0, make JS CDN automatically follow the package version.

* chore: rename hello.py to about.py

  Make hello.py the adapted EOSdash about page.

* chore: remove demo page from EOSdash

  As no the plan and prediction pages are working without configuration, the demo
  page is no longer necessary

* chore: split test_server.py for system test

  Split test_server.py to create explicit test_system.py for system tests.

* chore: move doc utils to generate_config_md.py

  The doc utils are only used in scripts/generate_config_md.py. Move it there to
  attribute for strong cohesion.

* chore: improve pydantic merge model documentation

* chore: remove pendulum warning from readme

* chore: remove GitHub discussions from contributing documentation

  Github discussions is to be replaced by Akkudoktor.net.

* chore(release): bump version to 0.1.0+dev for development

* build(deps): bump fastapi[standard] from 0.115.14 to 0.117.1

  bump fastapi and make coverage version (for pytest-cov) explicit to avoid pip break.

* build(deps): bump uvicorn from 0.36.0 to 0.37.0

BREAKING CHANGE: EOS configuration changed. V1 API changed.

  - The available_charge_rates_percent configuration is removed from optimization.
    Use the new charge_rate configuration for the electric vehicle
  - Optimization configuration parameter hours renamed to horizon_hours
  - Device configuration now has to provide the number of devices and device
    properties per device.
  - Specific prediction provider configuration to be provided by explicit
    configuration item (no union for all providers).
  - Measurement keys to be provided as a list.
  - New feed in tariff providers have to be configured.
  - /v1/measurement/loadxxx endpoints are removed. Use generic mesaurement endpoints.
  - /v1/admin/cache/clear now clears all cache files. Use
    /v1/admin/cache/clear-expired to only clear all expired cache files.

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

src/akkudoktoreos/devices/genetic/battery.py

@@ -0,0 +1,151 @@
+from typing import Any, Optional
+
+import numpy as np
+
+from akkudoktoreos.optimization.genetic.geneticdevices import (
+    BaseBatteryParameters,
+    SolarPanelBatteryParameters,
+)
+
+
+class Battery:
+    """Represents a battery device with methods to simulate energy charging and discharging."""
+
+    def __init__(self, parameters: BaseBatteryParameters, prediction_hours: int):
+        self.parameters = parameters
+        self.prediction_hours = prediction_hours
+        self._setup()
+
+    def _setup(self) -> None:
+        """Sets up the battery parameters based on configuration or provided parameters."""
+        self.capacity_wh = self.parameters.capacity_wh
+        self.initial_soc_percentage = self.parameters.initial_soc_percentage
+        self.charging_efficiency = self.parameters.charging_efficiency
+        self.discharging_efficiency = self.parameters.discharging_efficiency
+
+        # Only assign for storage battery
+        self.min_soc_percentage = (
+            self.parameters.min_soc_percentage
+            if isinstance(self.parameters, SolarPanelBatteryParameters)
+            else 0
+        )
+        self.max_soc_percentage = self.parameters.max_soc_percentage
+
+        # Initialize state of charge
+        if self.parameters.max_charge_power_w is not None:
+            self.max_charge_power_w = self.parameters.max_charge_power_w
+        else:
+            self.max_charge_power_w = self.capacity_wh  # TODO this should not be equal capacity_wh
+        self.discharge_array = np.full(self.prediction_hours, 1)
+        self.charge_array = np.full(self.prediction_hours, 1)
+        self.soc_wh = (self.initial_soc_percentage / 100) * self.capacity_wh
+        self.min_soc_wh = (self.min_soc_percentage / 100) * self.capacity_wh
+        self.max_soc_wh = (self.max_soc_percentage / 100) * self.capacity_wh
+
+    def to_dict(self) -> dict[str, Any]:
+        """Converts the object to a dictionary representation."""
+        return {
+            "device_id": self.parameters.device_id,
+            "capacity_wh": self.capacity_wh,
+            "initial_soc_percentage": self.initial_soc_percentage,
+            "soc_wh": self.soc_wh,
+            "hours": self.prediction_hours,
+            "discharge_array": self.discharge_array,
+            "charge_array": self.charge_array,
+            "charging_efficiency": self.charging_efficiency,
+            "discharging_efficiency": self.discharging_efficiency,
+            "max_charge_power_w": self.max_charge_power_w,
+        }
+
+    def reset(self) -> None:
+        """Resets the battery state to its initial values."""
+        self.soc_wh = (self.initial_soc_percentage / 100) * self.capacity_wh
+        self.soc_wh = min(max(self.soc_wh, self.min_soc_wh), self.max_soc_wh)
+        self.discharge_array = np.full(self.prediction_hours, 1)
+        self.charge_array = np.full(self.prediction_hours, 1)
+
+    def set_discharge_per_hour(self, discharge_array: np.ndarray) -> None:
+        """Sets the discharge values for each hour."""
+        if len(discharge_array) != self.prediction_hours:
+            raise ValueError(
+                f"Discharge array must have exactly {self.prediction_hours} elements. Got {len(discharge_array)} elements."
+            )
+        self.discharge_array = np.array(discharge_array)
+
+    def set_charge_per_hour(self, charge_array: np.ndarray) -> None:
+        """Sets the charge values for each hour."""
+        if len(charge_array) != self.prediction_hours:
+            raise ValueError(
+                f"Charge array must have exactly {self.prediction_hours} elements. Got {len(charge_array)} elements."
+            )
+        self.charge_array = np.array(charge_array)
+
+    def set_charge_allowed_for_hour(self, charge: float, hour: int) -> None:
+        """Sets the charge for a specific hour."""
+        if hour >= self.prediction_hours:
+            raise ValueError(
+                f"Hour {hour} is out of range. Must be less than {self.prediction_hours}."
+            )
+        self.charge_array[hour] = charge
+
+    def current_soc_percentage(self) -> float:
+        """Calculates the current state of charge in percentage."""
+        return (self.soc_wh / self.capacity_wh) * 100
+
+    def discharge_energy(self, wh: float, hour: int) -> tuple[float, float]:
+        """Discharges energy from the battery."""
+        if self.discharge_array[hour] == 0:
+            return 0.0, 0.0
+
+        max_possible_discharge_wh = (self.soc_wh - self.min_soc_wh) * self.discharging_efficiency
+        max_possible_discharge_wh = max(max_possible_discharge_wh, 0.0)
+
+        max_possible_discharge_wh = min(
+            max_possible_discharge_wh, self.max_charge_power_w
+        )  # TODO make a new cfg variable max_discharge_power_w
+
+        actual_discharge_wh = min(wh, max_possible_discharge_wh)
+        actual_withdrawal_wh = (
+            actual_discharge_wh / self.discharging_efficiency
+            if self.discharging_efficiency > 0
+            else 0.0
+        )
+
+        self.soc_wh -= actual_withdrawal_wh
+        self.soc_wh = max(self.soc_wh, self.min_soc_wh)
+
+        losses_wh = actual_withdrawal_wh - actual_discharge_wh
+        return actual_discharge_wh, losses_wh
+
+    def charge_energy(
+        self, wh: Optional[float], hour: int, relative_power: float = 0.0
+    ) -> tuple[float, float]:
+        """Charges energy into the battery."""
+        if hour is not None and self.charge_array[hour] == 0:
+            return 0.0, 0.0  # Charging not allowed in this hour
+
+        if relative_power > 0.0:
+            wh = self.max_charge_power_w * relative_power
+
+        wh = wh if wh is not None else self.max_charge_power_w
+
+        max_possible_charge_wh = (
+            (self.max_soc_wh - self.soc_wh) / self.charging_efficiency
+            if self.charging_efficiency > 0
+            else 0.0
+        )
+        max_possible_charge_wh = max(max_possible_charge_wh, 0.0)
+
+        effective_charge_wh = min(wh, max_possible_charge_wh)
+        charged_wh = effective_charge_wh * self.charging_efficiency
+
+        self.soc_wh += charged_wh
+        self.soc_wh = min(self.soc_wh, self.max_soc_wh)
+
+        losses_wh = effective_charge_wh - charged_wh
+        return charged_wh, losses_wh
+
+    def current_energy_content(self) -> float:
+        """Returns the current usable energy in the battery."""
+        usable_energy = (self.soc_wh - self.min_soc_wh) * self.discharging_efficiency
+        return max(usable_energy, 0.0)

src/akkudoktoreos/devices/genetic/heatpump.py

@@ -0,0 +1,151 @@
+from typing import List, Sequence
+
+from loguru import logger
+
+
+class Heatpump:
+    MAX_HEAT_OUTPUT = 5000
+    """Maximum heating power in watts"""
+
+    BASE_HEAT_POWER = 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: int, hours: int):
+        self.max_heat_output = max_heat_output
+        self.hours = hours
+
+    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 -100 < 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)"
+            )
+            logger.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_HEAT_POWER + 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)"
+            )
+            logger.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)"
+            )
+            logger.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.hours:
+            raise ValueError(
+                f"The temperature array must contain exactly {self.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_heating_power = 5000  # 5 kW heating power
+    initial_indoor_temperature = 15  # Initial indoor temperature
+    insulation_efficiency = 0.8  # Insulation efficiency
+    desired_indoor_temperature = 20  # Desired indoor temperature
+    hp = Heatpump(max_heating_power, 24)  # Initialize heat pump with prediction hours
+
+    # Print COP for various outside temperatures
+    print(hp.calculate_cop(-10), " ", hp.calculate_cop(0), " ", hp.calculate_cop(10))
+
+    # 24 hours of outside temperatures (example values)
+    temperatures = [
+        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
+    power_data = hp.simulate_24h(temperatures)
+
+    print(power_data)

src/akkudoktoreos/devices/genetic/homeappliance.py

@@ -0,0 +1,112 @@
+from typing import Optional
+
+import numpy as np
+
+from akkudoktoreos.optimization.genetic.geneticdevices import HomeApplianceParameters
+from akkudoktoreos.utils.datetimeutil import (
+    TimeWindow,
+    TimeWindowSequence,
+    to_datetime,
+    to_duration,
+    to_time,
+)
+
+
+class HomeAppliance:
+    def __init__(
+        self,
+        parameters: HomeApplianceParameters,
+        optimization_hours: int,
+        prediction_hours: int,
+    ):
+        self.parameters: HomeApplianceParameters = parameters
+        self.prediction_hours = prediction_hours
+        self._setup()
+
+    def _setup(self) -> None:
+        """Sets up the home appliance parameters based provided parameters."""
+        self.load_curve = np.zeros(self.prediction_hours)  # Initialize the load curve with zeros
+        self.duration_h = self.parameters.duration_h
+        self.consumption_wh = self.parameters.consumption_wh
+        self.appliance_start: Optional[int] = None
+        # setup possible start times
+        if self.parameters.time_windows is None:
+            self.parameters.time_windows = TimeWindowSequence(
+                windows=[
+                    TimeWindow(
+                        start_time=to_time("00:00"),
+                        duration=to_duration(f"{self.prediction_hours} hours"),
+                    ),
+                ]
+            )
+        start_datetime = to_datetime().set(hour=0, minute=0, second=0)
+        duration = to_duration(f"{self.duration_h} hours")
+        self.start_allowed: list[bool] = []
+        for hour in range(0, self.prediction_hours):
+            self.start_allowed.append(
+                self.parameters.time_windows.contains(
+                    start_datetime.add(hours=hour), duration=duration
+                )
+            )
+        start_earliest = self.parameters.time_windows.earliest_start_time(duration, start_datetime)
+        if start_earliest:
+            self.start_earliest = start_earliest.hour
+        else:
+            self.start_earliest = 0
+        start_latest = self.parameters.time_windows.latest_start_time(duration, start_datetime)
+        if start_latest:
+            self.start_latest = start_latest.hour
+        else:
+            self.start_latest = 23
+
+    def set_starting_time(self, start_hour: int, global_start_hour: int = 0) -> None:
+        """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_load_curve()
+
+        # Check if the duration of use is within the available time windows
+        if not self.start_allowed[start_hour]:
+            # No available time window to start home appliance
+            # Use the earliest one
+            start_hour = self.start_earliest
+
+        # Check if it is possibility to start the appliance
+        if start_hour < global_start_hour:
+            # Start is before current time
+            # Use the latest one
+            start_hour = self.start_latest
+
+        # Calculate power per hour based on total consumption and duration
+        power_per_hour = self.consumption_wh / self.duration_h  # Convert to watt-hours
+
+        # Set the power for the duration of use in the load curve array
+        self.load_curve[start_hour : start_hour + self.duration_h] = power_per_hour
+
+        # Set the selected start hour
+        self.appliance_start = start_hour
+
+    def reset_load_curve(self) -> None:
+        """Resets the load curve."""
+        self.load_curve = np.zeros(self.prediction_hours)
+
+    def get_load_curve(self) -> np.ndarray:
+        """Returns the current load curve."""
+        return self.load_curve
+
+    def get_load_for_hour(self, hour: int) -> float:
+        """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.prediction_hours:
+            raise ValueError(
+                f"The specified hour {hour} is outside the available time frame {self.prediction_hours}."
+            )
+
+        return self.load_curve[hour]
+
+    def get_appliance_start(self) -> Optional[int]:
+        return self.appliance_start

src/akkudoktoreos/devices/genetic/inverter.py

@@ -0,0 +1,112 @@
+from typing import Optional
+
+from loguru import logger
+
+from akkudoktoreos.devices.genetic.battery import Battery
+from akkudoktoreos.optimization.genetic.geneticdevices import InverterParameters
+from akkudoktoreos.prediction.interpolator import get_eos_load_interpolator
+
+
+class Inverter:
+    def __init__(
+        self,
+        parameters: InverterParameters,
+        battery: Optional[Battery] = None,
+    ):
+        self.parameters: InverterParameters = parameters
+        self.battery: Optional[Battery] = battery
+        self._setup()
+
+    def _setup(self) -> None:
+        if self.battery and self.parameters.battery_id != self.battery.parameters.device_id:
+            error_msg = f"Battery ID mismatch - {self.parameters.battery_id} is configured; got {self.battery.parameters.device_id}."
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+        self.self_consumption_predictor = get_eos_load_interpolator()
+        self.max_power_wh = (
+            self.parameters.max_power_wh
+        )  # Maximum power that the inverter can handle
+
+    def process_energy(
+        self, generation: float, consumption: float, hour: int
+    ) -> tuple[float, float, float, float]:
+        losses = 0.0
+        grid_export = 0.0
+        grid_import = 0.0
+        self_consumption = 0.0
+
+        if generation >= consumption:
+            if consumption > self.max_power_wh:
+                # If consumption exceeds maximum inverter power
+                losses += generation - self.max_power_wh
+                remaining_power = self.max_power_wh - consumption
+                grid_import = -remaining_power  # Negative indicates feeding into the grid
+                self_consumption = self.max_power_wh
+            else:
+                # Calculate scr using cached results per energy management/optimization run
+                scr = self.self_consumption_predictor.calculate_self_consumption(
+                    consumption, generation
+                )
+
+                # Remaining power after consumption
+                remaining_power = (generation - consumption) * scr  # EVQ
+                # Remaining load Self Consumption not perfect
+                remaining_load_evq = (generation - consumption) * (1.0 - scr)
+
+                if remaining_load_evq > 0:
+                    # Akku muss den Restverbrauch decken
+                    if self.battery:
+                        from_battery, discharge_losses = self.battery.discharge_energy(
+                            remaining_load_evq, hour
+                        )
+                        remaining_load_evq -= from_battery  # Restverbrauch nach Akkuentladung
+                        losses += discharge_losses
+
+                    # Wenn der Akku den Restverbrauch nicht vollständig decken kann, wird der Rest ins Netz gezogen
+                    if remaining_load_evq > 0:
+                        grid_import += remaining_load_evq
+                        remaining_load_evq = 0
+                else:
+                    from_battery = 0.0
+
+                if remaining_power > 0:
+                    # Load battery with excess energy
+                    if self.battery:
+                        charged_energie, charge_losses = self.battery.charge_energy(
+                            remaining_power, hour
+                        )
+                        remaining_surplus = remaining_power - (charged_energie + charge_losses)
+                    else:
+                        remaining_surplus = remaining_power
+
+                    # Feed-in to the grid based on remaining capacity
+                    if remaining_surplus > self.max_power_wh - consumption:
+                        grid_export = self.max_power_wh - consumption
+                        losses += remaining_surplus - grid_export
+                    else:
+                        grid_export = remaining_surplus
+
+                    losses += charge_losses
+                self_consumption = (
+                    consumption + from_battery
+                )  # Self-consumption is equal to the load
+
+        else:
+            # Case 2: Insufficient generation, cover shortfall
+            shortfall = consumption - generation
+            available_ac_power = max(self.max_power_wh - generation, 0)
+
+            # Discharge battery to cover shortfall, if possible
+            if self.battery:
+                battery_discharge, discharge_losses = self.battery.discharge_energy(
+                    min(shortfall, available_ac_power), hour
+                )
+                losses += discharge_losses
+            else:
+                battery_discharge = 0
+
+            # Draw remaining required power from the grid (discharge_losses are already substraved in the battery)
+            grid_import = shortfall - battery_discharge
+            self_consumption = generation + battery_discharge
+
+        return grid_export, grid_import, losses, self_consumption