Nested config, devices registry

* All config now nested.
    - Use default config from model field default values. If providers
      should be enabled by default, non-empty default config file could
      be provided again.
    - Environment variable support with EOS_ prefix and __ between levels,
      e.g. EOS_SERVER__EOS_SERVER_PORT=8503 where all values are case
      insensitive.
      For more information see:
      https://docs.pydantic.dev/latest/concepts/pydantic_settings/#parsing-environment-variable-values
    - Use devices as registry for configured devices. DeviceBase as base
      class with for now just initializion support (in the future expand
      to operations during optimization).
    - Strip down ConfigEOS to the only configuration instance. Reload
      from file or reset to defaults is possible.

 * Fix multi-initialization of derived SingletonMixin classes.

src/akkudoktoreos/optimization/genetic.py

@@ -1,7 +1,6 @@
 import logging
 import random
 import time
-from pathlib import Path
 from typing import Any, Optional
 
 import numpy as np
@@ -25,7 +24,6 @@ from akkudoktoreos.devices.battery import (
 )
 from akkudoktoreos.devices.generic import HomeAppliance, HomeApplianceParameters
 from akkudoktoreos.devices.inverter import Inverter, InverterParameters
-from akkudoktoreos.prediction.interpolator import SelfConsumptionProbabilityInterpolator
 from akkudoktoreos.utils.utils import NumpyEncoder
 
 logger = get_logger(__name__)
@@ -112,8 +110,12 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
     ):
         """Initialize the optimization problem with the required parameters."""
         self.opti_param: dict[str, Any] = {}
-        self.fixed_eauto_hours = self.config.prediction_hours - self.config.optimization_hours
-        self.possible_charge_values = self.config.optimization_ev_available_charge_rates_percent
+        self.fixed_eauto_hours = (
+            self.config.prediction.prediction_hours - self.config.optimization.optimization_hours
+        )
+        self.possible_charge_values = (
+            self.config.optimization.optimization_ev_available_charge_rates_percent
+        )
         self.verbose = verbose
         self.fix_seed = fixed_seed
         self.optimize_ev = True
@@ -180,25 +182,27 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
             total_states = 3 * len_ac
 
         # 1. Mutating the charge_discharge part
-        charge_discharge_part = individual[: self.config.prediction_hours]
+        charge_discharge_part = individual[: self.config.prediction.prediction_hours]
         (charge_discharge_mutated,) = self.toolbox.mutate_charge_discharge(charge_discharge_part)
 
         # Instead of a fixed clamping to 0..8 or 0..6 dynamically:
         charge_discharge_mutated = np.clip(charge_discharge_mutated, 0, total_states - 1)
-        individual[: self.config.prediction_hours] = charge_discharge_mutated
+        individual[: self.config.prediction.prediction_hours] = charge_discharge_mutated
 
         # 2. Mutating the EV charge part, if active
         if self.optimize_ev:
             ev_charge_part = individual[
-                self.config.prediction_hours : self.config.prediction_hours * 2
+                self.config.prediction.prediction_hours : self.config.prediction.prediction_hours
+                * 2
             ]
             (ev_charge_part_mutated,) = self.toolbox.mutate_ev_charge_index(ev_charge_part)
-            ev_charge_part_mutated[self.config.prediction_hours - self.fixed_eauto_hours :] = [
-                0
-            ] * self.fixed_eauto_hours
-            individual[self.config.prediction_hours : self.config.prediction_hours * 2] = (
-                ev_charge_part_mutated
-            )
+            ev_charge_part_mutated[
+                self.config.prediction.prediction_hours - self.fixed_eauto_hours :
+            ] = [0] * self.fixed_eauto_hours
+            individual[
+                self.config.prediction.prediction_hours : self.config.prediction.prediction_hours
+                * 2
+            ] = ev_charge_part_mutated
 
         # 3. Mutating the appliance start time, if applicable
         if self.opti_param["home_appliance"] > 0:
@@ -212,13 +216,15 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
     def create_individual(self) -> list[int]:
         # Start with discharge states for the individual
         individual_components = [
-            self.toolbox.attr_discharge_state() for _ in range(self.config.prediction_hours)
+            self.toolbox.attr_discharge_state()
+            for _ in range(self.config.prediction.prediction_hours)
         ]
 
         # Add EV charge index values if optimize_ev is True
         if self.optimize_ev:
             individual_components += [
-                self.toolbox.attr_ev_charge_index() for _ in range(self.config.prediction_hours)
+                self.toolbox.attr_ev_charge_index()
+                for _ in range(self.config.prediction.prediction_hours)
             ]
 
         # Add the start time of the household appliance if it's being optimized
@@ -251,7 +257,7 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
             individual.extend(eautocharge_hours_index.tolist())
         elif self.optimize_ev:
             # Falls optimize_ev aktiv ist, aber keine EV-Daten vorhanden sind, fügen wir Nullen hinzu
-            individual.extend([0] * self.config.prediction_hours)
+            individual.extend([0] * self.config.prediction.prediction_hours)
 
         # Add dishwasher start time if applicable
         if self.opti_param.get("home_appliance", 0) > 0 and washingstart_int is not None:
@@ -273,12 +279,17 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
         3. Dishwasher start time (integer if applicable).
         """
         # Discharge hours as a NumPy array of ints
-        discharge_hours_bin = np.array(individual[: self.config.prediction_hours], dtype=int)
+        discharge_hours_bin = np.array(
+            individual[: self.config.prediction.prediction_hours], dtype=int
+        )
 
         # EV charge hours as a NumPy array of ints (if optimize_ev is True)
         eautocharge_hours_index = (
             np.array(
-                individual[self.config.prediction_hours : self.config.prediction_hours * 2],
+                individual[
+                    self.config.prediction.prediction_hours : self.config.prediction.prediction_hours
+                    * 2
+                ],
                 dtype=int,
             )
             if self.optimize_ev
@@ -390,7 +401,7 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
             )
             self.ems.set_ev_charge_hours(eautocharge_hours_float)
         else:
-            self.ems.set_ev_charge_hours(np.full(self.config.prediction_hours, 0))
+            self.ems.set_ev_charge_hours(np.full(self.config.prediction.prediction_hours, 0))
 
         return self.ems.simulate(self.ems.start_datetime.hour)
 
@@ -452,7 +463,7 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
         #     min_length = min(battery_soc_per_hour.size, discharge_hours_bin.size)
         #     battery_soc_per_hour_tail = battery_soc_per_hour[-min_length:]
         #     discharge_hours_bin_tail = discharge_hours_bin[-min_length:]
-        #     len_ac = len(self.config.optimization_ev_available_charge_rates_percent)
+        #     len_ac = len(self.config.optimization.optimization_ev_available_charge_rates_percent)
 
         #     # # Find hours where battery SoC is 0
         #     # zero_soc_mask = battery_soc_per_hour_tail == 0
@@ -501,7 +512,7 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
                     if parameters.eauto and self.ems.ev
                     else 0
                 )
-                * self.config.optimization_penalty,
+                * self.config.optimization.optimization_penalty,
             )
 
         return (gesamtbilanz,)
@@ -569,30 +580,26 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
             start_hour = self.ems.start_datetime.hour
 
         einspeiseverguetung_euro_pro_wh = np.full(
-            self.config.prediction_hours, parameters.ems.einspeiseverguetung_euro_pro_wh
+            self.config.prediction.prediction_hours, parameters.ems.einspeiseverguetung_euro_pro_wh
         )
 
-        # 1h Load to Sub 1h Load Distribution -> SelfConsumptionRate
-        sc = SelfConsumptionProbabilityInterpolator(
-            Path(__file__).parent.resolve() / ".." / "data" / "regular_grid_interpolator.pkl"
-        )
+        # TODO: Refactor device setup phase out
+        self.devices.reset()
 
         # Initialize PV and EV batteries
         akku: Optional[Battery] = None
         if parameters.pv_akku:
-            akku = Battery(
-                parameters.pv_akku,
-                hours=self.config.prediction_hours,
-            )
-            akku.set_charge_per_hour(np.full(self.config.prediction_hours, 1))
+            akku = Battery(parameters.pv_akku)
+            self.devices.add_device(akku)
+            akku.set_charge_per_hour(np.full(self.config.prediction.prediction_hours, 1))
 
         eauto: Optional[Battery] = None
         if parameters.eauto:
             eauto = Battery(
                 parameters.eauto,
-                hours=self.config.prediction_hours,
             )
-            eauto.set_charge_per_hour(np.full(self.config.prediction_hours, 1))
+            self.devices.add_device(eauto)
+            eauto.set_charge_per_hour(np.full(self.config.prediction.prediction_hours, 1))
             self.optimize_ev = (
                 parameters.eauto.min_soc_percentage - parameters.eauto.initial_soc_percentage >= 0
             )
@@ -603,20 +610,22 @@ class optimization_problem(ConfigMixin, DevicesMixin, EnergyManagementSystemMixi
         dishwasher = (
             HomeAppliance(
                 parameters=parameters.dishwasher,
-                hours=self.config.prediction_hours,
             )
             if parameters.dishwasher is not None
             else None
         )
+        self.devices.add_device(dishwasher)
 
         # Initialize the inverter and energy management system
         inverter: Optional[Inverter] = None
         if parameters.inverter:
             inverter = Inverter(
-                sc,
                 parameters.inverter,
-                akku,
             )
+            self.devices.add_device(inverter)
+
+        self.devices.post_setup()
+
         self.ems.set_parameters(
             parameters.ems,
             inverter=inverter,

src/akkudoktoreos/optimization/optimization.py

@@ -16,7 +16,7 @@ class OptimizationCommonSettings(SettingsBaseModel):
     """
 
     optimization_hours: Optional[int] = Field(
-        default=24, ge=0, description="Number of hours into the future for optimizations."
+        default=48, ge=0, description="Number of hours into the future for optimizations."
     )
 
     optimization_penalty: Optional[int] = Field(