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renamed haushaltsgeräte to home appliance (#196)

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* * rename Haushaltsgeraete to home appliance
* renamed strafe to penalty (optimization problem)

Signed-off-by: Jürgen Eckel <juergen.eckel@gmail.com>

* removed penalty renaming

Signed-off-by: Jürgen Eckel <juergen.eckel@gmail.com>

* renamed one variable

Signed-off-by: Jürgen Eckel <juergen.eckel@gmail.com>

* * renamed variable names and methods of the home appliance class

* renamed missed method names

* fixed renamed variable

* renamed object

* adjusted to latest repo changes

* renamed file to class_home_applianc.py

* renamed method

---------

Signed-off-by: Jürgen Eckel <juergen.eckel@gmail.com>
Signed-off-by: Jürgen Eckel <juergen@riddleandcode.com>
This commit is contained in:
Jürgen Eckel
2024-11-26 00:53:16 +01:00
committed by GitHub
parent 12679b6ab1
commit 2a163569bc
16 changed files with 174 additions and 107 deletions
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44
src/akkudoktoreos/optimization/genetic.py
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@@ -8,7 +8,7 @@ from typing_extensions import Self
from akkudoktoreos.config import AppConfig
from akkudoktoreos.devices.battery import EAutoParameters, PVAkku, PVAkkuParameters
from akkudoktoreos.devices.generic import Haushaltsgeraet, HaushaltsgeraetParameters
from akkudoktoreos.devices.generic import HomeAppliance, HomeApplianceParameters
from akkudoktoreos.devices.inverter import Wechselrichter, WechselrichterParameters
from akkudoktoreos.prediction.ems import (
EnergieManagementSystem,
@@ -22,7 +22,7 @@ class OptimizationParameters(BaseModel):
pv_akku: PVAkkuParameters
wechselrichter: WechselrichterParameters = WechselrichterParameters()
eauto: EAutoParameters
spuelmaschine: Optional[HaushaltsgeraetParameters] = None
dishwasher: Optional[HomeApplianceParameters] = None
temperature_forecast: list[float] = Field(
"An array of floats representing the temperature forecast in degrees Celsius for different time intervals."
)
@@ -86,7 +86,7 @@ class SimulationResult(BaseModel):
)
Gesamteinnahmen_Euro: float = Field(description="The total revenues in euros.")
Gesamtkosten_Euro: float = Field(description="The total costs in euros.")
Haushaltsgeraet_wh_pro_stunde: list[Optional[float]] = Field(
Home_appliance_wh_per_hour: list[Optional[float]] = Field(
description="The energy consumption of a household appliance in watt-hours per hour."
)
Kosten_Euro_pro_Stunde: list[Optional[float]] = Field(
@@ -120,7 +120,7 @@ class SimulationResult(BaseModel):
# Gesamtbilanz_Euro: float = Field(description="The total simulated balance in euros.")
# Gesamteinnahmen_Euro: float = Field(description="The total simulated income in euros.")
# Gesamtkosten_Euro: float = Field(description="The total simulated costs in euros.")
# Haushaltsgeraet_wh_pro_stunde: list[Optional[float]] = Field(
# Home_appliance_wh_per_hour: list[Optional[float]] = Field(
# description="An array of floats representing the simulated energy consumption of a household appliance in watt-hours per hour."
# )
# Kosten_Euro_pro_Stunde: list[Optional[float]] = Field(
@@ -158,7 +158,7 @@ class OptimizeResponse(BaseModel):
None,
description="An array of binary values (0 or 1) representing a possible starting solution for the simulation.",
)
spuelstart: Optional[int] = Field(
washingstart: Optional[int] = Field(
None,
description="Can be `null` or contain an object representing the start of washing (if applicable).",
)
@@ -285,7 +285,7 @@ class optimization_problem:
individual[self.prediction_hours : self.prediction_hours * 2] = ev_charge_part_mutated
# Step 3: Mutate appliance start times if household appliances are part of the optimization
if self.opti_param["haushaltsgeraete"] > 0:
if self.opti_param["home_appliance"] > 0:
# Extract the appliance part (typically a single value for the start hour)
appliance_part = [individual[-1]]
@@ -311,7 +311,7 @@ class optimization_problem:
]
# Add the start time of the household appliance if it's being optimized
if self.opti_param["haushaltsgeraete"] > 0:
if self.opti_param["home_appliance"] > 0:
individual_components += [self.toolbox.attr_int()]
return creator.Individual(individual_components)
@@ -333,12 +333,12 @@ class optimization_problem:
else None
)
spuelstart_int = (
washingstart_int = (
individual[-1]
if self.opti_param and self.opti_param.get("haushaltsgeraete", 0) > 0
if self.opti_param and self.opti_param.get("home_appliance", 0) > 0
else None
)
return discharge_hours_bin, eautocharge_hours_float, spuelstart_int
return discharge_hours_bin, eautocharge_hours_float, washingstart_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."""
@@ -410,11 +410,11 @@ class optimization_problem:
This is an internal function.
"""
ems.reset()
discharge_hours_bin, eautocharge_hours_index, spuelstart_int = self.split_individual(
discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual(
individual
)
if self.opti_param.get("haushaltsgeraete", 0) > 0:
ems.set_haushaltsgeraet_start(spuelstart_int, global_start_hour=start_hour)
if self.opti_param.get("home_appliance", 0) > 0:
ems.set_home_appliance_start(washingstart_int, global_start_hour=start_hour)
ac, dc, discharge = self.decode_charge_discharge(discharge_hours_bin)
@@ -557,12 +557,12 @@ class optimization_problem:
eauto.set_charge_per_hour(np.full(self.prediction_hours, 1))
# Initialize household appliance if applicable
spuelmaschine = (
Haushaltsgeraet(
parameters=parameters.spuelmaschine,
dishwasher = (
HomeAppliance(
parameters=parameters.dishwasher,
hours=self.prediction_hours,
)
if parameters.spuelmaschine is not None
if parameters.dishwasher is not None
else None
)
@@ -572,12 +572,12 @@ class optimization_problem:
self._config.eos,
parameters.ems,
eauto=eauto,
haushaltsgeraet=spuelmaschine,
home_appliance=dishwasher,
wechselrichter=wr,
)
# Setup the DEAP environment and optimization process
self.setup_deap_environment({"haushaltsgeraete": 1 if spuelmaschine else 0}, start_hour)
self.setup_deap_environment({"home_appliance": 1 if dishwasher else 0}, start_hour)
self.toolbox.register(
"evaluate",
lambda ind: self.evaluate(ind, ems, parameters, start_hour, worst_case),
@@ -586,7 +586,7 @@ class optimization_problem:
# 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(
discharge_hours_bin, eautocharge_hours_float, washingstart_int = self.split_individual(
start_solution
)
if self.optimize_ev:
@@ -622,7 +622,7 @@ class optimization_problem:
"Einnahmen_Euro_pro_Stunde",
"EAuto_SoC_pro_Stunde",
"Verluste_Pro_Stunde",
"Haushaltsgeraet_wh_pro_stunde",
"Home_appliance_wh_per_hour",
]
# Loop through each key in the list
@@ -649,6 +649,6 @@ class optimization_problem:
"result": o,
"eauto_obj": ems.eauto.to_dict(),
"start_solution": start_solution,
"spuelstart": spuelstart_int,
"washingstart": washingstart_int,
# "simulation_data": o,
}