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Self consumption predictor

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* Inverter: Self consumption interpolator for better discharge_hour results
 * Small penalty when EV 100% and charge >0
 * Price Forceast (use mean of last 7 days instead of repeat)
 * Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
This commit is contained in:
Andreas
2024-12-19 14:45:20 +01:00
committed by Dominique Lasserre
parent 1c75060d8a
commit 410a23e375
15 changed files with 1243 additions and 820 deletions
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9
src/akkudoktoreos/devices/devices.py
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@@ -10,6 +10,9 @@ from akkudoktoreos.devices.battery import Battery
from akkudoktoreos.devices.devicesabc import DevicesBase
from akkudoktoreos.devices.generic import HomeAppliance
from akkudoktoreos.devices.inverter import Inverter
from akkudoktoreos.prediction.self_consumption_probability import (
self_consumption_probability_interpolator,
)
from akkudoktoreos.utils.datetimeutil import to_duration
from akkudoktoreos.utils.logutil import get_logger
@@ -162,7 +165,11 @@ class Devices(SingletonMixin, DevicesBase):
akku: ClassVar[Battery] = Battery(provider_id="GenericBattery")
eauto: ClassVar[Battery] = Battery(provider_id="GenericBEV")
home_appliance: ClassVar[HomeAppliance] = HomeAppliance(provider_id="GenericDishWasher")
inverter: ClassVar[Inverter] = Inverter(akku=akku, provider_id="GenericInverter")
inverter: ClassVar[Inverter] = Inverter(
self_consumption_predictor=self_consumption_probability_interpolator,
akku=akku,
provider_id="GenericInverter",
)
def update_data(self) -> None:
"""Update device simulation data."""