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Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
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@@ -78,6 +78,9 @@ class SimulationResult(BaseModel):
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akku_soc_pro_stunde: list[Optional[float]] = Field(
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description="The state of charge of the battery (not the EV) in percentage per hour."
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)
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Electricity_price: list[Optional[float]] = Field(
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description="Used Electricity Price, including predictions"
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)
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@field_validator(
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"Last_Wh_pro_Stunde",
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@@ -89,6 +92,7 @@ class SimulationResult(BaseModel):
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"EAuto_SoC_pro_Stunde",
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"Verluste_Pro_Stunde",
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"Home_appliance_wh_per_hour",
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"Electricity_price",
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mode="before",
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)
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def convert_numpy(cls, field: Any) -> Any:
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@@ -171,6 +175,7 @@ class EnergieManagementSystem:
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eauto_soc_pro_stunde = np.full((total_hours), np.nan)
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verluste_wh_pro_stunde = np.full((total_hours), np.nan)
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home_appliance_wh_per_hour = np.full((total_hours), np.nan)
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electricity_price_per_hour = np.full((total_hours), np.nan)
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# Set initial state
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akku_soc_pro_stunde[0] = self.akku.ladezustand_in_prozent()
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@@ -222,7 +227,7 @@ class EnergieManagementSystem:
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netzbezug_wh_pro_stunde[stunde_since_now] = netzbezug
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verluste_wh_pro_stunde[stunde_since_now] += verluste
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last_wh_pro_stunde[stunde_since_now] = verbrauch
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electricity_price_per_hour[stunde_since_now] = self.strompreis_euro_pro_wh[stunde]
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# Financial calculations
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kosten_euro_pro_stunde[stunde_since_now] = (
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netzbezug * self.strompreis_euro_pro_wh[stunde]
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@@ -252,6 +257,6 @@ class EnergieManagementSystem:
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"Verluste_Pro_Stunde": verluste_wh_pro_stunde,
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"Gesamt_Verluste": np.nansum(verluste_wh_pro_stunde),
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"Home_appliance_wh_per_hour": home_appliance_wh_per_hour,
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"Electricity_price": electricity_price_per_hour,
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}
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return out
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