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fix: load data for automatic optimization (#731)

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Automatic optimization used to take the adjusted load data even if there were no
measurements leading to 0 load values.

Split LoadAkkudoktor into LoadAkkudoktor and LoadAkkudoktorAdjusted. This allows
to select load data either purely from the load data database or load data additionally
adjusted by load measurements. Some value names have been adapted to denote
also the unit of a value.

For better load bug squashing the optimization solution data availability was
improved. For better data visbility prediction data can now be distinguished from
solution data in the generic optimization solution.

Some predictions that may be of interest to understand the solution were added.

Documentation was updated to resemble the addition load prediction provider and
the value name changes.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This commit is contained in:
Bobby Noelte
2025-11-01 00:49:11 +01:00
committed by GitHub
parent e3c5b758dd
commit b01bb1c61c
26 changed files with 515 additions and 227 deletions
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8
src/akkudoktoreos/optimization/genetic/geneticparams.py
View File

@@ -301,8 +301,8 @@ class GeneticOptimizationParameters(
# Retry
continue
try:
load_mean_adjusted = cls.prediction.key_to_array(
key="load_mean_adjusted",
loadforecast_power_w = cls.prediction.key_to_array(
key="loadforecast_power_w",
start_datetime=parameter_start_datetime,
end_datetime=parameter_end_datetime,
interval=interval,
@@ -319,7 +319,7 @@ class GeneticOptimizationParameters(
"provider": "LoadAkkudoktor",
"provider_settings": {
"LoadAkkudoktor": {
"loadakkudoktor_year_energy": "1000",
"loadakkudoktor_year_energy_kwh": "3000",
},
},
},
@@ -607,7 +607,7 @@ class GeneticOptimizationParameters(
pv_prognose_wh=pvforecast_ac_power,
strompreis_euro_pro_wh=elecprice_marketprice_wh,
einspeiseverguetung_euro_pro_wh=feed_in_tariff_wh,
gesamtlast=load_mean_adjusted,
gesamtlast=loadforecast_power_w,
preis_euro_pro_wh_akku=battery_lcos_kwh / 1000,
),
temperature_forecast=weather_temp_air,

132
src/akkudoktoreos/optimization/genetic/geneticsolution.py
View File

@@ -231,6 +231,7 @@ class GeneticSolution(GeneticParametersBaseModel):
config = get_config()
start_datetime = get_ems().start_datetime
interval_hours = 1
power_to_energy_per_interval_factor = 1.0
# --- Create index based on list length and interval ---
n_points = len(self.result.Kosten_Euro_pro_Stunde)
@@ -241,11 +242,9 @@ class GeneticSolution(GeneticParametersBaseModel):
)
end_datetime = start_datetime.add(hours=n_points)
# Fill data into dataframe with correct column names
# Fill solution into dataframe with correct column names
# - load_energy_wh: Load of all energy consumers in wh"
# - grid_energy_wh: Grid energy feed in (negative) or consumption (positive) in wh"
# - pv_prediction_energy_wh: PV energy prediction (positive) in wh"
# - elec_price_prediction_amt_kwh: Electricity price prediction in money per kwh"
# - costs_amt: Costs in money amount"
# - revenue_amt: Revenue in money amount"
# - losses_energy_wh: Energy losses in wh"
@@ -254,7 +253,7 @@ class GeneticSolution(GeneticParametersBaseModel):
# - <device-id>_soc_factor: State of charge of a battery/ electric vehicle device as factor of total capacity."
# - <device-id>_energy_wh: Energy consumption (positive) of a device in wh."
data = pd.DataFrame(
solution = pd.DataFrame(
{
"date_time": time_index,
"load_energy_wh": self.result.Last_Wh_pro_Stunde,
@@ -269,7 +268,7 @@ class GeneticSolution(GeneticParametersBaseModel):
)
# Add battery data
data["battery1_soc_factor"] = [v / 100 for v in self.result.akku_soc_pro_stunde]
solution["battery1_soc_factor"] = [v / 100 for v in self.result.akku_soc_pro_stunde]
operation: dict[str, list[float]] = {}
for hour, rate in enumerate(self.ac_charge):
if hour >= n_points:
@@ -290,13 +289,13 @@ class GeneticSolution(GeneticParametersBaseModel):
operation[mode_key].append(0.0)
operation[factor_key].append(0.0)
for key in operation.keys():
data[key] = operation[key]
solution[key] = operation[key]
# Add EV battery data
# Add EV battery solution
if self.eauto_obj:
if self.eautocharge_hours_float is None:
# Electric vehicle is full enough. No load times.
data[f"{self.eauto_obj.device_id}_soc_factor"] = [
solution[f"{self.eauto_obj.device_id}_soc_factor"] = [
self.eauto_obj.initial_soc_percentage / 100.0
] * n_points
# operation modes
@@ -305,13 +304,13 @@ class GeneticSolution(GeneticParametersBaseModel):
mode_key = f"{self.eauto_obj.device_id}_{mode.lower()}_op_mode"
factor_key = f"{self.eauto_obj.device_id}_{mode.lower()}_op_factor"
if mode == operation_mode:
data[mode_key] = [1.0] * n_points
data[factor_key] = [1.0] * n_points
solution[mode_key] = [1.0] * n_points
solution[factor_key] = [1.0] * n_points
else:
data[mode_key] = [0.0] * n_points
data[factor_key] = [0.0] * n_points
solution[mode_key] = [0.0] * n_points
solution[factor_key] = [0.0] * n_points
else:
data[f"{self.eauto_obj.device_id}_soc_factor"] = [
solution[f"{self.eauto_obj.device_id}_soc_factor"] = [
v / 100 for v in self.result.EAuto_SoC_pro_Stunde
]
operation = {}
@@ -334,18 +333,30 @@ class GeneticSolution(GeneticParametersBaseModel):
operation[mode_key].append(0.0)
operation[factor_key].append(0.0)
for key in operation.keys():
data[key] = operation[key]
solution[key] = operation[key]
# Add home appliance data
if self.washingstart:
data["homeappliance1_energy_wh"] = self.result.Home_appliance_wh_per_hour
solution["homeappliance1_energy_wh"] = self.result.Home_appliance_wh_per_hour
# Add important predictions that are not already available from the GenericSolution
prediction = get_prediction()
power_to_energy_per_interval_factor = 1.0
if "pvforecast_ac_power" in prediction.record_keys:
data["pv_prediction_energy_wh"] = (
prediction.key_to_array(
# Fill prediction into dataframe with correct column names
# - pvforecast_ac_energy_wh_energy_wh: PV energy prediction (positive) in wh
# - elec_price_amt_kwh: Electricity price prediction in money per kwh
# - weather_temp_air_celcius: Temperature in °C"
# - loadforecast_energy_wh: Load energy prediction in wh
# - loadakkudoktor_std_energy_wh: Load energy standard deviation prediction in wh
# - loadakkudoktor_mean_energy_wh: Load mean energy prediction in wh
prediction = pd.DataFrame(
{
"date_time": time_index,
},
index=time_index,
)
pred = get_prediction()
if "pvforecast_ac_power" in pred.record_keys:
prediction["pvforecast_ac_energy_wh"] = (
pred.key_to_array(
key="pvforecast_ac_power",
start_datetime=start_datetime,
end_datetime=end_datetime,
@@ -354,18 +365,82 @@ class GeneticSolution(GeneticParametersBaseModel):
)
* power_to_energy_per_interval_factor
).tolist()
if "weather_temp_air" in prediction.record_keys:
data["weather_temp_air"] = (
prediction.key_to_array(
key="weather_temp_air",
if "pvforecast_dc_power" in pred.record_keys:
prediction["pvforecast_dc_energy_wh"] = (
pred.key_to_array(
key="pvforecast_dc_power",
start_datetime=start_datetime,
end_datetime=end_datetime,
interval=to_duration(f"{interval_hours} hours"),
fill_method="linear",
)
* power_to_energy_per_interval_factor
).tolist()
if "elecprice_marketprice_wh" in pred.record_keys:
prediction["elec_price_amt_kwh"] = (
pred.key_to_array(
key="elecprice_marketprice_wh",
start_datetime=start_datetime,
end_datetime=end_datetime,
interval=to_duration(f"{interval_hours} hours"),
fill_method="ffill",
)
* 1000
).tolist()
if "feed_in_tariff_wh" in pred.record_keys:
prediction["feed_in_tariff_amt_kwh"] = (
pred.key_to_array(
key="feed_in_tariff_wh",
start_datetime=start_datetime,
end_datetime=end_datetime,
interval=to_duration(f"{interval_hours} hours"),
fill_method="linear",
)
* 1000
).tolist()
if "weather_temp_air" in pred.record_keys:
prediction["weather_air_temp_celcius"] = pred.key_to_array(
key="weather_temp_air",
start_datetime=start_datetime,
end_datetime=end_datetime,
interval=to_duration(f"{interval_hours} hours"),
fill_method="linear",
).tolist()
if "loadforecast_power_w" in pred.record_keys:
prediction["loadforecast_energy_wh"] = (
pred.key_to_array(
key="loadforecast_power_w",
start_datetime=start_datetime,
end_datetime=end_datetime,
interval=to_duration(f"{interval_hours} hours"),
fill_method="linear",
)
* power_to_energy_per_interval_factor
).tolist()
if "loadakkudoktor_std_power_w" in pred.record_keys:
prediction["loadakkudoktor_std_energy_wh"] = (
pred.key_to_array(
key="loadakkudoktor_std_power_w",
start_datetime=start_datetime,
end_datetime=end_datetime,
interval=to_duration(f"{interval_hours} hours"),
fill_method="linear",
)
* power_to_energy_per_interval_factor
).tolist()
if "loadakkudoktor_mean_power_w" in pred.record_keys:
prediction["loadakkudoktor_mean_energy_wh"] = (
pred.key_to_array(
key="loadakkudoktor_mean_power_w",
start_datetime=start_datetime,
end_datetime=end_datetime,
interval=to_duration(f"{interval_hours} hours"),
fill_method="linear",
)
* power_to_energy_per_interval_factor
).tolist()
solution = OptimizationSolution(
optimization_solution = OptimizationSolution(
id=f"optimization-genetic@{to_datetime(as_string=True)}",
generated_at=to_datetime(),
comment="Optimization solution derived from GeneticSolution.",
@@ -374,10 +449,11 @@ class GeneticSolution(GeneticParametersBaseModel):
total_losses_energy_wh=self.result.Gesamt_Verluste,
total_revenues_amt=self.result.Gesamteinnahmen_Euro,
total_costs_amt=self.result.Gesamtkosten_Euro,
data=PydanticDateTimeDataFrame.from_dataframe(data),
prediction=PydanticDateTimeDataFrame.from_dataframe(prediction),
solution=PydanticDateTimeDataFrame.from_dataframe(solution),
)
return solution
return optimization_solution
def energy_management_plan(self) -> EnergyManagementPlan:
"""Provide the genetic solution as an energy management plan."""

19
src/akkudoktoreos/optimization/optimization.py
View File

@@ -110,13 +110,24 @@ class OptimizationSolution(PydanticBaseModel):
total_costs_amt: float = Field(description="The total costs [money amount].")
data: PydanticDateTimeDataFrame = Field(
prediction: PydanticDateTimeDataFrame = Field(
description=(
"Datetime data frame with time series optimization data per optimization interval:"
"Datetime data frame with time series prediction data per optimization interval:"
"- pv_energy_wh: PV energy prediction (positive) in wh"
"- elec_price_amt_kwh: Electricity price prediction in money per kwh"
"- feed_in_tariff_amt_kwh: Feed in tariff prediction in money per kwh"
"- weather_temp_air_celcius: Temperature in °C"
"- loadforecast_energy_wh: Load mean energy prediction in wh"
"- loadakkudoktor_std_energy_wh: Load energy standard deviation prediction in wh"
"- loadakkudoktor_mean_energy_wh: Load mean energy prediction in wh"
)
)
solution: PydanticDateTimeDataFrame = Field(
description=(
"Datetime data frame with time series solution data per optimization interval:"
"- load_energy_wh: Load of all energy consumers in wh"
"- grid_energy_wh: Grid energy feed in (negative) or consumption (positive) in wh"
"- pv_prediction_energy_wh: PV energy prediction (positive) in wh"
"- elec_price_prediction_amt_kwh: Electricity price prediction in money per kwh"
"- costs_amt: Costs in money amount"
"- revenue_amt: Revenue in money amount"
"- losses_energy_wh: Energy losses in wh"