mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-11-05 01:06:21 +00:00
feat: improve config backup and update and revert (#737)
Some checks failed
docker-build / platform-excludes (push) Has been cancelled
docker-build / build (push) Has been cancelled
docker-build / merge (push) Has been cancelled
pre-commit / pre-commit (push) Has been cancelled
Run Pytest on Pull Request / test (push) Has been cancelled
Some checks failed
docker-build / platform-excludes (push) Has been cancelled
docker-build / build (push) Has been cancelled
docker-build / merge (push) Has been cancelled
pre-commit / pre-commit (push) Has been cancelled
Run Pytest on Pull Request / test (push) Has been cancelled
Improve the backup of the EOS configuration on configuration migration from another version. Backup files now get a backup id based on date and time. Add the configuration backup listing and the revert to the backup to the EOS api. Add revert to backup to the EOSdash admin tab. Improve documentation about install, update and revert of EOS versions. Add EOS execution profiling to make commands and to test description in the development guideline. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This commit is contained in:
Bobby Noelte
GitHub
526
tests/single_test_optimization.py
Executable file
526
tests/single_test_optimization.py
Executable file
@@ -0,0 +1,526 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import cProfile
|
||||
import json
|
||||
import pstats
|
||||
import sys
|
||||
import time
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
from loguru import logger
|
||||
|
||||
from akkudoktoreos.config.config import get_config
|
||||
from akkudoktoreos.core.ems import get_ems
|
||||
from akkudoktoreos.core.emsettings import EnergyManagementMode
|
||||
from akkudoktoreos.optimization.genetic.geneticparams import (
|
||||
GeneticOptimizationParameters,
|
||||
)
|
||||
from akkudoktoreos.prediction.prediction import get_prediction
|
||||
from akkudoktoreos.utils.datetimeutil import to_datetime
|
||||
|
||||
config_eos = get_config()
|
||||
prediction_eos = get_prediction()
|
||||
ems_eos = get_ems()
|
||||
|
||||
|
||||
def prepare_optimization_real_parameters() -> GeneticOptimizationParameters:
|
||||
"""Prepare and return optimization parameters with real world data.
|
||||
|
||||
Returns:
|
||||
GeneticOptimizationParameters: Configured optimization parameters
|
||||
"""
|
||||
# Make a config
|
||||
settings = {
|
||||
"general": {
|
||||
"latitude": 52.52,
|
||||
"longitude": 13.405,
|
||||
},
|
||||
"prediction": {
|
||||
"hours": 48,
|
||||
"historic_hours": 24,
|
||||
},
|
||||
"optimization": {
|
||||
"horizon_hours": 24,
|
||||
"interval": 3600,
|
||||
"genetic": {
|
||||
"individuals": 300,
|
||||
"generations": 400,
|
||||
"seed": None,
|
||||
"penalties": {
|
||||
"ev_soc_miss": 10,
|
||||
},
|
||||
},
|
||||
},
|
||||
# PV Forecast
|
||||
"pvforecast": {
|
||||
"provider": "PVForecastAkkudoktor",
|
||||
"planes": [
|
||||
{
|
||||
"peakpower": 5.0,
|
||||
"surface_azimuth": -10,
|
||||
"surface_tilt": 7,
|
||||
"userhorizon": [20, 27, 22, 20],
|
||||
"inverter_paco": 10000,
|
||||
},
|
||||
{
|
||||
"peakpower": 4.8,
|
||||
"surface_azimuth": -90,
|
||||
"surface_tilt": 7,
|
||||
"userhorizon": [30, 30, 30, 50],
|
||||
"inverter_paco": 10000,
|
||||
},
|
||||
{
|
||||
"peakpower": 1.4,
|
||||
"surface_azimuth": -40,
|
||||
"surface_tilt": 60,
|
||||
"userhorizon": [60, 30, 0, 30],
|
||||
"inverter_paco": 2000,
|
||||
},
|
||||
{
|
||||
"peakpower": 1.6,
|
||||
"surface_azimuth": 5,
|
||||
"surface_tilt": 45,
|
||||
"userhorizon": [45, 25, 30, 60],
|
||||
"inverter_paco": 1400,
|
||||
},
|
||||
],
|
||||
},
|
||||
# Weather Forecast
|
||||
"weather": {
|
||||
"provider": "ClearOutside",
|
||||
},
|
||||
# Electricity Price Forecast
|
||||
"elecprice": {
|
||||
"provider": "ElecPriceAkkudoktor",
|
||||
},
|
||||
# Load Forecast
|
||||
"load": {
|
||||
"provider": "LoadAkkudoktor",
|
||||
"provider_settings": {
|
||||
"LoadAkkudoktor": {
|
||||
"loadakkudoktor_year_energy_kwh": 5000, # Energy consumption per year in kWh
|
||||
},
|
||||
},
|
||||
},
|
||||
# -- Simulations --
|
||||
# Assure we have charge rates for the EV
|
||||
"devices": {
|
||||
"max_electric_vehicles": 1,
|
||||
"electric_vehicles": [
|
||||
{
|
||||
"charge_rates": [
|
||||
0.0,
|
||||
6.0 / 16.0,
|
||||
8.0 / 16.0,
|
||||
10.0 / 16.0,
|
||||
12.0 / 16.0,
|
||||
14.0 / 16.0,
|
||||
1.0,
|
||||
],
|
||||
},
|
||||
],
|
||||
},
|
||||
}
|
||||
|
||||
# Update/ set configuration
|
||||
config_eos.merge_settings_from_dict(settings)
|
||||
|
||||
# Get current prediction data for optimization run
|
||||
ems_eos.set_start_datetime()
|
||||
print(
|
||||
f"Real data prediction from {prediction_eos.ems_start_datetime} to {prediction_eos.end_datetime}"
|
||||
)
|
||||
prediction_eos.update_data()
|
||||
|
||||
# PV Forecast (in W)
|
||||
pv_forecast = prediction_eos.key_to_array(
|
||||
key="pvforecast_ac_power",
|
||||
start_datetime=prediction_eos.ems_start_datetime,
|
||||
end_datetime=prediction_eos.end_datetime,
|
||||
)
|
||||
print(f"pv_forecast: {pv_forecast}")
|
||||
|
||||
# Temperature Forecast (in degree C)
|
||||
temperature_forecast = prediction_eos.key_to_array(
|
||||
key="weather_temp_air",
|
||||
start_datetime=prediction_eos.ems_start_datetime,
|
||||
end_datetime=prediction_eos.end_datetime,
|
||||
)
|
||||
print(f"temperature_forecast: {temperature_forecast}")
|
||||
|
||||
# Electricity Price (in Euro per Wh)
|
||||
strompreis_euro_pro_wh = prediction_eos.key_to_array(
|
||||
key="elecprice_marketprice_wh",
|
||||
start_datetime=prediction_eos.ems_start_datetime,
|
||||
end_datetime=prediction_eos.end_datetime,
|
||||
)
|
||||
print(f"strompreis_euro_pro_wh: {strompreis_euro_pro_wh}")
|
||||
|
||||
# Overall System Load (in W)
|
||||
gesamtlast = prediction_eos.key_to_array(
|
||||
key="load_mean",
|
||||
start_datetime=prediction_eos.ems_start_datetime,
|
||||
end_datetime=prediction_eos.end_datetime,
|
||||
)
|
||||
print(f"gesamtlast: {gesamtlast}")
|
||||
|
||||
# Start Solution (binary)
|
||||
start_solution = None
|
||||
print(f"start_solution: {start_solution}")
|
||||
|
||||
# Define parameters for the optimization problem
|
||||
return GeneticOptimizationParameters(
|
||||
**{
|
||||
"ems": {
|
||||
"preis_euro_pro_wh_akku": 0e-05,
|
||||
"einspeiseverguetung_euro_pro_wh": 7e-05,
|
||||
"gesamtlast": gesamtlast,
|
||||
"pv_prognose_wh": pv_forecast,
|
||||
"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
|
||||
},
|
||||
"pv_akku": {
|
||||
"device_id": "battery 1",
|
||||
"capacity_wh": 26400,
|
||||
"initial_soc_percentage": 15,
|
||||
"min_soc_percentage": 15,
|
||||
},
|
||||
"inverter": {
|
||||
"device_id": "inverter 1",
|
||||
"max_power_wh": 10000,
|
||||
"battery_id": "battery 1",
|
||||
},
|
||||
"eauto": {
|
||||
"device_id": "electric vehicle 1",
|
||||
"min_soc_percentage": 50,
|
||||
"capacity_wh": 60000,
|
||||
"charging_efficiency": 0.95,
|
||||
"max_charge_power_w": 11040,
|
||||
"initial_soc_percentage": 5,
|
||||
},
|
||||
"temperature_forecast": temperature_forecast,
|
||||
"start_solution": start_solution,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def prepare_optimization_parameters() -> GeneticOptimizationParameters:
|
||||
"""Prepare and return optimization parameters with predefined data.
|
||||
|
||||
Returns:
|
||||
GeneticOptimizationParameters: Configured optimization parameters
|
||||
"""
|
||||
# Initialize the optimization problem using the default configuration
|
||||
config_eos.merge_settings_from_dict(
|
||||
{
|
||||
"prediction": {"hours": 48},
|
||||
"optimization": {
|
||||
"horizon_hours": 48,
|
||||
"interval": 3600,
|
||||
"genetic": {
|
||||
"individuals": 300,
|
||||
"generations": 400,
|
||||
"seed": None,
|
||||
"penalties": {
|
||||
"ev_soc_miss": 10,
|
||||
},
|
||||
},
|
||||
},
|
||||
# Assure we have charge rates for the EV
|
||||
"devices": {
|
||||
"max_electric_vehicles": 1,
|
||||
"electric_vehicles": [
|
||||
{
|
||||
"device_id": "Default EV",
|
||||
"charge_rates": [
|
||||
0.0,
|
||||
6.0 / 16.0,
|
||||
8.0 / 16.0,
|
||||
10.0 / 16.0,
|
||||
12.0 / 16.0,
|
||||
14.0 / 16.0,
|
||||
1.0,
|
||||
],
|
||||
},
|
||||
],
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
# PV Forecast (in W)
|
||||
pv_forecast = np.zeros(48)
|
||||
pv_forecast[12] = 5000
|
||||
|
||||
# Temperature Forecast (in degree C)
|
||||
temperature_forecast = [
|
||||
18.3,
|
||||
17.8,
|
||||
16.9,
|
||||
16.2,
|
||||
15.6,
|
||||
15.1,
|
||||
14.6,
|
||||
14.2,
|
||||
14.3,
|
||||
14.8,
|
||||
15.7,
|
||||
16.7,
|
||||
17.4,
|
||||
18.0,
|
||||
18.6,
|
||||
19.2,
|
||||
19.1,
|
||||
18.7,
|
||||
18.5,
|
||||
17.7,
|
||||
16.2,
|
||||
14.6,
|
||||
13.6,
|
||||
13.0,
|
||||
12.6,
|
||||
12.2,
|
||||
11.7,
|
||||
11.6,
|
||||
11.3,
|
||||
11.0,
|
||||
10.7,
|
||||
10.2,
|
||||
11.4,
|
||||
14.4,
|
||||
16.4,
|
||||
18.3,
|
||||
19.5,
|
||||
20.7,
|
||||
21.9,
|
||||
22.7,
|
||||
23.1,
|
||||
23.1,
|
||||
22.8,
|
||||
21.8,
|
||||
20.2,
|
||||
19.1,
|
||||
18.0,
|
||||
17.4,
|
||||
]
|
||||
|
||||
# Electricity Price (in Euro per Wh)
|
||||
strompreis_euro_pro_wh = np.full(48, 0.001)
|
||||
strompreis_euro_pro_wh[0:10] = 0.00001
|
||||
strompreis_euro_pro_wh[11:15] = 0.00005
|
||||
strompreis_euro_pro_wh[20] = 0.00001
|
||||
|
||||
# Overall System Load (in W)
|
||||
gesamtlast = [
|
||||
676.71,
|
||||
876.19,
|
||||
527.13,
|
||||
468.88,
|
||||
531.38,
|
||||
517.95,
|
||||
483.15,
|
||||
472.28,
|
||||
1011.68,
|
||||
995.00,
|
||||
1053.07,
|
||||
1063.91,
|
||||
1320.56,
|
||||
1132.03,
|
||||
1163.67,
|
||||
1176.82,
|
||||
1216.22,
|
||||
1103.78,
|
||||
1129.12,
|
||||
1178.71,
|
||||
1050.98,
|
||||
988.56,
|
||||
912.38,
|
||||
704.61,
|
||||
516.37,
|
||||
868.05,
|
||||
694.34,
|
||||
608.79,
|
||||
556.31,
|
||||
488.89,
|
||||
506.91,
|
||||
804.89,
|
||||
1141.98,
|
||||
1056.97,
|
||||
992.46,
|
||||
1155.99,
|
||||
827.01,
|
||||
1257.98,
|
||||
1232.67,
|
||||
871.26,
|
||||
860.88,
|
||||
1158.03,
|
||||
1222.72,
|
||||
1221.04,
|
||||
949.99,
|
||||
987.01,
|
||||
733.99,
|
||||
592.97,
|
||||
]
|
||||
|
||||
# Start Solution (binary)
|
||||
start_solution = None
|
||||
|
||||
# Define parameters for the optimization problem
|
||||
return GeneticOptimizationParameters(
|
||||
**{
|
||||
"ems": {
|
||||
"preis_euro_pro_wh_akku": 0e-05,
|
||||
"einspeiseverguetung_euro_pro_wh": 7e-05,
|
||||
"gesamtlast": gesamtlast,
|
||||
"pv_prognose_wh": pv_forecast,
|
||||
"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
|
||||
},
|
||||
"pv_akku": {
|
||||
"device_id": "battery 1",
|
||||
"capacity_wh": 26400,
|
||||
"initial_soc_percentage": 15,
|
||||
"min_soc_percentage": 15,
|
||||
},
|
||||
"inverter": {
|
||||
"device_id": "inverter 1",
|
||||
"max_power_wh": 10000,
|
||||
"battery_id": "battery 1",
|
||||
},
|
||||
"eauto": {
|
||||
"device_id": "electric vehicle 1",
|
||||
"min_soc_percentage": 50,
|
||||
"capacity_wh": 60000,
|
||||
"charging_efficiency": 0.95,
|
||||
"max_charge_power_w": 11040,
|
||||
"initial_soc_percentage": 5,
|
||||
},
|
||||
"temperature_forecast": temperature_forecast,
|
||||
"start_solution": start_solution,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def run_optimization(
|
||||
real_world: bool, start_hour: int, verbose: bool, seed: int, parameters_file: str, ngen: int
|
||||
) -> Any:
|
||||
"""Run the optimization problem.
|
||||
|
||||
Args:
|
||||
start_hour (int, optional): Starting hour for optimization. Defaults to 0.
|
||||
verbose (bool, optional): Whether to print verbose output. Defaults to False.
|
||||
|
||||
Returns:
|
||||
dict: Optimization result as a dictionary
|
||||
"""
|
||||
# Prepare parameters
|
||||
if parameters_file:
|
||||
with open(parameters_file, "r") as f:
|
||||
parameters = GeneticOptimizationParameters(**json.load(f))
|
||||
elif real_world:
|
||||
parameters = prepare_optimization_real_parameters()
|
||||
else:
|
||||
parameters = prepare_optimization_parameters()
|
||||
logger.info("Optimization Parameters:")
|
||||
logger.info(parameters.model_dump_json(indent=4))
|
||||
|
||||
if start_hour is None:
|
||||
start_datetime = None
|
||||
else:
|
||||
start_datetime = to_datetime().set(hour=start_hour)
|
||||
|
||||
asyncio.run(
|
||||
ems_eos.run(
|
||||
start_datetime=start_datetime,
|
||||
mode=EnergyManagementMode.OPTIMIZATION,
|
||||
genetic_parameters=parameters,
|
||||
genetic_individuals=ngen,
|
||||
genetic_seed=seed,
|
||||
)
|
||||
)
|
||||
|
||||
solution = ems_eos.genetic_solution()
|
||||
if solution is None:
|
||||
return None
|
||||
return solution.model_dump_json()
|
||||
|
||||
|
||||
def main():
|
||||
"""Main function to run the optimization script with optional profiling."""
|
||||
parser = argparse.ArgumentParser(description="Run Energy Optimization Simulation")
|
||||
parser.add_argument("--profile", action="store_true", help="Enable performance profiling")
|
||||
parser.add_argument(
|
||||
"--verbose", action="store_true", help="Enable verbose output during optimization"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--real-world", action="store_true", help="Use real world data for predictions"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--start-hour", type=int, default=0, help="Starting hour for optimization (default: 0)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--parameters-file",
|
||||
type=str,
|
||||
default="",
|
||||
help="Load optimization parameters from json file (default: unset)",
|
||||
)
|
||||
parser.add_argument("--seed", type=int, default=42, help="Use fixed random seed (default: 42)")
|
||||
parser.add_argument(
|
||||
"--ngen",
|
||||
type=int,
|
||||
default=400,
|
||||
help="Number of generations during optimization process (default: 400)",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.profile:
|
||||
# Run with profiling
|
||||
profiler = cProfile.Profile()
|
||||
try:
|
||||
result = profiler.runcall(
|
||||
run_optimization,
|
||||
real_world=args.real_world,
|
||||
start_hour=args.start_hour,
|
||||
verbose=args.verbose,
|
||||
seed=args.seed,
|
||||
parameters_file=args.parameters_file,
|
||||
ngen=args.ngen,
|
||||
)
|
||||
# Print profiling statistics
|
||||
stats = pstats.Stats(profiler)
|
||||
stats.strip_dirs().sort_stats("cumulative").print_stats(200)
|
||||
# Print result
|
||||
if args.verbose:
|
||||
print("\nOptimization Result:")
|
||||
print(result)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error during optimization: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
else:
|
||||
# Run without profiling
|
||||
try:
|
||||
start_time = time.time()
|
||||
result = run_optimization(
|
||||
real_world=args.real_world,
|
||||
start_hour=args.start_hour,
|
||||
verbose=args.verbose,
|
||||
seed=args.seed,
|
||||
parameters_file=args.parameters_file,
|
||||
ngen=args.ngen,
|
||||
)
|
||||
end_time = time.time()
|
||||
elapsed_time = end_time - start_time
|
||||
if args.verbose:
|
||||
print(f"\nElapsed time: {elapsed_time:.4f} seconds.")
|
||||
print("\nOptimization Result:")
|
||||
print(result)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error during optimization: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
229
tests/single_test_prediction.py
Normal file
229
tests/single_test_prediction.py
Normal file
@@ -0,0 +1,229 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import argparse
|
||||
import cProfile
|
||||
import pstats
|
||||
import sys
|
||||
import time
|
||||
|
||||
from akkudoktoreos.config.config import get_config
|
||||
from akkudoktoreos.prediction.prediction import get_prediction
|
||||
|
||||
config_eos = get_config()
|
||||
prediction_eos = get_prediction()
|
||||
|
||||
|
||||
def config_pvforecast() -> dict:
|
||||
"""Configure settings for PV forecast."""
|
||||
settings = {
|
||||
"general": {
|
||||
"latitude": 52.52,
|
||||
"longitude": 13.405,
|
||||
},
|
||||
"prediction": {
|
||||
"hours": 48,
|
||||
"historic_hours": 24,
|
||||
},
|
||||
"pvforecast": {
|
||||
"provider": "PVForecastAkkudoktor",
|
||||
"planes": [
|
||||
{
|
||||
"peakpower": 5.0,
|
||||
"surface_azimuth": -10,
|
||||
"surface_tilt": 7,
|
||||
"userhorizon": [20, 27, 22, 20],
|
||||
"inverter_paco": 10000,
|
||||
},
|
||||
{
|
||||
"peakpower": 4.8,
|
||||
"surface_azimuth": -90,
|
||||
"surface_tilt": 7,
|
||||
"userhorizon": [30, 30, 30, 50],
|
||||
"inverter_paco": 10000,
|
||||
},
|
||||
{
|
||||
"peakpower": 1.4,
|
||||
"surface_azimuth": -40,
|
||||
"surface_tilt": 60,
|
||||
"userhorizon": [60, 30, 0, 30],
|
||||
"inverter_paco": 2000,
|
||||
},
|
||||
{
|
||||
"peakpower": 1.6,
|
||||
"surface_azimuth": 5,
|
||||
"surface_tilt": 45,
|
||||
"userhorizon": [45, 25, 30, 60],
|
||||
"inverter_paco": 1400,
|
||||
},
|
||||
],
|
||||
},
|
||||
}
|
||||
return settings
|
||||
|
||||
|
||||
def config_weather() -> dict:
|
||||
"""Configure settings for weather forecast."""
|
||||
settings = {
|
||||
"general": {
|
||||
"latitude": 52.52,
|
||||
"longitude": 13.405,
|
||||
},
|
||||
"prediction": {
|
||||
"hours": 48,
|
||||
"historic_hours": 24,
|
||||
},
|
||||
"weather": dict(),
|
||||
}
|
||||
return settings
|
||||
|
||||
|
||||
def config_elecprice() -> dict:
|
||||
"""Configure settings for electricity price forecast."""
|
||||
settings = {
|
||||
"general": {
|
||||
"latitude": 52.52,
|
||||
"longitude": 13.405,
|
||||
},
|
||||
"prediction": {
|
||||
"hours": 48,
|
||||
"historic_hours": 24,
|
||||
},
|
||||
"elecprice": dict(),
|
||||
}
|
||||
return settings
|
||||
|
||||
|
||||
def config_feedintarifffixed() -> dict:
|
||||
"""Configure settings for feed in tariff forecast."""
|
||||
settings = {
|
||||
"general": {
|
||||
"latitude": 52.52,
|
||||
"longitude": 13.405,
|
||||
},
|
||||
"prediction": {
|
||||
"hours": 48,
|
||||
"historic_hours": 24,
|
||||
},
|
||||
"feedintariff": dict(),
|
||||
}
|
||||
return settings
|
||||
|
||||
|
||||
def config_load() -> dict:
|
||||
"""Configure settings for load forecast."""
|
||||
settings = {
|
||||
"general": {
|
||||
"latitude": 52.52,
|
||||
"longitude": 13.405,
|
||||
},
|
||||
"prediction": {
|
||||
"hours": 48,
|
||||
"historic_hours": 24,
|
||||
},
|
||||
}
|
||||
return settings
|
||||
|
||||
|
||||
def run_prediction(provider_id: str, verbose: bool = False) -> str:
|
||||
"""Run the prediction.
|
||||
|
||||
Args:
|
||||
provider_id (str): ID of prediction provider.
|
||||
verbose (bool, optional): Whether to print verbose output. Defaults to False.
|
||||
|
||||
Returns:
|
||||
dict: Prediction result as a dictionary
|
||||
"""
|
||||
# Initialize the oprediction
|
||||
config_eos = get_config()
|
||||
prediction_eos = get_prediction()
|
||||
if provider_id in ("PVForecastAkkudoktor",):
|
||||
settings = config_pvforecast()
|
||||
forecast = "pvforecast"
|
||||
elif provider_id in ("BrightSky", "ClearOutside"):
|
||||
settings = config_weather()
|
||||
forecast = "weather"
|
||||
elif provider_id in ("ElecPriceAkkudoktor",):
|
||||
settings = config_elecprice()
|
||||
forecast = "elecprice"
|
||||
elif provider_id in ("FeedInTariffFixed",):
|
||||
settings = config_feedintarifffixed()
|
||||
forecast = "feedintariff"
|
||||
elif provider_id in ("LoadAkkudoktor",):
|
||||
settings = config_load()
|
||||
forecast = "loadforecast"
|
||||
settings["load"]["LoadAkkudoktor"]["loadakkudoktor_year_energy_wh"] = 1000
|
||||
else:
|
||||
raise ValueError(f"Unknown provider '{provider_id}'.")
|
||||
settings[forecast]["provider"] = provider_id
|
||||
config_eos.merge_settings_from_dict(settings)
|
||||
|
||||
provider = prediction_eos.provider_by_id(provider_id)
|
||||
|
||||
prediction_eos.update_data()
|
||||
|
||||
# Return result of prediction
|
||||
if verbose:
|
||||
print(f"\nProvider ID: {provider.provider_id()}")
|
||||
print("----------")
|
||||
print("\nSettings\n----------")
|
||||
print(settings)
|
||||
print("\nProvider\n----------")
|
||||
print(f"elecprice.provider: {config_eos.elecprice.provider}")
|
||||
print(f"feedintariff.provider: {config_eos.feedintariff.provider}")
|
||||
print(f"load.provider: {config_eos.load.provider}")
|
||||
print(f"pvforecast.provider: {config_eos.pvforecast.provider}")
|
||||
print(f"weather.provider: {config_eos.weather.provider}")
|
||||
print(f"enabled: {provider.enabled()}")
|
||||
for key in provider.record_keys:
|
||||
print(f"\n{key}\n----------")
|
||||
print(f"Array: {provider.key_to_array(key)}")
|
||||
return provider.model_dump_json(indent=4)
|
||||
|
||||
|
||||
def main():
|
||||
"""Main function to run the optimization script with optional profiling."""
|
||||
parser = argparse.ArgumentParser(description="Run Prediction")
|
||||
parser.add_argument("--profile", action="store_true", help="Enable performance profiling")
|
||||
parser.add_argument(
|
||||
"--verbose", action="store_true", help="Enable verbose output during prediction"
|
||||
)
|
||||
parser.add_argument("--provider-id", type=str, default=0, help="Provider ID of prediction")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.profile:
|
||||
# Run with profiling
|
||||
profiler = cProfile.Profile()
|
||||
try:
|
||||
result = profiler.runcall(
|
||||
run_prediction, provider_id=args.provider_id, verbose=args.verbose
|
||||
)
|
||||
# Print profiling statistics
|
||||
stats = pstats.Stats(profiler)
|
||||
stats.strip_dirs().sort_stats("cumulative").print_stats(200)
|
||||
# Print result
|
||||
print("\nPrediction Result:")
|
||||
print(result)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error during prediction: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
else:
|
||||
# Run without profiling
|
||||
try:
|
||||
start_time = time.time()
|
||||
result = run_prediction(provider_id=args.provider_id, verbose=args.verbose)
|
||||
end_time = time.time()
|
||||
elapsed_time = end_time - start_time
|
||||
print(f"\nElapsed time: {elapsed_time:.4f} seconds.")
|
||||
print("\nPrediction Result:")
|
||||
print(result)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error during prediction: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user