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Improve genetic optimizer seeding

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This commit is contained in:
Andreas
2026-07-16 09:56:50 +02:00
parent d4056af0f6
commit b0b437f1d7
8 changed files with 1152 additions and 793 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/test_genetic_seeding.py
+113
View File
@@ -0,0 +1,113 @@
from types import SimpleNamespace
from unittest.mock import patch
import numpy as np
import pytest
from deap import creator
from akkudoktoreos.config.config import ConfigEOS
from akkudoktoreos.core.coreabc import get_ems
from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
from akkudoktoreos.utils.datetimeutil import to_datetime
def _configure_hourly_grid(config_eos: ConfigEOS, *, start_hour: int = 0) -> None:
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 3600},
}
)
get_ems(init=True).set_start_datetime(to_datetime().set(hour=start_hour, minute=0))
def test_ev_repair_is_resimulated_before_fitness_assignment(config_eos: ConfigEOS):
_configure_hourly_grid(config_eos)
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = True
opt.ev_possible_charge_values = [0.0, 1.0]
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
individual = creator.Individual([0] * opt.total_slots + [1] * opt.total_slots)
first_result = {
"Gesamtbilanz_Euro": 10.0,
"Gesamt_Verluste": 0.0,
"EAuto_SoC_pro_Stunde": np.full(opt.total_slots, 100.0),
}
repaired_result = {
"Gesamtbilanz_Euro": 1.0,
"Gesamt_Verluste": 0.0,
"EAuto_SoC_pro_Stunde": np.full(opt.total_slots, 100.0),
}
parameters = SimpleNamespace(
ems=SimpleNamespace(preis_euro_pro_wh_akku=0.0),
eauto=None,
)
with patch.object(opt, "evaluate_inner", side_effect=[first_result, repaired_result]) as evaluate:
fitness = opt.evaluate(individual, parameters, start_hour=0, worst_case=False) # type: ignore[arg-type]
assert evaluate.call_count == 2
assert fitness == pytest.approx((1.0,))
assert individual[opt.total_slots :] == [0] * opt.total_slots
def test_mutated_warm_start_neighbors_keep_elapsed_slots(config_eos: ConfigEOS):
_configure_hourly_grid(config_eos, start_hour=10)
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
opt.setup_deap_environment({"home_appliance": 0}, start_hour=10)
start_solution = [0] * opt.total_slots
neighbors = opt._mutated_warm_start_neighbors(start_solution, count=5)
assert len(neighbors) == 5
assert len({tuple(neighbor) for neighbor in neighbors}) == 5
assert all(neighbor[:10] == start_solution[:10] for neighbor in neighbors)
assert all(neighbor != start_solution for neighbor in neighbors)
def test_educated_guesses_encode_high_price_direct_marketing(config_eos: ConfigEOS):
_configure_hourly_grid(config_eos)
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
opt.optimize_dc_charge = True
opt.optimize_battery_grid_export = True
opt.bat_possible_charge_values = [1.0]
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
slots = opt.total_slots
opt.simulation.elect_price_hourly = np.linspace(0.0001, 0.0004, slots)
opt.simulation.elect_revenue_per_hour_arr = np.linspace(0.00001, 0.0003, slots)
opt.simulation.pv_prediction_wh = np.full(slots, 1000.0)
opt.simulation.load_energy_array = np.full(slots, 500.0)
guesses = opt._educated_guess_individuals()
dc_allowed_state = 4
export_state = 5
assert len(guesses) >= 4
assert all(len(guess) == slots for guess in guesses)
assert any(guess[0] == dc_allowed_state for guess in guesses)
assert any(guess[-1] == export_state for guess in guesses)
def test_flat_feed_in_tariff_does_not_seed_direct_marketing(config_eos: ConfigEOS):
_configure_hourly_grid(config_eos)
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
opt.optimize_dc_charge = True
opt.optimize_battery_grid_export = True
opt.bat_possible_charge_values = [1.0]
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
slots = opt.total_slots
opt.simulation.elect_price_hourly = np.linspace(0.0001, 0.0004, slots)
opt.simulation.elect_revenue_per_hour_arr = np.full(slots, 0.00005)
opt.simulation.pv_prediction_wh = np.full(slots, 1000.0)
opt.simulation.load_energy_array = np.full(slots, 500.0)
guesses = opt._educated_guess_individuals()
export_state = 5
assert all(export_state not in guess for guess in guesses)
tests/test_geneticoptimize.py
+6 -2
View File
@@ -1,4 +1,5 @@
import json
from datetime import datetime
from pathlib import Path
from typing import Any
from unittest.mock import patch
@@ -32,7 +33,10 @@ def compare_dict(actual: dict[str, Any], expected: dict[str, Any]):
compare_dict(actual[key], value)
elif isinstance(value, list):
assert isinstance(actual[key], list)
assert actual[key] == pytest.approx(value)
if value and isinstance(value[0], datetime):
assert actual[key] == value
else:
assert actual[key] == pytest.approx(value)
else:
assert actual[key] == pytest.approx(value)
@@ -149,7 +153,7 @@ def test_optimize(
pass
# Fake energy management run start datetime
ems_eos.set_start_datetime(to_datetime().set(hour=fixed_start_hour))
ems_eos.set_start_datetime(to_datetime("2025-01-15T10:00:00+01:00"))
# Throw away any cached results of the last energy management run.
CacheEnergyManagementStore().clear()
tests/testdata/optimize_result_1.json
Vendored
+13 -11
View File
@@ -145,7 +145,7 @@
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@@ -272,10 +272,10 @@
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"Gesamtbilanz_Euro": 0.9585224311392879,
"Gesamt_Verluste": 3290.8745999936527,
"Gesamtbilanz_Euro": 1.251565700139288,
"Gesamteinnahmen_Euro": 1.1316277804018695,
"Gesamtkosten_Euro": 2.0901502115411574,
"Gesamtkosten_Euro": 2.3831934805411574,
"Home_appliance_wh_per_hour": [
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@@ -316,6 +316,7 @@
0.0,
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],
"home_appliance_energy_wh": {},
"Kosten_Euro_pro_Stunde": [
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@@ -352,7 +353,7 @@
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@@ -472,7 +473,7 @@
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@@ -513,8 +514,8 @@
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],
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@@ -753,9 +754,10 @@
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],
"washingstart": null
"washingstart": null,
"appliance_starts": {}
}
tests/testdata/optimize_result_1_be.json
Vendored
+149 -147
View File
@@ -14,13 +14,6 @@
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"Gesamtkosten_Euro": 2.562898755047597,
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@@ -316,98 +316,97 @@
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@@ -708,39 +709,21 @@
"initial_soc_percentage": 54
},
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"appliance_starts": {}
}
tests/testdata/optimize_result_2.json
Vendored
+352 -352
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],
"result": {
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"EAuto_SoC_pro_Stunde": [
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tests/testdata/optimize_result_2_be.json
Vendored
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View File
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