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Improve genetic optimizer convergence

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This commit is contained in:
Andreas
2026-07-16 14:32:20 +02:00
parent 6465e22f07
commit 4dfd4b275b
9 changed files with 1084 additions and 665 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/single_test_optimization.py
+1 -1
View File
@@ -430,7 +430,7 @@ def run_optimization(
start_datetime=start_datetime,
mode=EnergyManagementMode.OPTIMIZATION,
genetic_parameters=parameters,
genetic_individuals=ngen,
genetic_generations=ngen,
genetic_seed=seed,
)
)
tests/test_genetic_seeding.py
+140 -5
View File
@@ -1,5 +1,5 @@
from types import SimpleNamespace
from unittest.mock import patch
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
@@ -7,6 +7,7 @@ from deap import creator
from akkudoktoreos.config.config import ConfigEOS
from akkudoktoreos.core.coreabc import get_ems
from akkudoktoreos.core.emsettings import EnergyManagementMode
from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
from akkudoktoreos.utils.datetimeutil import to_datetime
@@ -21,6 +22,36 @@ def _configure_hourly_grid(config_eos: ConfigEOS, *, start_hour: int = 0) -> Non
get_ems(init=True).set_start_datetime(to_datetime().set(hour=start_hour, minute=0))
def test_energy_management_forwards_individuals_and_generations_separately(
config_eos: ConfigEOS,
):
_configure_hourly_grid(config_eos)
config_eos.optimization.genetic.individuals = 100
config_eos.optimization.genetic.generations = 80
ems = get_ems(init=True)
parameters = MagicMock()
solution = MagicMock()
optimizer = MagicMock()
optimizer.optimierung_ems.return_value = solution
with (
patch("akkudoktoreos.adapter.adapterabc.AdapterContainer.update_data"),
patch("akkudoktoreos.core.ems.GeneticOptimization", return_value=optimizer),
):
ems._run(
start_datetime=to_datetime().set(hour=0, minute=0),
mode=EnergyManagementMode.OPTIMIZATION,
genetic_parameters=parameters,
)
optimizer.optimierung_ems.assert_called_once_with(
start_hour=0,
parameters=parameters,
ngen=80,
individuals=100,
)
def test_ev_repair_is_resimulated_before_fitness_assignment(config_eos: ConfigEOS):
_configure_hourly_grid(config_eos)
opt = GeneticOptimization(fixed_seed=42)
@@ -44,7 +75,9 @@ def test_ev_repair_is_resimulated_before_fitness_assignment(config_eos: ConfigEO
eauto=None,
)
with patch.object(opt, "evaluate_inner", side_effect=[first_result, repaired_result]) as evaluate:
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
@@ -124,7 +157,9 @@ def test_mutated_warm_start_neighbors_keep_elapsed_slots(config_eos: ConfigEOS):
assert all(neighbor != start_solution for neighbor in neighbors)
def test_initial_population_uses_fixed_seed_budget_and_150_survivors(config_eos: ConfigEOS):
def test_initial_population_uses_fixed_seed_budget_and_configured_population(
config_eos: ConfigEOS,
):
_configure_hourly_grid(config_eos)
config_eos.optimization.genetic.individuals = 300
opt = GeneticOptimization(fixed_seed=42)
@@ -164,8 +199,108 @@ def test_initial_population_uses_fixed_seed_budget_and_150_survivors(config_eos:
assert first_genes.count(6) == 50
assert first_genes.count(7) == 100
assert first_genes.count(9) == 140
assert captured["mu"] == 150
assert captured["lambda"] == 150
assert captured["mu"] == 300
assert captured["lambda"] == 300
def test_small_population_scales_warm_and_educated_seed_families(config_eos: ConfigEOS):
_configure_hourly_grid(config_eos)
config_eos.optimization.genetic.individuals = 100
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
start_solution = [5] * opt.total_slots
captured: dict[str, object] = {}
def warm_neighbors(_solution, count):
captured["warm_count"] = count
return [[6] * opt.total_slots for _ in range(count)]
def educated(count):
captured["educated_count"] = count
return [[7] * opt.total_slots for _ in range(count)]
def fake_ea(population, toolbox, **kwargs):
captured["population"] = list(population)
captured["mu"] = kwargs["mu"]
captured["lambda"] = kwargs["lambda_"]
for individual in population:
individual.fitness.values = (float(sum(individual)),)
individual.extra_data = (0.0, 0.0, 0.0)
kwargs["halloffame"].update(population)
return population, SimpleNamespace(select=lambda _name: [])
with (
patch.object(opt, "_mutated_warm_start_neighbors", side_effect=warm_neighbors),
patch.object(opt, "_educated_guess_individuals", side_effect=educated),
patch.object(
opt.toolbox,
"population",
side_effect=lambda n: [creator.Individual([9] * opt.total_slots) for _ in range(n)],
),
patch("akkudoktoreos.optimization.genetic.genetic.algorithms.eaMuPlusLambda", fake_ea),
):
opt.optimize(start_solution=start_solution, ngen=1)
population = captured["population"]
first_genes = [individual[0] for individual in population] # type: ignore[union-attr]
assert len(population) == 100 # type: ignore[arg-type]
assert first_genes.count(5) == 10
assert first_genes.count(6) == 20
assert first_genes.count(7) == 40
assert first_genes.count(9) == 30
assert captured["warm_count"] == 20
assert captured["educated_count"] == 40
assert captured["mu"] == 100
assert captured["lambda"] == 100
def test_local_search_moves_weak_export_to_later_expensive_import(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
export_state = 5
self_consumption_state = 6
discharge_state = 1
source = 10
targets = list(range(20, 32))
base = [self_consumption_state] * slots
base[source] = export_state
for slot in targets:
base[slot] = 0
opt.simulation.elect_price_hourly = np.full(slots, 0.10)
opt.simulation.elect_price_hourly[targets] = 0.30
opt.simulation.elect_revenue_per_hour_arr = np.full(slots, 0.05)
opt.simulation.elect_revenue_per_hour_arr[source] = 0.20
opt.simulation.pv_prediction_wh = np.zeros(slots)
opt.simulation.load_energy_array = np.full(slots, 100.0)
def evaluate(individual):
export_value = -0.20 if individual[source] == export_state else 0.0
avoided_import = -0.05 * sum(individual[slot] == discharge_state for slot in targets)
return (export_value + avoided_import,)
opt.toolbox.register("evaluate", evaluate)
incumbent = creator.Individual(base)
incumbent.fitness.values = evaluate(incumbent)
best, evaluations, improvements, initial, final = opt._locally_improve_grid_export(
incumbent,
max_evaluations=96,
)
assert evaluations > 0
assert improvements == 1
assert final < initial
assert best[source] == self_consumption_state
assert sum(best[slot] == discharge_state for slot in targets) >= 6
def test_educated_guesses_encode_high_price_direct_marketing(config_eos: ConfigEOS):
tests/testdata/optimize_result_1_be.json
Vendored
+68 -68
View File
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@@ -272,10 +272,10 @@
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],
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"Gesamtbilanz_Euro": 0.5580695656866563,
"Gesamteinnahmen_Euro": 1.0626586223779864,
"Gesamtkosten_Euro": 1.6207281880646427,
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"Gesamtbilanz_Euro": 0.5951993628823129,
"Gesamteinnahmen_Euro": 1.1298399163065491,
"Gesamtkosten_Euro": 1.725039279188862,
"Home_appliance_wh_per_hour": [
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@@ -318,16 +318,16 @@
],
"home_appliance_energy_wh": {},
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@@ -358,16 +358,16 @@
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],
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@@ -438,16 +438,16 @@
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tests/testdata/optimize_result_2.json
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tests/testdata/optimize_result_2_be.json
Vendored
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View File
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"washingstart": 39,
"appliance_starts": {
"dishwasher1": [
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"2025-01-16 15:00:00+01:00"
]
}
}