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Optimize states (#256)

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* optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull.
 * Ruff
 * test_class_optimize: Update testdata
 * Write pdf and json to test/testdata/new.... so it can be analyzed
   manually or just copied as new expected result.
 * workflow: Upload pytest optimization result artifacts (pdf, json)

---------

Co-authored-by: Andreas <drbacke@gmx.de>
This commit is contained in:
Dominique Lasserre
2024-12-16 13:37:16 +01:00
committed by GitHub
parent da419dbf39
commit 3d8a97e128
8 changed files with 842 additions and 871 deletions
Download Patch File Download Diff File Expand all files Collapse all files

49
tests/test_class_optimize.py
View File

@@ -37,9 +37,7 @@ def compare_dict(actual: dict[str, Any], expected: dict[str, Any]):
("optimize_input_2.json", "optimize_result_2_full.json", 400),
],
)
@patch("akkudoktoreos.optimization.genetic.visualisiere_ergebnisse")
def test_optimize(
visualisiere_ergebnisse_patch,
fn_in: str,
fn_out: str,
ngen: int,
@@ -53,8 +51,12 @@ def test_optimize(
input_data = OptimizationParameters(**json.load(f_in))
file = DIR_TESTDATA / fn_out
with file.open("r") as f_out:
expected_result = OptimizeResponse(**json.load(f_out))
# In case a new test case is added, we don't want to fail here, so the new output is written to disk before
try:
with file.open("r") as f_out:
expected_result = OptimizeResponse(**json.load(f_out))
except FileNotFoundError:
pass
opt_class = optimization_problem(tmp_config, fixed_seed=42)
start_hour = 10
@@ -62,15 +64,34 @@ def test_optimize(
if ngen > 10 and not is_full_run:
pytest.skip()
# Call the optimization function
ergebnis = opt_class.optimierung_ems(parameters=input_data, start_hour=start_hour, ngen=ngen)
# with open(f"new_{fn_out}", "w") as f_out:
# f_out.write(ergebnis.model_dump_json(indent=4, exclude_unset=True))
visualize_filename = str((DIR_TESTDATA / f"new_{fn_out}").with_suffix(".pdf"))
# Assert that the output contains all expected entries.
# This does not assert that the optimization always gives the same result!
# Reproducibility and mathematical accuracy should be tested on the level of individual components.
compare_dict(ergebnis.model_dump(), expected_result.model_dump())
def visualize_to_file(*args, **kwargs):
from akkudoktoreos.visualize import visualisiere_ergebnisse
# The function creates a visualization result PDF as a side-effect.
visualisiere_ergebnisse_patch.assert_called_once()
# Write test output pdf to file, so we can look at it manually
kwargs["filename"] = visualize_filename
return visualisiere_ergebnisse(*args, **kwargs)
with patch(
"akkudoktoreos.optimization.genetic.visualisiere_ergebnisse", side_effect=visualize_to_file
) as visualisiere_ergebnisse_patch:
# Call the optimization function
ergebnis = opt_class.optimierung_ems(
parameters=input_data, start_hour=start_hour, ngen=ngen
)
# Write test output to file, so we can take it as new data on intended change
with open(DIR_TESTDATA / f"new_{fn_out}", "w") as f_out:
f_out.write(ergebnis.model_dump_json(indent=4, exclude_unset=True))
assert ergebnis.result.Gesamtbilanz_Euro == pytest.approx(
expected_result.result.Gesamtbilanz_Euro
)
# Assert that the output contains all expected entries.
# This does not assert that the optimization always gives the same result!
# Reproducibility and mathematical accuracy should be tested on the level of individual components.
compare_dict(ergebnis.model_dump(), expected_result.model_dump())
# The function creates a visualization result PDF as a side-effect.
visualisiere_ergebnisse_patch.assert_called_once()

364
tests/testdata/optimize_result_1.json vendored
View File

@@ -1,5 +1,24 @@
{
"ac_charge": [
0.75,
0.75,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.75,
0.0,
0.0,
0.0,
@@ -20,26 +39,7 @@
0.0,
0.0,
0.0,
0.0,
0.0,
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0.0,
0.0,
0.875,
0.0,
0.0,
0.0,
@@ -100,30 +100,21 @@
1.0
],
"discharge_allowed": [
1,
1,
1,
1,
0,
0,
1,
0,
1,
0,
1,
1,
1,
0,
1,
0,
1,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
0,
@@ -132,22 +123,31 @@
0,
0,
0,
0,
0,
1,
1,
0,
1,
1,
0,
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1,
0,
1,
0,
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0,
1,
0,
0,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0,
1,
0
1
],
"eautocharge_hours_float": null,
"result": {
@@ -237,7 +237,7 @@
0.0,
0.0,
0.0,
0.19546715971074372,
0.212927386983471,
0.19275619999999996,
0.1339926,
0.0569765,
@@ -260,7 +260,7 @@
0.0,
0.0,
0.0,
0.19167938450413202,
0.1512260012396691,
0.257789,
0.1516669,
0.09915009999999999,
@@ -271,10 +271,10 @@
0.0,
0.0
],
"Gesamt_Verluste": 1859.6205371900821,
"Gesamtbilanz_Euro": 1.3505190567851246,
"Gesamteinnahmen_Euro": 1.3143154442148755,
"Gesamtkosten_Euro": 2.664834501,
"Gesamt_Verluste": 2071.900103305785,
"Gesamtbilanz_Euro": 0.9349588927768602,
"Gesamteinnahmen_Euro": 1.2913222882231399,
"Gesamtkosten_Euro": 2.226281181,
"Home_appliance_wh_per_hour": [
null,
null,
@@ -317,8 +317,8 @@
],
"Kosten_Euro_pro_Stunde": [
0.0,
0.004569992000000018,
0.0,
0.04475252599999999,
0.0,
0.0,
0.0,
@@ -326,16 +326,16 @@
0.0,
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0.05480703000000003,
0.0,
0.225316611,
0.291163892,
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0.19588158,
0.174739608,
0.0,
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0.22802125600000003,
0.199865757,
0.182970359,
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0.26411047,
0.0,
@@ -350,15 +350,15 @@
0.0,
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0.08231598,
0.174597189,
0.0,
0.293043269,
0.0,
0.16484566
0.0
],
"Netzbezug_Wh_pro_Stunde": [
0.0,
20.660000000000082,
0.0,
213.81999999999994,
0.0,
0.0,
0.0,
@@ -366,16 +366,16 @@
0.0,
0.0,
171.54000000000008,
0.0,
731.31,
980.68,
0.0,
912.38,
704.61,
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694.34,
608.79,
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799.85,
0.0,
@@ -390,10 +390,10 @@
0.0,
0.0,
257.64,
566.69,
0.0,
987.01,
0.0,
592.97
0.0
],
"Netzeinspeisung_Wh_pro_Stunde": [
0.0,
@@ -401,7 +401,7 @@
0.0,
0.0,
0.0,
2792.3879958677676,
3041.819814049586,
2753.66,
1914.18,
813.95,
@@ -424,7 +424,7 @@
0.0,
0.0,
0.0,
2738.2769214876007,
2160.371446280987,
3682.7,
2166.67,
1416.43,
@@ -437,20 +437,11 @@
],
"Verluste_Pro_Stunde": [
16.744090909090914,
2.817272727272737,
0.0,
29.157272727272726,
3.5592000000000112,
582.6179999999995,
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124.41545454545451,
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70.41409090909087,
156.0516223140496,
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@@ -458,62 +449,71 @@
0.0,
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118.37045454545455,
94.68272727272722,
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109.96227272727276,
0.0,
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53.855999999999995,
159.6443999999999,
21.032399999999996,
209.70516942148788,
279.0538264462814,
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100.08954545454549,
0.0
80.85954545454547
],
"akku_soc_pro_stunde": [
79.4714617768595,
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78.55109331955923,
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94.83379331955922,
100.0,
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96.85214359504131,
96.85214359504131,
92.92488808539943,
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90.70222107438015,
90.70222107438015,
90.70222107438015,
90.70222107438015,
90.70222107438015,
87.231189738292,
87.231189738292,
87.66005640495867,
89.15605640495866,
93.59062307162533,
94.17485640495867,
79.38253271349862,
79.38253271349862,
79.48139938016529,
95.66523271349861,
100.0,
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94.40134297520663
100.0,
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96.26355888429752,
93.27483643250687,
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90.88025137741046,
88.7758694903581,
88.7758694903581,
88.7758694903581,
85.30483815426996,
85.30483815426996,
85.73370482093662,
87.22970482093662,
91.66427148760329,
92.24850482093663,
100.0,
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100.0,
100.0,
95.75150654269973,
92.59211432506888,
90.0397296831956
]
},
"eauto_obj": {
@@ -626,106 +626,54 @@
"start_soc_prozent": 54
},
"start_solution": [
1.0,
1.0,
1.0,
1.0,
18.0,
18.0,
9.0,
13.0,
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],
"washingstart": null
}

620
tests/testdata/optimize_result_2.json vendored
View File

@@ -2,50 +2,50 @@
"ac_charge": [
0.0,
0.0,
0.2,
0.4,
0.0,
0.4,
0.625,
0.0,
0.8,
1.0,
0.6,
0.0,
0.8,
1.0,
0.6,
1.0,
0.0,
0.4,
1.0,
0.8,
0.2,
0.0,
1.0,
0.8,
0.8,
0.4,
0.6,
0.2,
0.6,
0.2,
0.8,
0.2,
0.625,
0.0,
0.375,
0.0,
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],
@@ -100,6 +100,42 @@
1.0
],
"discharge_allowed": [
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@@ -111,104 +147,68 @@
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],
"eautocharge_hours_float": [
0.0,
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],
"result": {
"Last_Wh_pro_Stunde": [
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@@ -232,7 +232,7 @@
1232.67,
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4027.009545454555,
1222.72,
1221.04,
949.99,
@@ -241,13 +241,13 @@
592.97
],
"EAuto_SoC_pro_Stunde": [
22.48,
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59.62499999999999,
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@@ -289,7 +289,6 @@
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@@ -305,14 +304,15 @@
0.0,
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@@ -320,17 +320,17 @@
0.0,
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],
"Gesamt_Verluste": 5357.560661157026,
"Gesamtbilanz_Euro": 13.87138792561157,
"Gesamteinnahmen_Euro": 1.0602444999999998,
"Gesamtkosten_Euro": 14.93163242561157,
"Gesamt_Verluste": 8823.859090909093,
"Gesamtbilanz_Euro": 12.348447740818184,
"Gesamteinnahmen_Euro": 0.0348376,
"Gesamtkosten_Euro": 12.383285340818183,
"Home_appliance_wh_per_hour": [
0.0,
0.0,
0.0,
2500.0,
2500.0,
0.0,
2500.0,
2500.0,
0.0,
0.0,
0.0,
@@ -365,29 +365,29 @@
0.0
],
"Kosten_Euro_pro_Stunde": [
1.2792601199999998,
1.759349592,
2.737606326,
2.9985720859999994,
1.5787430366115707,
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0.005407139999999934,
2.54785212,
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0.9189986259999999,
2.1770732859999997,
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@@ -396,38 +396,38 @@
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],
"Netzbezug_Wh_pro_Stunde": [
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