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Add more optimization tests (#171)

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* Integrated single_test_optimization into pytest to run a basic optimization test with tolerance set to 1e-6, ensuring quick detection of deviations.
* Added a long-run test (400 generations, like single_test_optimization), which can be triggered using --full-run in pytest.
* Mocked PDF creation in optimization tests and added a new PDF generation test with image comparison validation.

Note: Current tolerance is set to 1e-6; feedback on whether this tolerance is tight enough is welcome.

---------

Co-authored-by: Normann <github@koldrack.com>
Co-authored-by: Michael Osthege <michael.osthege@outlook.com>
This commit is contained in:
Dominique Lasserre 2024-11-10 23:22:30 +01:00 committed by GitHub
parent df202db242
commit ef93574f87
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GPG Key ID: B5690EEEBB952194
10 changed files with 395 additions and 10 deletions
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4
.gitignore vendored
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@ -246,3 +246,7 @@ $RECYCLE.BIN/
# Visualization side effects
**/visualization_results.pdf
visualize_output_*.pdf
# Test images
*_pdf.png

7
Makefile
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@ -1,5 +1,5 @@
# Define the targets
.PHONY: help venv pip install dist test docker-run docs clean
.PHONY: help venv pip install dist test test-full docker-run docs clean
# Default target
all: help
@ -70,6 +70,11 @@ test:
@echo "Running tests..."
.venv/bin/pytest -vs --cov src --cov-report term-missing
# Target to run all tests.
test-full:
@echo "Running all tests..."
.venv/bin/pytest --full-run
# Run entire setup on docker
docker-run:
@docker compose up --remove-orphans

11
tests/conftest.py
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@ -6,6 +6,17 @@ import pytest
from xprocess import ProcessStarter
def pytest_addoption(parser):
parser.addoption(
"--full-run", action="store_true", default=False, help="Run with all optimization tests."
)
@pytest.fixture
def is_full_run(request):
yield bool(request.config.getoption("--full-run"))
@pytest.fixture
def server(xprocess):
class Starter(ProcessStarter):

45
tests/test_class_optimize.py
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@ -1,16 +1,39 @@
import json
from pathlib import Path
from typing import Any
from unittest.mock import patch
import pytest
from akkudoktoreos.class_optimize import optimization_problem
from akkudoktoreos.config import output_dir
DIR_TESTDATA = Path(__file__).parent / "testdata"
@pytest.mark.parametrize("fn_in, fn_out", [("optimize_input_1.json", "optimize_result_1.json")])
def test_optimize(fn_in, fn_out):
def compare_dict(actual: dict[str, Any], expected: dict[str, Any]):
assert set(actual) == set(expected)
for key, value in expected.items():
if isinstance(value, dict):
assert isinstance(actual[key], dict)
compare_dict(actual[key], value)
elif isinstance(value, list):
assert isinstance(actual[key], list)
assert actual[key] == pytest.approx(value)
else:
assert actual[key] == pytest.approx(value)
@pytest.mark.parametrize(
"fn_in, fn_out, ngen",
[
("optimize_input_1.json", "optimize_result_1.json", 3),
("optimize_input_2.json", "optimize_result_2.json", 3),
("optimize_input_2.json", "optimize_result_2_full.json", 400),
],
)
@patch("akkudoktoreos.class_optimize.visualisiere_ergebnisse")
def test_optimize(visualisiere_ergebnisse_patch, fn_in: str, fn_out: str, ngen: int, is_full_run):
# Load input and output data
with open(DIR_TESTDATA / fn_in, "r") as f_in:
input_data = json.load(f_in)
@ -23,16 +46,20 @@ def test_optimize(fn_in, fn_out):
)
start_hour = 10
if ngen > 10 and not is_full_run:
pytest.skip()
# Call the optimization function
ergebnis = opt_class.optimierung_ems(parameter=input_data, start_hour=start_hour, ngen=3)
# with open("new.json", "w") as f_out:
# json.dump(ergebnis, f_out, indent=4)
ergebnis = opt_class.optimierung_ems(parameter=input_data, start_hour=start_hour, ngen=ngen)
# with open(f"new_{fn_out}", "w") as f_out:
# from akkudoktoreos.class_numpy_encoder import NumpyEncoder
# json_data_str = NumpyEncoder.dumps(ergebnis)
# json.dump(json.loads(json_data_str), f_out, indent=4)
# 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.
assert set(ergebnis) == set(expected_output_data)
compare_dict(ergebnis, expected_output_data)
# The function creates a visualization result PDF as a side-effect.
fp_viz = Path(output_dir) / "visualization_results.pdf"
assert fp_viz.exists()
visualisiere_ergebnisse_patch.assert_called_once()

30
tests/test_visualize.py Normal file
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@ -0,0 +1,30 @@
import json
from pathlib import Path
from unittest.mock import patch
import pytest
from matplotlib.testing.compare import compare_images
from akkudoktoreos.visualize import visualisiere_ergebnisse
DIR_TESTDATA = Path(__file__).parent / "testdata"
DIR_IMAGEDATA = DIR_TESTDATA / "images"
@pytest.mark.parametrize(
"fn_in, fn_out, fn_out_base",
[("visualize_input_1.json", "visualize_output_1.pdf", "visualize_base_output_1.pdf")],
)
@patch("akkudoktoreos.visualize.output_dir", DIR_IMAGEDATA)
def test_visualisiere_ergebnisse(fn_in, fn_out, fn_out_base):
with open(DIR_TESTDATA / fn_in, "r") as f:
input_data = json.load(f)
visualisiere_ergebnisse(**input_data)
assert (
compare_images(
str(DIR_IMAGEDATA / fn_out),
str(DIR_IMAGEDATA / fn_out_base),
0,
)
is None
)

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tests/testdata/images/visualize_base_output_1.pdf vendored Normal file
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tests/testdata/optimize_input_2.json vendored Normal file
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@ -0,0 +1,47 @@
{
"preis_euro_pro_wh_akku": 0.0001,
"pv_soc": 80,
"pv_akku_cap": 26400,
"year_energy": 4100000,
"einspeiseverguetung_euro_pro_wh": 0.00007,
"max_heizleistung": 1000,
"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
],
"pv_forecast": [
0, 0, 0, 0, 0, 0, 0, 8.05, 352.91, 728.51, 930.28, 1043.25, 1106.74, 1161.69,
6018.82, 5519.07, 3969.88, 3017.96, 1943.07, 1007.17, 319.67, 7.88, 0, 0, 0, 0,
0, 0, 0, 0, 0, 5.04, 335.59, 705.32, 1121.12, 1604.79, 2157.38, 1433.25, 5718.49,
4553.96, 3027.55, 2574.46, 1720.4, 963.4, 383.3, 0, 0, 0
],
"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
],
"strompreis_euro_pro_wh": [
0.0003384, 0.0003318, 0.0003284, 0.0003283, 0.0003289, 0.0003334, 0.0003290,
0.0003302, 0.0003042, 0.0002430, 0.0002280, 0.0002212, 0.0002093, 0.0001879,
0.0001838, 0.0002004, 0.0002198, 0.0002270, 0.0002997, 0.0003195, 0.0003081,
0.0002969, 0.0002921, 0.0002780, 0.0003384, 0.0003318, 0.0003284, 0.0003283,
0.0003289, 0.0003334, 0.0003290, 0.0003302, 0.0003042, 0.0002430, 0.0002280,
0.0002212, 0.0002093, 0.0001879, 0.0001838, 0.0002004, 0.0002198, 0.0002270,
0.0002997, 0.0003195, 0.0003081, 0.0002969, 0.0002921, 0.0002780
],
"eauto_min_soc": 80,
"eauto_cap": 60000,
"eauto_charge_efficiency": 0.95,
"eauto_charge_power": 11040,
"eauto_soc": 5,
"pvpowernow": 211.137503624,
"start_solution": null,
"haushaltsgeraet_wh": 5000,
"haushaltsgeraet_dauer": 2,
"min_soc_prozent": 15
}

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tests/testdata/optimize_result_2.json vendored Normal file
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@ -0,0 +1,99 @@
{
"ac_charge": [
0.0, 0.0, 0.6, 0.4, 0.0, 0.4, 0.0, 0.8, 1.0, 0.6, 0.0, 0.4, 1.0, 0.6, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.4, 0.6, 0.0, 0.4, 0.2, 0.6, 0.6, 0.2, 0.4, 0.2, 0.0, 0.0, 0.2, 0.6, 0.0, 0.0, 1.0, 1.0, 0.2, 0.0, 0.6, 0.4, 0.4, 0.2, 1.0, 0.6, 0.0, 0.0
],
"dc_charge": [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
],
"discharge_allowed": [
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1
],
"eautocharge_hours_float": [
0.0, 0.375, 0.875, 0.375, 0.5, 0.75, 1.0, 1.0, 0.875, 0.0, 0.5, 0.375, 1.0, 0.875, 1.0, 0.625, 0.5, 1.0, 1.0, 0.375, 0.375, 0.375, 0.0, 0.5, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
],
"result": {
"Last_Wh_pro_Stunde": [
6297.07, 6756.91, 16208.56, 15449.029999999999, 16313.488181818184, 7731.82, 6460.22, 6974.78, 1129.12, 1178.71, 1050.98, 988.56, 912.38, 1741.405454545456, 516.37, 868.05, 694.34, 608.79, 556.31, 488.89, 506.91, 804.89, 1718.014090909092, 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
],
"Netzeinspeisung_Wh_pro_Stunde": [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 813.95, 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, 128.65999999999985, 448.79999999999995, 1330.3700000000001, 175.26999999999998, 4485.82, 3682.7, 2166.67, 1416.43, 497.68000000000006, 0.0, 0.0, 0.0, 0.0, 0.0
],
"Netzbezug_Wh_pro_Stunde": [
366.78999999999996, 5713.66, 15101.82, 14287.339999999998, 10294.668181818184, 2212.75, 2490.34, 3956.8199999999997, 0.0, 171.54000000000008, 731.31, 980.68, 0.0, 1741.405454545456, 516.37, 868.05, 694.34, 608.79, 556.31, 488.89, 0.0, 799.85, 1382.424090909092, 351.65, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 257.64, 566.69, 987.01, 0.0, 0.0
],
"Kosten_Euro_pro_Stunde": [
0.08362812, 1.263861592, 3.160810926, 2.6845911859999996, 1.8921600118181823, 0.44343509999999997, 0.547376732, 0.8981981399999999, 0.0, 0.05480703000000003, 0.225316611, 0.291163892, 0.0, 0.48411071636363673, 0.174739608, 0.28801899, 0.22802125600000003, 0.199865757, 0.182970359, 0.162995926, 0.0, 0.26411047, 0.42053340845454584, 0.08545095, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08231598, 0.174597189, 0.293043269, 0.0, 0.0
],
"akku_soc_pro_stunde": [
58.47796143250689, 65.14462809917354, 81.81129476584022, 91.81129476584022, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 96.07274449035812, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 97.8180526859504, 97.8180526859504, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 96.84060778236915, 94.28822314049587
],
"Einnahmen_Euro_pro_Stunde": [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0569765, 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, 0.009006199999999989, 0.03141599999999999, 0.0931259, 0.012268899999999998, 0.31400739999999994, 0.257789, 0.1516669, 0.09915009999999999, 0.0348376, 0.0, 0.0, 0.0, 0.0, 0.0
],
"Gesamtbilanz_Euro": 13.525878719636365,
"E-Auto_SoC_pro_Stunde": [
13.74, 20.294999999999998, 37.775, 53.06999999999999, 70.55, 81.475, 90.215, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0
],
"Gesamteinnahmen_Euro": 1.0602444999999998,
"Gesamtkosten_Euro": 14.586123219636365,
"Verluste_Pro_Stunde": [
957.818181818182, 447.0, 1152.0, 843.0, 846.7933884297522, 345.0, 276.0, 309.0, 0.0, 0.0, 0.0, 0.0, 124.41545454545451, 141.38119834710756, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 69.12409090909085, 0.0, 78.55010330578523, 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, 100.08954545454549, 80.85954545454547
],
"Gesamt_Verluste": 5771.031508264463,
"Haushaltsgeraet_wh_pro_stunde": [
0.0, 0.0, 0.0, 2500.0, 2500.0, 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, 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, 0.0
]
},
"eauto_obj": {
"kapazitaet_wh": 60000,
"start_soc_prozent": 5,
"soc_wh": 60000.0,
"hours": 48,
"discharge_array": [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
],
"charge_array": [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
],
"lade_effizienz": 0.95,
"entlade_effizienz": 1.0,
"max_ladeleistung_w": 11040
},
"start_solution": [
0, 0, 4, 3, 1, 3, 1, 5, 6, 4, 1, 3, 6, 4, 6, 0, 6, 6, 0, 0, 3, 4, 1, 3, 2, 4, 4, 2, 3, 2, 1, 0, 2, 4, 1, 0, 6, 6, 2, 0, 4, 3, 3, 2, 6, 4, 1, 1, 0, 1, 5, 1, 2, 4, 6, 6, 5, 0, 2, 1, 6, 5, 6, 3, 2, 6, 6, 1, 1, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13
],
"spuelstart": 13,
"simulation_data": {
"Last_Wh_pro_Stunde": [
6297.07, 6756.91, 16208.56, 15449.029999999999, 16313.488181818184, 7731.82, 6460.22, 6974.78, 1129.12, 1178.71, 1050.98, 988.56, 912.38, 1741.405454545456, 516.37, 868.05, 694.34, 608.79, 556.31, 488.89, 506.91, 804.89, 1718.014090909092, 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
],
"Netzeinspeisung_Wh_pro_Stunde": [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 813.95, 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, 128.65999999999985, 448.79999999999995, 1330.3700000000001, 175.26999999999998, 4485.82, 3682.7, 2166.67, 1416.43, 497.68000000000006, 0.0, 0.0, 0.0, 0.0, 0.0
],
"Netzbezug_Wh_pro_Stunde": [
366.78999999999996, 5713.66, 15101.82, 14287.339999999998, 10294.668181818184, 2212.75, 2490.34, 3956.8199999999997, 0.0, 171.54000000000008, 731.31, 980.68, 0.0, 1741.405454545456, 516.37, 868.05, 694.34, 608.79, 556.31, 488.89, 0.0, 799.85, 1382.424090909092, 351.65, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 257.64, 566.69, 987.01, 0.0, 0.0
],
"Kosten_Euro_pro_Stunde": [
0.08362812, 1.263861592, 3.160810926, 2.6845911859999996, 1.8921600118181823, 0.44343509999999997, 0.547376732, 0.8981981399999999, 0.0, 0.05480703000000003, 0.225316611, 0.291163892, 0.0, 0.48411071636363673, 0.174739608, 0.28801899, 0.22802125600000003, 0.199865757, 0.182970359, 0.162995926, 0.0, 0.26411047, 0.42053340845454584, 0.08545095, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08231598, 0.174597189, 0.293043269, 0.0, 0.0
],
"akku_soc_pro_stunde": [
58.47796143250689, 65.14462809917354, 81.81129476584022, 91.81129476584022, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 96.07274449035812, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 97.8180526859504, 97.8180526859504, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 96.84060778236915, 94.28822314049587
],
"Einnahmen_Euro_pro_Stunde": [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0569765, 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, 0.009006199999999989, 0.03141599999999999, 0.0931259, 0.012268899999999998, 0.31400739999999994, 0.257789, 0.1516669, 0.09915009999999999, 0.0348376, 0.0, 0.0, 0.0, 0.0, 0.0
],
"Gesamtbilanz_Euro": 13.525878719636365,
"E-Auto_SoC_pro_Stunde": [
13.74, 20.294999999999998, 37.775, 53.06999999999999, 70.55, 81.475, 90.215, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0
],
"Gesamteinnahmen_Euro": 1.0602444999999998,
"Gesamtkosten_Euro": 14.586123219636365,
"Verluste_Pro_Stunde": [
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99
tests/testdata/optimize_result_2_full.json vendored Normal file
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63
tests/testdata/visualize_input_1.json vendored Normal file
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]
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"discharge": [
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"temperature": [
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],
"start_hour": 0,
"prediction_hours": 48,
"einspeiseverguetung_euro_pro_wh": [
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],
"filename": "visualize_output_1.pdf",
"extra_data": null
}