Mirrors/EOS
1
0
Fork 0
mirror of https://github.com/Akkudoktor-EOS/EOS.git synced 2025-04-19 08:55:15 +00:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Activate optimization test with 3 generations

Browse Source
This commit is contained in:
Michael Osthege 2024-10-07 19:52:48 +02:00 committed by Andreas
parent b6d0ef22f3
commit 3c1482c2c0
2 changed files with 10 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
src/akkudoktoreos/class_optimize.py
View File

@ -198,7 +198,7 @@ class optimization_problem:
return (gesamtbilanz,) return (gesamtbilanz,)
def optimize( def optimize(
self, start_solution: Optional[List[float]] = None self, start_solution: Optional[List[float]] = None, ngen: int = 400
) -> Tuple[Any, Dict[str, List[Any]]]: ) -> Tuple[Any, Dict[str, List[Any]]]:
"""Run the optimization process using a genetic algorithm.""" """Run the optimization process using a genetic algorithm."""
population = self.toolbox.population(n=300) population = self.toolbox.population(n=300)
@ -222,7 +222,7 @@ class optimization_problem:
lambda_=200, lambda_=200,
cxpb=0.5, cxpb=0.5,
mutpb=0.3, mutpb=0.3,
ngen=400, ngen=ngen,
stats=stats, stats=stats,
halloffame=hof, halloffame=hof,
verbose=self.verbose, verbose=self.verbose,
@ -244,6 +244,8 @@ class optimization_problem:
start_hour: Optional[int] = None, start_hour: Optional[int] = None,
worst_case: bool = False, worst_case: bool = False,
startdate: Optional[Any] = None, # startdate is not used! startdate: Optional[Any] = None, # startdate is not used!
*,
ngen: int = 400,
) -> Dict[str, Any]: ) -> Dict[str, Any]:
""" """
Perform EMS (Energy Management System) optimization and visualize results. Perform EMS (Energy Management System) optimization and visualize results.
@ -303,7 +305,9 @@ class optimization_problem:
"evaluate", "evaluate",
lambda ind: self.evaluate(ind, ems, parameter, start_hour, worst_case), lambda ind: self.evaluate(ind, ems, parameter, start_hour, worst_case),
) )
start_solution, extra_data = self.optimize(parameter["start_solution"]) start_solution, extra_data = self.optimize(
parameter["start_solution"], ngen=ngen
)
# Perform final evaluation on the best solution # Perform final evaluation on the best solution
o = self.evaluate_inner(start_solution, ems, start_hour) o = self.evaluate_inner(start_solution, ems, start_hour)

5
tests/test_class_optimize.py
View File

@ -11,7 +11,6 @@ DIR_TESTDATA = Path(__file__).parent / "testdata"
@pytest.mark.parametrize( @pytest.mark.parametrize(
"fn_in, fn_out", [("optimize_input_1.json", "optimize_result_1.json")] "fn_in, fn_out", [("optimize_input_1.json", "optimize_result_1.json")]
) )
@pytest.mark.skip(reason="Expensive - Skipped per default")
def test_optimize(fn_in, fn_out): def test_optimize(fn_in, fn_out):
# Load input and output data # Load input and output data
with open(DIR_TESTDATA / fn_in, "r") as f_in: with open(DIR_TESTDATA / fn_in, "r") as f_in:
@ -26,7 +25,9 @@ def test_optimize(fn_in, fn_out):
start_hour = 10 start_hour = 10
# Call the optimization function # Call the optimization function
ergebnis = opt_class.optimierung_ems(parameter=input_data, start_hour=start_hour) ergebnis = opt_class.optimierung_ems(
parameter=input_data, start_hour=start_hour, ngen=3
)
# Assert that the output contains all expected entries. # Assert that the output contains all expected entries.
# This does not assert that the optimization always gives the same result! # This does not assert that the optimization always gives the same result!