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Discharge Mask Bug, Tests updated, simple Price Forecast with linear weighting

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This commit is contained in:
Andreas 2024-12-26 07:50:25 +01:00
parent 56aa0ac2f5
commit 8b9ad60759
5 changed files with 483 additions and 473 deletions
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15
src/akkudoktoreos/optimization/genetic.py
View File

@ -436,13 +436,22 @@ class optimization_problem:
# New check: Activate discharge when battery SoC is 0 # New check: Activate discharge when battery SoC is 0
battery_soc_per_hour = np.array( battery_soc_per_hour = np.array(
o.get("Battery_SoC_pro_Stunde", []) o.get("akku_soc_pro_stunde", [])
) # Example key for battery SoC ) # Example key for battery SoC
if battery_soc_per_hour is not None: if battery_soc_per_hour is not None:
if battery_soc_per_hour is None or discharge_hours_bin is None:
raise ValueError("battery_soc_per_hour or discharge_hours_bin is None")
min_length = min(battery_soc_per_hour.size, discharge_hours_bin.size)
battery_soc_per_hour_tail = battery_soc_per_hour[-min_length:]
discharge_hours_bin_tail = discharge_hours_bin[-min_length:]
len_ac = len(self._config.eos.available_charging_rates_in_percentage)
# Find hours where battery SoC is 0 # Find hours where battery SoC is 0
zero_soc_mask = battery_soc_per_hour == 0 zero_soc_mask = battery_soc_per_hour_tail == 0
discharge_hours_bin[zero_soc_mask] = 1 # Activate discharge for these hours discharge_hours_bin_tail[zero_soc_mask] = (
len_ac + 2
) # Activate discharge for these hours
# Merge the updated discharge_hours_bin back into the individual # Merge the updated discharge_hours_bin back into the individual
adjusted_individual = self.merge_individual( adjusted_individual = self.merge_individual(

19
src/akkudoktoreos/prediction/price_forecast.py
View File

@ -155,24 +155,25 @@ class HourlyElectricityPriceForecast:
raise ValueError( raise ValueError(
"Not enough data to calculate the average for the last 7 days.", price_data "Not enough data to calculate the average for the last 7 days.", price_data
) )
# Calculate the overall average price across all data
# overall_average_price = np.mean(price_data)
# Create an array of 24 hourly values filled with the overall average
# average_prices = np.full(24, overall_average_price)
# print("Overall AVG (duplicated for 24 hours):", average_prices)
# return average_prices
# Reshape the data into a 7x24 matrix (7 rows for days, 24 columns for hours) # Reshape the data into a 7x24 matrix (7 rows for days, 24 columns for hours)
price_matrix = price_data.reshape(-1, 24) price_matrix = price_data.reshape(-1, 24)
# Calculate the average price for each hour across the 7 days # Calculate the average price for each hour across the 7 days
average_prices = np.average( average_prices = np.average(
price_matrix, price_matrix,
axis=0, axis=0,
weights=np.array([1, 2, 4, 8, 16, 32, 64]) / np.sum(np.array([1, 2, 4, 8, 16, 32, 64])), weights=np.array([1, 2, 4, 8, 16, 32, 64]) / np.sum(np.array([1, 2, 4, 8, 16, 32, 64])),
) )
return average_prices final_weights = np.linspace(1, 0, price_matrix.shape[1])
# Weight last known price linear falling
average_prices_with_final_weight = [
(average_prices[i] * (1 - final_weights[i]))
+ (price_matrix[-1, -1] * (final_weights[i]))
for i in range(price_matrix.shape[1])
]
return np.array(average_prices_with_final_weight)
def get_price_for_daterange( def get_price_for_daterange(
self, start_date_str: str, end_date_str: str, repeat: bool = False self, start_date_str: str, end_date_str: str, repeat: bool = False

4
tests/test_class_optimize.py
View File

@ -63,8 +63,8 @@ def test_optimize(
start_hour = 10 start_hour = 10
# Activate with pytest --full-run # Activate with pytest --full-run
if ngen > 10 and not is_full_run: # if ngen > 10 and not is_full_run:
pytest.skip() # pytest.skip()
visualize_filename = str((DIR_TESTDATA / f"new_{fn_out}").with_suffix(".pdf")) visualize_filename = str((DIR_TESTDATA / f"new_{fn_out}").with_suffix(".pdf"))

446
tests/testdata/optimize_result_2.json vendored
View File

@ -1,19 +1,25 @@
{ {
"ac_charge": [ "ac_charge": [
0.5,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
0.75,
0.0, 0.0,
0.5, 0.5,
0.375,
0.0,
0.0, 0.0,
1.0, 1.0,
0.0, 0.0,
0.0, 0.0,
0.75, 0.0,
0.75, 0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.375,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
@ -30,17 +36,11 @@
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
0.75,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
0.0, 0.5,
0.0,
0.0,
0.0,
0.0,
0.625,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
@ -102,75 +102,75 @@
"discharge_allowed": [ "discharge_allowed": [
0, 0,
1, 1,
1,
0,
0,
0,
0,
0,
0,
0, 0,
1, 1,
0, 0,
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1,
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0,
0,
1, 1,
0, 0,
0, 0,
0, 0,
0, 0,
0, 1,
0 1
], ],
"eautocharge_hours_float": [ "eautocharge_hours_float": [
0.875,
0.75,
0.875,
0.5, 0.5,
0.375, 1.0,
0.75, 0.625,
0.875, 0.5,
0.625, 0.625,
0.375, 0.375,
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@ -202,17 +202,17 @@
"result": { "result": {
"Last_Wh_pro_Stunde": [ "Last_Wh_pro_Stunde": [
4986.07, 4986.07,
1063.91, 11551.91,
14247.56, 1320.56,
12626.029999999999, 10309.03,
11529.67, 6407.67,
7731.82, 11664.82,
10393.22, 5149.22,
1103.78, 8969.78,
6373.12, 7000.12,
7733.71, 3053.71,
4299.98, 3550.98,
988.56, 3488.56,
912.38, 912.38,
704.61, 704.61,
516.37, 516.37,
@ -221,7 +221,7 @@
608.79, 608.79,
556.31, 556.31,
488.89, 488.89,
4256.91, 506.91,
804.89, 804.89,
1141.98, 1141.98,
1056.97, 1056.97,
@ -231,7 +231,7 @@
1257.98, 1257.98,
1232.67, 1232.67,
871.26, 871.26,
3985.88, 3360.88,
1158.03, 1158.03,
1222.72, 1222.72,
1221.04, 1221.04,
@ -243,15 +243,15 @@
"EAuto_SoC_pro_Stunde": [ "EAuto_SoC_pro_Stunde": [
5.0, 5.0,
11.555, 11.555,
11.555, 29.035,
26.85, 29.035,
35.589999999999996, 44.330000000000005,
48.699999999999996, 53.06999999999999,
59.62499999999999, 70.55,
74.92, 77.105,
74.92, 90.215,
83.66, 100.0,
94.585, 100.0,
100.0, 100.0,
100.0, 100.0,
100.0, 100.0,
@ -320,11 +320,18 @@
0.0, 0.0,
0.0 0.0
], ],
"Gesamt_Verluste": 9872.776074746695, "Gesamt_Verluste": 8120.988554782056,
"Gesamtbilanz_Euro": 12.332782378812306, "Gesamtbilanz_Euro": 10.524080664893,
"Gesamteinnahmen_Euro": 0.0, "Gesamteinnahmen_Euro": 0.0,
"Gesamtkosten_Euro": 12.332782378812306, "Gesamtkosten_Euro": 10.524080664893,
"Home_appliance_wh_per_hour": [ "Home_appliance_wh_per_hour": [
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@ -355,94 +362,87 @@
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], ],
"Kosten_Euro_pro_Stunde": [ "Kosten_Euro_pro_Stunde": [
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0.004569992000000018, 1.218515592,
2.750373626, 0.04475252599999999,
2.1541494859999997, 1.7187851859999999,
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0.174739608, 0.174739608,
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0.0, 0.22802125600000003,
0.199865757, 0.199865757,
0.182970359, 0.182970359,
0.162995926, 0.162995926,
1.4005233899999998, 0.16677339,
0.26411047, 0.26411047,
0.24530383800000005, 0.24530383800000005,
0.08545095, 0.08545095,
0.007989913613567745, 0.007989913613567745,
0.028255713342252034, 0.028255713342252034,
0.025392879919306634, 0.0,
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0.0003442778967139274, 0.0003442778967139274,
0.6945180797975209, 0.5571430797975209,
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0.04565364324294593, 0.04565364324294593,
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0.174597189, 0.174597189,
0.293043269, 0.293043269,
0.214398479, 0.0,
0.16484566 0.0
], ],
"Netzbezug_Wh_pro_Stunde": [ "Netzbezug_Wh_pro_Stunde": [
0.0, 0.0,
20.660000000000082, 5508.66,
13140.82, 213.81999999999994,
11464.339999999998, 9147.34,
5510.85, 388.85000000000036,
0.0, 6145.75,
6423.339999999999,
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57.05000000000018,
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1909.5199999999986,
912.38,
704.61,
516.37, 516.37,
0.0, 868.05,
0.0, 694.34,
608.79, 608.79,
556.31, 556.31,
488.89, 488.89,
4256.91, 506.91,
799.85, 799.85,
806.3900000000001, 806.3900000000001,
351.65, 351.65,
35.04348076126204, 35.04348076126204,
127.73830624887898, 127.73830624887898,
121.32288542430308, 0.0,
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1.7179535764168035, 1.7179535764168035,
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], ],
"Netzeinspeisung_Wh_pro_Stunde": [ "Netzeinspeisung_Wh_pro_Stunde": [
0.0, 0.0,
@ -486,83 +486,83 @@
], ],
"Verluste_Pro_Stunde": [ "Verluste_Pro_Stunde": [
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], ],
"Electricity_price": [ "Electricity_price": [
0.000228, 0.000228,
@ -715,75 +715,74 @@
"start_soc_prozent": 5 "start_soc_prozent": 5
}, },
"start_solution": [ "start_solution": [
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0.0, 0.0,
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13.0 0.0,
20.0
], ],
"washingstart": 13 "washingstart": 20
} }

472
tests/testdata/optimize_result_2_full.json vendored
View File

@ -3,13 +3,14 @@
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@ -150,24 +150,24 @@
1 1
], ],
"eautocharge_hours_float": [ "eautocharge_hours_float": [
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@ -202,15 +202,15 @@
"result": { "result": {
"Last_Wh_pro_Stunde": [ "Last_Wh_pro_Stunde": [
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