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

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Andreas
2024-12-26 07:50:25 +01:00
parent c8c8d86bcc
commit 015a6a7a8d
5 changed files with 483 additions and 473 deletions
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15
src/akkudoktoreos/optimization/genetic.py
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@@ -436,13 +436,22 @@ class optimization_problem:
# New check: Activate discharge when battery SoC is 0
battery_soc_per_hour = np.array(
o.get("Battery_SoC_pro_Stunde", [])
o.get("akku_soc_pro_stunde", [])
) # Example key for battery SoC
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
zero_soc_mask = battery_soc_per_hour == 0
discharge_hours_bin[zero_soc_mask] = 1 # Activate discharge for these hours
zero_soc_mask = battery_soc_per_hour_tail == 0
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
adjusted_individual = self.merge_individual(

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

@@ -155,24 +155,25 @@ class HourlyElectricityPriceForecast:
raise ValueError(
"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)
price_matrix = price_data.reshape(-1, 24)
# Calculate the average price for each hour across the 7 days
average_prices = np.average(
price_matrix,
axis=0,
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(
self, start_date_str: str, end_date_str: str, repeat: bool = False