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Fix - Fixed bug from forecast out method related to issue 240 #250

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Apr 4, 2024
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Fix - Fixed bug from forecast out method related to issue 240 #250

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3d5eca4
Fix - Fixed bug from forecast out method related to issue 240
davidusb-geek Apr 3, 2024
7e77345
Fix - Fixed case for method list and perfect optim
davidusb-geek Apr 3, 2024
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22 changes: 12 additions & 10 deletions src/emhass/command_line.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,12 +76,12 @@ def set_input_data_dict(config_path: pathlib.Path, base_path: str, costfun: str,
days_list = utils.get_days_list(retrieve_hass_conf['days_to_retrieve'])
var_list = [retrieve_hass_conf['var_load'], retrieve_hass_conf['var_PV']]
if not rh.get_data(days_list, var_list,
minimal_response=False, significant_changes_only=False):
minimal_response=False, significant_changes_only=False):
return False
if not rh.prepare_data(retrieve_hass_conf['var_load'], load_negative = retrieve_hass_conf['load_negative'],
set_zero_min = retrieve_hass_conf['set_zero_min'],
var_replace_zero = retrieve_hass_conf['var_replace_zero'],
var_interp = retrieve_hass_conf['var_interp']):
set_zero_min = retrieve_hass_conf['set_zero_min'],
var_replace_zero = retrieve_hass_conf['var_replace_zero'],
var_interp = retrieve_hass_conf['var_interp']):
return False
df_input_data = rh.df_final.copy()
# What we don't need for this type of action
Expand Down Expand Up @@ -113,12 +113,12 @@ def set_input_data_dict(config_path: pathlib.Path, base_path: str, costfun: str,
days_list = utils.get_days_list(1)
var_list = [retrieve_hass_conf['var_load'], retrieve_hass_conf['var_PV']]
if not rh.get_data(days_list, var_list,
minimal_response=False, significant_changes_only=False):
minimal_response=False, significant_changes_only=False):
return False
if not rh.prepare_data(retrieve_hass_conf['var_load'], load_negative = retrieve_hass_conf['load_negative'],
set_zero_min = retrieve_hass_conf['set_zero_min'],
var_replace_zero = retrieve_hass_conf['var_replace_zero'],
var_interp = retrieve_hass_conf['var_interp']):
set_zero_min = retrieve_hass_conf['set_zero_min'],
var_replace_zero = retrieve_hass_conf['var_replace_zero'],
var_interp = retrieve_hass_conf['var_interp']):
return False
df_input_data = rh.df_final.copy()
# Get PV and load forecasts
Expand Down Expand Up @@ -201,9 +201,11 @@ def perfect_forecast_optim(input_data_dict: dict, logger: logging.Logger,
# Load cost and prod price forecast
df_input_data = input_data_dict['fcst'].get_load_cost_forecast(
input_data_dict['df_input_data'],
method=input_data_dict['fcst'].optim_conf['load_cost_forecast_method'])
method=input_data_dict['fcst'].optim_conf['load_cost_forecast_method'],
list_and_perfect=True)
df_input_data = input_data_dict['fcst'].get_prod_price_forecast(
df_input_data, method=input_data_dict['fcst'].optim_conf['prod_price_forecast_method'])
df_input_data, method=input_data_dict['fcst'].optim_conf['prod_price_forecast_method'],
list_and_perfect=True)
opt_res = input_data_dict['opt'].perform_perfect_forecast_optim(df_input_data, input_data_dict['days_list'])
# Save CSV file for analysis
if save_data_to_file:
Expand Down
86 changes: 61 additions & 25 deletions src/emhass/forecast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@

from emhass.retrieve_hass import RetrieveHass
from emhass.machine_learning_forecaster import MLForecaster
from emhass.utils import get_days_list, get_root
from emhass.utils import get_days_list, get_root, set_df_index_freq


class Forecast(object):
Expand Down Expand Up @@ -487,8 +487,9 @@ def get_forecast_days_csv(self, timedelta_days: Optional[int] = 1) -> pd.date_ra
forecast_dates_csv = forecast_dates_csv[0:self.params['passed_data']['prediction_horizon']]
return forecast_dates_csv

def get_forecast_out_from_csv(self, df_final: pd.DataFrame, forecast_dates_csv: pd.date_range,
csv_path: str, data_list: Optional[list] = None) -> pd.DataFrame:
def get_forecast_out_from_csv_or_list(self, df_final: pd.DataFrame, forecast_dates_csv: pd.date_range,
csv_path: str, data_list: Optional[list] = None,
list_and_perfect: Optional[bool] = False) -> pd.DataFrame:
r"""
Get the forecast data as a DataFrame from a CSV file.

Expand All @@ -506,34 +507,67 @@ def get_forecast_out_from_csv(self, df_final: pd.DataFrame, forecast_dates_csv:
:rtype: pd.DataFrame

"""
days_list = df_final.index.day.unique().tolist()
if csv_path is None:
data_dict = {'ts':forecast_dates_csv, 'yhat':data_list}
df_csv = pd.DataFrame.from_dict(data_dict)
df_csv.index = forecast_dates_csv
df_csv.drop(['ts'], axis=1, inplace=True)
df_csv = set_df_index_freq(df_csv)
if list_and_perfect:
days_list = df_final.index.day.unique().tolist()
else:
days_list = df_csv.index.day.unique().tolist()
else:
load_csv_file_path = self.root + csv_path
df_csv = pd.read_csv(load_csv_file_path, header=None, names=['ts', 'yhat'])
df_csv.index = forecast_dates_csv
df_csv.drop(['ts'], axis=1, inplace=True)
df_csv = set_df_index_freq(df_csv)
days_list = df_final.index.day.unique().tolist()
forecast_out = pd.DataFrame()
for day in days_list:
first_elm_index = [i for i, x in enumerate(df_final.index.day == day) if x][0]
last_elm_index = [i for i, x in enumerate(df_final.index.day == day) if x][-1]
fcst_index = pd.date_range(start=df_final.index[first_elm_index],
end=df_final.index[last_elm_index],
freq=df_final.index.freq)
first_hour = str(df_final.index[first_elm_index].hour)+":"+str(df_final.index[first_elm_index].minute)
last_hour = str(df_final.index[last_elm_index].hour)+":"+str(df_final.index[last_elm_index].minute)
if csv_path is None:
if list_and_perfect:
df_tmp = copy.deepcopy(df_final)
else:
df_tmp = copy.deepcopy(df_csv)
else:
df_tmp = copy.deepcopy(df_final)
first_elm_index = [i for i, x in enumerate(df_tmp.index.day == day) if x][0]
last_elm_index = [i for i, x in enumerate(df_tmp.index.day == day) if x][-1]
fcst_index = pd.date_range(start=df_tmp.index[first_elm_index],
end=df_tmp.index[last_elm_index],
freq=df_tmp.index.freq)
first_hour = str(df_tmp.index[first_elm_index].hour)+":"+str(df_tmp.index[first_elm_index].minute)
last_hour = str(df_tmp.index[last_elm_index].hour)+":"+str(df_tmp.index[last_elm_index].minute)
if len(forecast_out) == 0:
forecast_out = pd.DataFrame(
df_csv.between_time(first_hour, last_hour).values,
index=fcst_index)
if csv_path is None:
if list_and_perfect:
forecast_out = pd.DataFrame(
df_csv.between_time(first_hour, last_hour).values,
index=fcst_index)
else:
forecast_out = pd.DataFrame(
df_csv.loc[fcst_index,:].between_time(first_hour, last_hour).values,
index=fcst_index)
else:
forecast_out = pd.DataFrame(
df_csv.between_time(first_hour, last_hour).values,
index=fcst_index)
else:
forecast_tp = pd.DataFrame(
df_csv.between_time(first_hour, last_hour).values,
index=fcst_index)
if csv_path is None:
if list_and_perfect:
forecast_tp = pd.DataFrame(
df_csv.between_time(first_hour, last_hour).values,
index=fcst_index)
else:
forecast_tp = pd.DataFrame(
df_csv.loc[fcst_index,:].between_time(first_hour, last_hour).values,
index=fcst_index)
else:
forecast_tp = pd.DataFrame(
df_csv.between_time(first_hour, last_hour).values,
index=fcst_index)
forecast_out = pd.concat([forecast_out, forecast_tp], axis=0)
return forecast_out

Expand Down Expand Up @@ -668,7 +702,8 @@ def get_load_forecast(self, days_min_load_forecast: Optional[int] = 3, method: O
return P_Load_forecast

def get_load_cost_forecast(self, df_final: pd.DataFrame, method: Optional[str] = 'hp_hc_periods',
csv_path: Optional[str] = "data_load_cost_forecast.csv") -> pd.DataFrame:
csv_path: Optional[str] = "data_load_cost_forecast.csv",
list_and_perfect: Optional[bool] = False) -> pd.DataFrame:
r"""
Get the unit cost for the load consumption based on multiple tariff \
periods. This is the cost of the energy from the utility in a vector \
Expand Down Expand Up @@ -698,7 +733,7 @@ def get_load_cost_forecast(self, df_final: pd.DataFrame, method: Optional[str] =
df_final.loc[df_hp.index, self.var_load_cost] = self.optim_conf['load_cost_hp']
elif method == 'csv':
forecast_dates_csv = self.get_forecast_days_csv(timedelta_days=0)
forecast_out = self.get_forecast_out_from_csv(
forecast_out = self.get_forecast_out_from_csv_or_list(
df_final, forecast_dates_csv, csv_path)
df_final[self.var_load_cost] = forecast_out
elif method == 'list': # reading a list of values
Expand All @@ -712,8 +747,8 @@ def get_load_cost_forecast(self, df_final: pd.DataFrame, method: Optional[str] =
data_list = data_list[0:len(self.forecast_dates)]
# Define the correct dates
forecast_dates_csv = self.get_forecast_days_csv(timedelta_days=0)
forecast_out = self.get_forecast_out_from_csv(
df_final, forecast_dates_csv, None, data_list=data_list)
forecast_out = self.get_forecast_out_from_csv_or_list(
df_final, forecast_dates_csv, None, data_list=data_list, list_and_perfect=list_and_perfect)
# Fill the final DF
df_final[self.var_load_cost] = forecast_out
else:
Expand All @@ -722,7 +757,8 @@ def get_load_cost_forecast(self, df_final: pd.DataFrame, method: Optional[str] =
return df_final

def get_prod_price_forecast(self, df_final: pd.DataFrame, method: Optional[str] = 'constant',
csv_path: Optional[str] = "/data/data_prod_price_forecast.csv") -> pd.DataFrame:
csv_path: Optional[str] = "/data/data_prod_price_forecast.csv",
list_and_perfect: Optional[bool] = False) -> pd.DataFrame:
r"""
Get the unit power production price for the energy injected to the grid.\
This is the price of the energy injected to the utility in a vector \
Expand All @@ -747,7 +783,7 @@ def get_prod_price_forecast(self, df_final: pd.DataFrame, method: Optional[str]
df_final[self.var_prod_price] = self.optim_conf['prod_sell_price']
elif method == 'csv':
forecast_dates_csv = self.get_forecast_days_csv(timedelta_days=0)
forecast_out = self.get_forecast_out_from_csv(df_final,
forecast_out = self.get_forecast_out_from_csv_or_list(df_final,
forecast_dates_csv,
csv_path)
df_final[self.var_prod_price] = forecast_out
Expand All @@ -762,8 +798,8 @@ def get_prod_price_forecast(self, df_final: pd.DataFrame, method: Optional[str]
data_list = data_list[0:len(self.forecast_dates)]
# Define the correct dates
forecast_dates_csv = self.get_forecast_days_csv(timedelta_days=0)
forecast_out = self.get_forecast_out_from_csv(
df_final, forecast_dates_csv, None, data_list=data_list)
forecast_out = self.get_forecast_out_from_csv_or_list(
df_final, forecast_dates_csv, None, data_list=data_list, list_and_perfect=list_and_perfect)
# Fill the final DF
df_final[self.var_prod_price] = forecast_out
else:
Expand Down
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