diff --git a/demand_generator/electrical_demand/ElectricalDemand.py b/demand_generator/electrical_demand/ElectricalDemand.py
index 3b773e8cd3b97c4ef55e3af0b9bc36c0e129ae24..ef3acb8a330272337075ca3a095a9a22a2a0e0d8 100644
--- a/demand_generator/electrical_demand/ElectricalDemand.py
+++ b/demand_generator/electrical_demand/ElectricalDemand.py
@@ -163,16 +163,6 @@ class ElectricalDemand:
b = datetime.datetime(self.year, self.seasons[p][2],
self.seasons[p][3], 23, 59)
- # Old Version
- # --------------------------------------------------------
- # new_df.update(pd.DataFrame.merge(
- # tmp_df[tmp_df['period'] == p[:-1]], time_df[a:b],
- # left_on=left_cols, right_on=right_cols,
- # how='inner', left_index=True).sort_index().drop(
- # ['hour_of_day'], 1))
- # --------------------------------------------------------
- # New Version
- # Tested with pandas version 1.3.1
new_df.update(pd.DataFrame.merge(
tmp_df[tmp_df['period'] == p[:-1]], time_df[a:b],
left_on=left_cols, right_on=right_cols, how='inner').sort_values(
diff --git a/input_profile_processor/input_profile_processor.py b/input_profile_processor/input_profile_processor.py
index d1aa0cb393b4ea3c021be48b80eb79a13e2e09fc..58137e835a75cbbecea82ca0e8aae754612f14c5 100644
--- a/input_profile_processor/input_profile_processor.py
+++ b/input_profile_processor/input_profile_processor.py
@@ -33,22 +33,20 @@ def process_input_profiles(input_profile_dict, t_start, t_horizon, t_step):
input_profiles = {}
for input_profile_name, input_profile_config in input_profile_dict.items():
if input_profile_config[1] == 'generate':
- profile = generate_profile(input_profile_config[0], input_profile_config[2:], input_profiles, t_start,
- t_horizon, t_step)
+ profile = generate_profile(input_profile_config[0], input_profile_config[2:], input_profiles, t_start, t_horizon, t_step)
elif input_profile_config[1] == 'modify':
- profile = modify_profile(input_profile_config[0], input_profile_config[2:], input_profiles, t_start,
- t_horizon, t_step)
+ profile = modify_profile(input_profile_config[0], input_profile_config[2:], input_profiles, t_start, t_horizon, t_step)
else:
- profile = pd.read_csv(input_profile_config[1], index_col=0)
+ profile = pd.read_csv(input_profile_config[1], index_col = 0)
try:
- profile.index = pd.to_datetime(profile.index, format='%d-%m-%Y %H:%M:%S')
+ profile.index = pd.to_datetime(profile.index, format = '%d-%m-%Y %H:%M:%S')
except ValueError:
- profile.index = pd.to_datetime(profile.index, format='%Y-%m-%d %H:%M:%S')
+ profile.index = pd.to_datetime(profile.index, format = '%Y-%m-%d %H:%M:%S')
profile = profile.resample(str(t_step) + 'H').mean().interpolate('linear')
input_profiles[input_profile_name] = (input_profile_config[0], profile)
for input_profile_name, (input_profile_type, input_profile) in input_profiles.items():
- input_profile = input_profile[t_start:t_start + pd.Timedelta(hours=t_horizon * t_step - t_step)]
+ input_profile = input_profile[t_start:t_start + pd.Timedelta(hours = t_horizon * t_step - t_step)]
if input_profile_type == 'irradiance':
lambda_1 = 14.122
@@ -56,8 +54,7 @@ def process_input_profiles(input_profile_dict, t_start, t_horizon, t_step):
phi = 52.21
psi_f = 0
beta = 30
- input_profile = generate_g_t_series(input_profile, beta, psi_f, phi, lambda_st, lambda_1, t_start,
- t_horizon, t_step)
+ input_profile = generate_g_t_series(input_profile, beta, psi_f, phi, lambda_st, lambda_1, t_start, t_horizon, t_step)
input_profile = input_profile.squeeze()
input_profile.set_axis(list(range(t_horizon)), inplace = True)
@@ -66,14 +63,13 @@ def process_input_profiles(input_profile_dict, t_start, t_horizon, t_step):
def generate_profile(profile_type, parameters, input_profiles, t_start, t_horizon, t_step):
- t_last = t_start + pd.Timedelta(hours=(t_horizon - 1) * t_step)
+ t_last = t_start + pd.Timedelta(hours = (t_horizon - 1) * t_step)
years = range(t_start.year, t_last.year + 1)
if profile_type == 'elec_demand':
profiles = []
for year in years:
- profile = ElectricalDemand(year).get_profile(parameters[0], 'h0',
- True) # True: with smoothing function for household profiles, False: no smoothing function for household profiles
+ profile = ElectricalDemand(year).get_profile(parameters[0], 'h0', True) # True: with smoothing function for household profiles, False: no smoothing function for household profiles
profile = profile.resample(str(t_step) + 'H').mean().interpolate('linear')
profiles.append(profile)
timeseries = pd.concat(profiles)
@@ -132,12 +128,14 @@ def modify_profile(mod_type: str, parameters, input_profiles, t_start, t_horizon
# t_horizon: int, number of days that should be created, for now hardcoded to 365 due to external dependencies
# parameters[]: array of additional parameters, should contain csv_path, season_offset, model_path, new, save.
# For details on the specific parameters look at the documentation of the prophet_based_modification
- profile = Modifier.prophet_based_modification(csv_path=parameters[0],
- feature_name=parameters[1],
- season_offset=parameters[2],
- start=t_start,
- model_path=parameters[3],
- new=parameters[4], mod_period=365, save=parameters[5])
+ profile = Modifier.prophet_based_modification(csv_path = parameters[0],
+ feature_name = parameters[1],
+ season_offset = parameters[2],
+ start = t_start,
+ model_path = parameters[3],
+ new = parameters[4],
+ mod_period = 365,
+ save = parameters[5])
return profile
else:
raise Exception("Modification for mod type " + str(mod_type) + " is not implemented!")
diff --git a/predictor/Predictor.py b/predictor/Predictor.py
index dd7bb4bcae2a9765dbe1ada1e0ecf53545c18701..448a0cb9b6d23f2a367f21db4caff906c83daee7 100644
--- a/predictor/Predictor.py
+++ b/predictor/Predictor.py
@@ -1,5 +1,6 @@
import numpy as np
-import copy as cp
+import pandas as pd
+import math
# ----------------------------------------------------------------------------------------------------------------------
@@ -13,84 +14,60 @@ class Predictor:
Parameters
----------
- time_series:
- The original time series that should be used for predictions.
+ profile:
+ The original profile that should be used for predictions.
name: str
Does not have to be unique, should be used so that the nature of the time series can be identified.
method: str
Name of the prediction method to be used. The default method is "same_as_last_day".
"""
- def __init__(self, time_series, type: str, method: str = "same_as_last_day"):
- self.profile = time_series
+ def __init__(self, profile, type: str, method: str, t_step: float):
+ self.profile = profile
self.type = type
self.method = method
-
- def rh_update(self, time_steps_rh, time_steps_fix):
- """
- Selects the correct prediction method based on the selected prediction method.
-
- Parameters
- ----------
- time_steps_rh:
- Series of time steps covering the entire horizon
- time_steps_fix:
- Series of time steps covering the fix part of the horizon, i.e. the part that should not be predicted.
-
- Returns
- -------
- The original time series after applying the prediction method.
- """
- if self.method == "time_forward":
- return time_forward(time_steps_rh, time_steps_fix, cp.deepcopy(self.profile))
+ self.t_step = t_step
+
+ def predict(self, time_steps):
+ if self.method == "perfect_foresight":
+ return self.profile[time_steps]
+ elif self.method == "time_forward":
+ if 0 in time_steps:
+ print('Requested time forward prediction for time steps that include the first time step! Using original data for the first time step.')
+ return pd.Series(self.profile[time_steps[0]], index = time_steps)
+ else:
+ return pd.Series(self.profile[time_steps[0]] - 1, index = time_steps)
elif self.method == "same_as_last_day":
- return same_as_last_day(time_steps_rh, time_steps_fix, cp.deepcopy(self.profile))
+ time_steps_per_day = int(24 / self.t_step)
+ if time_steps[0] - time_steps_per_day < 0:
+ print('Requested same as last day prediction for time steps that include the first day. Using original data for the first day.')
+ previous_day_data = np.zeros(time_steps_per_day)
+ previous_day_data[:time_steps_per_day - time_steps[0]] = self.profile[time_steps[0]:time_steps_per_day]
+ previous_day_data[time_steps_per_day - time_steps[0]:] = self.profile[:time_steps[0]]
+ else:
+ previous_day_data = np.array(self.profile[time_steps[0] - len(time_steps_per_day):time_steps[0]])
+ days_in_prediction = [(t * time_steps_per_day, (t + 1) * time_steps_per_day) for t in range(math.ceil(len(time_steps) / time_steps_per_day))]
+ days_in_prediction[-1] = (days_in_prediction[-1][0], time_steps[-1] - time_steps[0] + 1)
+
+ prediction = pd.Series(0.0, index = time_steps)
+ for start, end in days_in_prediction:
+ prediction[start:end] = previous_day_data[0:end - start]
+ return prediction
elif self.method == "same_as_last_week":
- return same_as_last_week(time_steps_rh, time_steps_fix, cp.deepcopy(self.profile))
- elif self.method == "perfect_foresight":
- return perfect_foresight(time_steps_rh, time_steps_fix, cp.deepcopy(self.profile))
+ time_steps_per_week = int(7 * 24 / self.t_step)
+ if time_steps[0] - time_steps_per_week < 0:
+ print('Requested same as last week prediction for time steps that include the first week. Using original data for the first week.')
+ previous_week_data = np.zeros(time_steps_per_week)
+ previous_week_data[:time_steps_per_week - time_steps[0]] = self.profile[time_steps[0]:time_steps_per_week]
+ previous_week_data[time_steps_per_week - time_steps[0]:] = self.profile[:time_steps[0]]
+ else:
+ previous_week_data = np.array(self.profile[time_steps[0] - len(time_steps_per_week):time_steps[0]])
+ weeks_in_prediction = [(t * time_steps_per_week, (t + 1) * time_steps_per_week) for t in range(math.ceil(len(time_steps) / time_steps_per_week))]
+ weeks_in_prediction[-1] = (weeks_in_prediction[-1][0], time_steps[-1] - time_steps[0] + 1)
+
+ prediction = pd.Series(0.0, index = time_steps)
+ for start, end in weeks_in_prediction:
+ prediction[start:end] = previous_week_data[0:end - start]
+ return prediction
else:
- # Default: Time Forward
- return time_forward(time_steps_rh, time_steps_fix, cp.deepcopy(self.profile))
-
- def get_profile(self):
- """
- Returns a copy of the original time series stored in the predictor.
-
- Returns
- -------
- Copy of the original time series.
- """
- return cp.deepcopy(self.profile)
-
-
-def time_forward(time_steps_rh, time_steps_fix, reference):
- reference.loc[list(set(time_steps_rh) - set(time_steps_fix))] = reference[time_steps_fix[-1]]
- return reference
-
-
-def same_as_last_day(time_steps_rh, time_steps_fix, reference):
- # Assumes that one time step is equal to 1h, should this change this code needs to be adjusted
- # Get list of time steps that need to be predicted. time_steps_rh \ time_steps_fix
- pred_steps = np.array(list(set(time_steps_rh) - set(time_steps_fix)))
- # Remove all pred_steps that are below 24, because no data from 24h earlier is present in the data.
- pred_steps = pred_steps[np.abs(pred_steps) > 23]
- # Replace all pred_steps with its data from 24 hours earlier
- reference.loc[pred_steps] = reference[pred_steps - 24].tolist()
- return reference
-
-
-def same_as_last_week(time_steps_rh, time_steps_fix, reference):
- # Assumes that one time step is equal to 1h, should this change this code needs to be adjusted
- # Get list of time steps that need to be predicted. time_steps_rh \ time_steps_fix
- pred_steps = np.array(list(set(time_steps_rh) - set(time_steps_fix)))
- # Remove all pred_steps that are below 168, because no data from one week earlier is present in the data.
- pred_steps = pred_steps[np.abs(pred_steps) > 167]
- # Replace all pred_steps with its data from 7*24 hours earlier
- reference.loc[pred_steps] = reference[pred_steps - 168].tolist()
- return reference
-
-
-def perfect_foresight(time_steps_rh, time_steps_fix, reference):
- # Assume that we can perfectly "predict" the future
- return reference
+ return self.profile[time_steps]