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Added dynamic function parameter to get_profiles function #21

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Jul 15, 2020
Merged

Added dynamic function parameter to get_profiles function #21

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37 changes: 29 additions & 8 deletions demandlib/bdew.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,21 +115,40 @@ def create_bdew_load_profiles(self, dt_index, slp_types, holidays=None):
new_df.drop('date', axis=1, inplace=True)
return new_df.div(new_df.sum(axis=0), axis=1)

def get_profile(self, ann_el_demand_per_sector):
def get_profile(self, ann_el_demand_per_sector,
dyn_function_h0: bool = True):
""" Get the profiles for the given annual demand

Parameters
----------
ann_el_demand_per_sector : dictionary
Key: sector, value: annual value
dyn_function_h0: bool, default True
Uses the dynamisation function of the BDEW to smoothen the
seasonal edges. Functions resolution is daily.
f(x) = -3.916649251 * 10^-10 * x^4 + 3,2 * 10^-7 * x³ - 7,02
* 10^-5 * x²+0,0021 * x +1,24
Adjustment of accuracy: from -3,92 to -3.916649251

Returns
-------
pandas.DataFrame : Table with all profiles

"""
return self.slp_frame.multiply(pd.Series(
ann_el_demand_per_sector), axis=1).dropna(how='all', axis=1) * 4
if dyn_function_h0 == True:
quartersinyear = len(self.slp_frame)
for quarter in range(quartersinyear):
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This way of doing it is rather inefficient. You should rather calculate an array of all smoothing factors and multiply the SLP by that. The following might work (did not verify):

day_of_year = [(quarter + 1) / (24 * 4) for quarter in range(len(self.slp_frame))]
smoothing_factor = [-3.916649251 * 10 ** -10
                                   * timestep ** 4 + 3.2 * 10 ** -7
                                   * timestep ** 3 - 7.02 * 10 ** -5
                                   * timestep ** 2 + 0.0021
                                   * timestep + 1.24 for timestep in day_of_year]
self.slp_frame['h0'][quarter] = self.slp_frame['h0'] * smoothing_factor

(Or - preferably - using numpy.)

quarterhour_to_day = (quarter + 1) / (24 * 4)
smoothing_factor = -3.916649251 * 10 ** -10 \
* quarterhour_to_day ** 4 + 3.2 * 10 ** -7 \
* quarterhour_to_day ** 3 - 7.02 * 10 ** -5 \
* quarterhour_to_day ** 2 + 0.0021 \
* quarterhour_to_day + 1.24

self.slp_frame['h0'][quarter] = self.slp_frame['h0'][
quarter] * smoothing_factor
return self.slp_frame.multiply(pd.Series(ann_el_demand_per_sector),
axis=1).dropna(how='all', axis=1) * 4


class HeatBuilding:
Expand Down Expand Up @@ -188,7 +207,7 @@ def weighted_temperature(self, how='geometric_series'):
Notes
-----
Equation for the mathematical series of the average
tempaerature [1]_:
temperature [1]_:

.. math::
T=\frac{T_{D}+0.5\cdot T_{D-1}+0.25\cdot T_{D-2}+
Expand Down Expand Up @@ -263,10 +282,12 @@ def get_sf_values(self, filename='shlp_hour_factors.csv'):

# drop unnecessary columns
drop_cols = (
['hour_of_day', 'hour', 'building_class', 'shlp_type',
'date', 'temperature'] + (['weekday_x'] if residential else []) +
(['weekday_y'] if residential else []) +
(['weekday'] if not residential else []))
['hour_of_day', 'hour', 'building_class', 'shlp_type',
'date', 'temperature']
+ (['weekday_x'] if residential else [])
+ (['weekday_y'] if residential else [])
+ (['weekday'] if not residential else []))

sf_mat = sf_mat.drop(drop_cols, 1)

# Determine the h values
Expand Down
3 changes: 2 additions & 1 deletion demandlib/examples/power_demand_example.py
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Expand Up @@ -54,7 +54,8 @@ def power_example():
e_slp = bdew.ElecSlp(year, holidays=holidays)

# multiply given annual demand with timeseries
elec_demand = e_slp.get_profile(ann_el_demand_per_sector)
elec_demand = e_slp.get_profile(ann_el_demand_per_sector,
dyn_function_h0=True)

# Add the slp for the industrial group
ilp = profiles.IndustrialLoadProfile(e_slp.date_time_index,
Expand Down