def sampling_scaler(locator, random_variables, target_parameters, boolean_vars,
list_building_names, number_samples_scaler, nn_delay, gv,
config, climatic_variables, year, use_daysim_radiation,
use_stochastic_occupancy, region):
'''
this function creates a number of random samples for the entire district (city)
:param locator: points to the variables
:param random_variables: a list containing the names of variables associated with uncertainty (can be accessed from 'nn_settings.py')
:param target_parameters: a list containing the name of desirable outputs (can be accessed from 'nn_settings.py')
:param list_building_names: a list containing the name of desired buildings
:param weather_path: weather path
:param gv: global variables
:return: -
'''
# get number of buildings
size_city = np.shape(list_building_names)
size_city = size_city[0]
# create random samples of the entire district
for i in range(
number_samples_scaler
): # the parameter "number_samples" is accessible from 'nn_settings.py'
bld_counter = 0
# create list of samples with a LHC sampler and save to disk
samples, samples_norm, pdf_list = latin_sampler(
locator, size_city, random_variables, region)
# create a file of overides with the samples
dictionary = dict(zip(random_variables, samples.transpose()))
overides_dataframe = pd.DataFrame(dictionary)
overides_dataframe['Name'] = list_building_names
# replace the 1, 0 with True and False
for var in boolean_vars:
somthing = overides_dataframe['ECONOMIZER']
overides_dataframe[var].replace(1, 'True', inplace=True)
overides_dataframe[var].replace(0, 'False', inplace=True)
overides_dataframe[var].replace(0.0, 'False', inplace=True)
# save file so the demand calculation can know about it.
overides_dataframe.to_csv(locator.get_building_overrides())
# run cea demand
config.demand.override_variables = True
demand_main.demand_calculation(locator, config)
urban_input_matrix, urban_taget_matrix = input_prepare_main(
list_building_names, locator, target_parameters, nn_delay,
climatic_variables, config.region, year, use_daysim_radiation,
use_stochastic_occupancy)
scaler_inout_path = locator.get_minmaxscaler_folder()
file_path_inputs = os.path.join(scaler_inout_path,
"input%(i)s.csv" % locals())
data_file_inputs = pd.DataFrame(urban_input_matrix)
data_file_inputs.to_csv(file_path_inputs, header=False, index=False)
file_path_targets = os.path.join(scaler_inout_path,
"target%(i)s.csv" % locals())
data_file_targets = pd.DataFrame(urban_taget_matrix)
data_file_targets.to_csv(file_path_targets, header=False, index=False)
def sampling_single(locator, random_variables, target_parameters,
list_building_names, gv, config, nn_delay,
climatic_variables, region, year, use_daysim_radiation,
use_stochastic_occupancy):
size_city = np.shape(list_building_names)
size_city = size_city[0]
bld_counter = 0
# create list of samples with a LHC sampler and save to disk (*.csv)
samples, samples_norm, pdf_list = latin_sampler(locator, size_city,
random_variables, region)
for building_name in (list_building_names):
np.save(locator.get_calibration_folder(), samples)
building_load = config.single_calibration.load
override_file = Gdf.from_file(
locator.get_zone_geometry()).set_index('Name')
override_file = pd.DataFrame(index=override_file.index)
problem = {
'variables': random_variables,
'building_load': target_parameters,
'probabiltiy_vars': pdf_list
}
pickle.dump(
problem,
file(locator.get_calibration_problem(building_name, building_load),
'w'))
sample = np.asarray(zip(random_variables, samples[bld_counter, :]))
apply_sample_parameters(locator, sample, override_file)
bld_counter = bld_counter + 1
# read the saved *.csv file and replace Boolean with logical (True/False)
overwritten = pd.read_csv(locator.get_building_overrides())
bld_counter = 0
for building_name in (list_building_names):
sample = np.asarray(zip(random_variables, samples[bld_counter, :]))
for boolean_mask in (boolean_vars):
indices = np.where(sample == boolean_mask)
if sample[indices[0], 1] == '0.0':
sample[indices[0], 1] = 'False'
else:
sample[indices[0], 1] = 'True'
overwritten.loc[overwritten.Name == building_name,
random_variables] = sample[:, 1]
bld_counter = bld_counter + 1
# write to csv format
overwritten.to_csv(locator.get_building_overrides())
# run cea demand
demand_main.demand_calculation(locator, config)
# prepare the inputs for feeding into the neural network
urban_input_matrix, urban_taget_matrix = input_prepare_main(
list_building_names, locator, target_parameters, gv, nn_delay,
climatic_variables, region, year, use_daysim_radiation,
use_stochastic_occupancy)
return urban_input_matrix, urban_taget_matrix
def sampling_main(locator, config):
"""
This script creates samples using a lating Hypercube sample of 5 variables of interest.
then runs the demand calculation of CEA for all the samples. It delivers a json file storing
the results of cv_rmse and rmse for each sample.
for more details on the work behind this please check:
Rysanek A., Fonseca A., Schlueter, A. Bayesian calibration of Dyanmic building Energy Models. Applied Energy 2017.
:param locator: pointer to location of CEA files
:param variables: input variables of CEA to sample. They must be 6!
:param building_name: name of building to calibrate
:param building_load: name of building load to calibrate
:return:
1. a file storing values of cv_rmse and rmse for all samples. the file is sotred in
file(locator.get_calibration_cvrmse_file(building_name)
2 a file storing information about variables, the building_load and the probability distribtuions used in the
excercise. the file is stored in locator.get_calibration_problem(building_name)
:rtype: .json and .pkl
"""
# Local variables
number_samples = config.single_calibration.samples
variables = config.single_calibration.variables
building_name = config.single_calibration.building
building_load = config.single_calibration.load
override_file = Gdf.from_file(
locator.get_zone_geometry()).set_index('Name')
override_file = pd.DataFrame(index=override_file.index)
region = config.region
# Generate latin hypercube samples
latin_samples, latin_samples_norm, distributions = latin_sampler.latin_sampler(
locator, number_samples, variables, region)
# Run demand calulation for every latin sample
cv_rmse_list = []
rmse_list = []
for i in range(number_samples):
#create list of tuples with variables and sample
sample = zip(variables, latin_samples[i, :])
#create overrides and return pointer to files
apply_sample_parameters(locator, sample, override_file)
# run cea demand and calculate cv_rmse
simulation = simulate_demand_sample(locator, building_name,
building_load, config)
#calculate cv_rmse
measured = pd.read_csv(
locator.get_demand_measured_file(building_name))[building_load +
"_kWh"].values
cv_rmse, rmse = calc_cv_rmse(simulation, measured)
cv_rmse_list.append(cv_rmse)
rmse_list.append(rmse)
print("The cv_rmse for this iteration is:", cv_rmse)
# Save results into json
# Create problem and save to disk as a pickle
problem = {
'variables': variables,
'building_load': building_load,
'probabiltiy_vars': distributions,
'samples': latin_samples,
'samples_norm': latin_samples_norm,
'cv_rmse': cv_rmse_list,
'rmse': rmse_list
}
pickle.dump(
problem,
open(locator.get_calibration_problem(building_name, building_load),
'w'))
def sampling_main(locator, random_variables, target_parameters,
list_building_names, weather_path, multiprocessing, config,
nn_delay, climatic_variables, year):
'''
this function creates a number of random samples for the entire district (city)
:param locator: points to the variables
:param random_variables: a list containing the names of variables associated with uncertainty (can be accessed from 'nn_settings.py')
:param target_parameters: a list containing the name of desirable outputs (can be accessed from 'nn_settings.py')
:param list_building_names: a list containing the name of desired buildings
:param weather_path: weather path
:return: -
'''
# get number of buildings
size_city = np.shape(list_building_names)
size_city = size_city[0]
# create random samples of the entire district
for i in range(
number_samples
): # the parameter "number_samples" is accessible from 'nn_settings.py'
bld_counter = 0
# create list of samples with a LHC sampler and save to disk
samples, samples_norm, pdf_list = latin_sampler(
locator, size_city, random_variables)
#samples = samples[0] # extract the non-normalized samples
# create a file of overides with the samples
dictionary = dict(zip(random_variables, samples.transpose()))
overides_dataframe = pd.DataFrame(dictionary)
overides_dataframe['Name'] = list_building_names
# replace the 1, 0 with True and False
for var in boolean_vars:
overides_dataframe[var].replace(1, "True", inplace=True)
overides_dataframe[var].replace(0, "False", inplace=True)
overides_dataframe[var].replace(0.0, "False", inplace=True)
# save file so the demand calculation can know about it.
overides_dataframe.to_csv(locator.get_building_overrides())
# run cea demand
config.demand.override_variables = True
demand_main.demand_calculation(locator, config)
# prepare the inputs for feeding into the neural network
urban_input_matrix, urban_taget_matrix = input_prepare_main(
list_building_names, locator, target_parameters, nn_delay,
climatic_variables, year)
# drop half the inputs and targets to avoid overfitting and save RAM / Disk space
urban_input_matrix, urban_taget_matrix = input_dropout(
urban_input_matrix, urban_taget_matrix)
# get the pathfor saving the files
nn_inout_path = locator.get_nn_inout_folder()
# save inputs with sequential naming
file_path_inputs = os.path.join(nn_inout_path,
"input%(i)s.csv" % locals())
data_file_inputs = pd.DataFrame(urban_input_matrix)
data_file_inputs.to_csv(file_path_inputs, header=False, index=False)
# save inputs with sequential naming
file_path_targets = os.path.join(nn_inout_path,
"target%(i)s.csv" % locals())
data_file_targets = pd.DataFrame(urban_taget_matrix)
data_file_targets.to_csv(file_path_targets, header=False, index=False)
