def setUp(self):
# Testing time
self.start_time = '3/8/2013'
self.final_time = '3/15/2013 23:59'
# Set path variable(s)
self.MPCPyPath = utility.get_MPCPy_path()
# Setup building measurement collection from csv
self.csv_filepath = self.MPCPyPath + os.sep + 'resources' + os.sep + 'building' + os.sep + 'OccData.csv'
# Measurements
self.measurements = {}
self.measurements['occupancy'] = {
'Sample': variables.Static('occupancy_sample', 300, units.s)
}
self.measurement_variable_map = {
'Total People Count for the whole building (+)':
('occupancy', units.unit1)
}
# Instantiate building measurement source
self.building = systems.RealFromCSV(self.csv_filepath, \
self.measurements,
self.measurement_variable_map,
time_header = 'Date')
# Where to save ref occupancy model
self.occupancy_model_file = self.get_ref_path(
) + os.sep + 'occupancy_model_estimated.txt'
def setUp(self):
# Setup building measurement collection from csv
self.csv_filepath = os.path.join(self.get_unittest_path(), 'resources', 'building', 'OccData.csv');
# Measurements
self.measurements = {};
self.measurements['occupancy'] = {'Sample' : variables.Static('occupancy_sample', 300, units.s)};
self.measurement_variable_map = {'Total People Count for the whole building (+)' : ('occupancy', units.unit1)};
# Instantiate building measurement source
self.building = systems.RealFromCSV(self.csv_filepath, \
self.measurements,
self.measurement_variable_map,
time_header = 'Date');
def test_estimate_and_validate_missing_measurements(self):
'''Test the estimation of a model's coefficients based on measured data.
Some of the validation measurement data is missing.
'''
plt.close('all')
# Check references
df_test = self.model.display_measurements('Simulated')
self.check_df(df_test, 'simulate_initial_parameters.csv')
# Estimate model based on emulated data
self.model.estimate(self.start_time_estimation,
self.final_time_estimation,
self.measurement_variable_list)
# Validate model based on estimation data
self.model.validate(self.start_time_estimation, self.final_time_estimation, \
os.path.join(self.get_unittest_path(), 'outputs', 'model_estimation_csv'), plot=0)
# Check references
RMSE = {}
for key in self.model.RMSE.keys():
RMSE[key] = {}
RMSE[key]['Value'] = self.model.RMSE[key].display_data()
df_test = pd.DataFrame(data=RMSE)
self.check_df(df_test, 'estimate_RMSE.csv', timeseries=False)
# Instantiate validate building
building_val = systems.RealFromCSV(
self.building_source_file_path_val_missing,
self.measurements,
self.measurement_variable_map,
tz_name=self.weather.tz_name)
# Validate on validation data
building_val.collect_measurements(self.start_time_validation,
self.final_time_validation)
self.model.measurements = building_val.measurements
self.model.validate(self.start_time_validation, self.final_time_validation, \
os.path.join(self.get_unittest_path(), 'outputs', 'model_validation_csv'), plot=0)
# Check references
RMSE = {}
for key in self.model.RMSE.keys():
RMSE[key] = {}
RMSE[key]['Value'] = self.model.RMSE[key].display_data()
df_test = pd.DataFrame(data=RMSE)
self.check_df(df_test, 'validate_RMSE_missing.csv', timeseries=False)
def _finish_estimate_validate(self, tag):
'''Internal method for finishing the estimate and valudate tests.'''
# Validate model based on estimation data
self.model.validate(self.start_time_estimation, self.final_time_estimation, \
os.path.join(self.get_unittest_path(), 'outputs', 'model_estimation_csv'), plot=0)
# Check references
RMSE = {}
for key in self.model.RMSE.keys():
RMSE[key] = {}
RMSE[key]['Value'] = self.model.RMSE[key].display_data()
df_test = pd.DataFrame(data=RMSE)
self.check_df(df_test,
'estimate_RMSE{0}.csv'.format(tag),
timeseries=False)
# All estimates if global estimate
try:
glo_est_data_test = self.model.get_global_estimate_data()
self.check_json(glo_est_data_test,
'estimate_gloest{0}.txt'.format(tag))
except:
pass
# Instantiate validate building
self.building_val = systems.RealFromCSV(
self.building_source_file_path_val,
self.measurements,
self.measurement_variable_map,
tz_name=self.weather.tz_name)
# Validate on validation data
self.building_val.collect_measurements(self.start_time_validation,
self.final_time_validation)
self.model.measurements = self.building_val.measurements
self.model.validate(self.start_time_validation, self.final_time_validation, \
os.path.join(self.get_unittest_path(), 'outputs', 'model_validation_csv'), plot=0)
# Check references
RMSE = {}
for key in self.model.RMSE.keys():
RMSE[key] = {}
RMSE[key]['Value'] = self.model.RMSE[key].display_data()
df_test = pd.DataFrame(data=RMSE)
self.check_df(df_test,
'validate_RMSE{0}.csv'.format(tag),
timeseries=False)
def setUp(self):
## Setup building fmu emulation
self.building_source_file_path_est = os.path.join(
self.get_unittest_path(), 'resources', 'building',
'RealMeasurements_est.csv')
self.building_source_file_path_val = os.path.join(
self.get_unittest_path(), 'resources', 'building',
'RealMeasurements_val.csv')
self.building_source_file_path_val_missing = os.path.join(
self.get_unittest_path(), 'resources', 'building',
'RealMeasurements_val_missing.csv')
self.zone_names = ['wes', 'hal', 'eas']
self.weather_path = os.path.join(
self.get_unittest_path(), 'resources', 'weather',
'USA_IL_Chicago-OHare.Intl.AP.725300_TMY3.epw')
self.internal_path = os.path.join(self.get_unittest_path(),
'resources', 'internal',
'sampleCSV.csv')
self.internal_variable_map = {'intRad_wes' : ('wes', 'intRad', units.W_m2), \
'intCon_wes' : ('wes', 'intCon', units.W_m2), \
'intLat_wes' : ('wes', 'intLat', units.W_m2), \
'intRad_hal' : ('hal', 'intRad', units.W_m2), \
'intCon_hal' : ('hal', 'intCon', units.W_m2), \
'intLat_hal' : ('hal', 'intLat', units.W_m2), \
'intRad_eas' : ('eas', 'intRad', units.W_m2), \
'intCon_eas' : ('eas', 'intCon', units.W_m2), \
'intLat_eas' : ('eas', 'intLat', units.W_m2)}
self.control_path = os.path.join(self.get_unittest_path(), 'resources',
'building', 'ControlCSV_0.csv')
self.control_variable_map = {'conHeat_wes' : ('conHeat_wes', units.unit1), \
'conHeat_hal' : ('conHeat_hal', units.unit1), \
'conHeat_eas' : ('conHeat_eas', units.unit1)}
# Measurements
self.measurements = {}
self.measurements['wesTdb'] = {
'Sample': variables.Static('wesTdb_sample', 1800, units.s)
}
self.measurements['halTdb'] = {
'Sample': variables.Static('halTdb_sample', 1800, units.s)
}
self.measurements['easTdb'] = {
'Sample': variables.Static('easTdb_sample', 1800, units.s)
}
self.measurements['wesPhvac'] = {
'Sample': variables.Static('easTdb_sample', 1800, units.s)
}
self.measurements['halPhvac'] = {
'Sample': variables.Static('easTdb_sample', 1800, units.s)
}
self.measurements['easPhvac'] = {
'Sample': variables.Static('easTdb_sample', 1800, units.s)
}
self.measurements['Ptot'] = {
'Sample': variables.Static('easTdb_sample', 1800, units.s)
}
self.measurement_variable_map = {
'wesTdb_mea': ('wesTdb', units.K),
'halTdb_mea': ('halTdb', units.K),
'easTdb_mea': ('easTdb', units.K),
'wesPhvac_mea': ('wesPhvac', units.W),
'halPhvac_mea': ('halPhvac', units.W),
'easPhvac_mea': ('easPhvac', units.W),
'Ptot_mea': ('Ptot', units.W)
}
## Setup model
self.mopath = os.path.join(self.get_unittest_path(), 'resources',
'model', 'LBNL71T_MPC.mo')
self.modelpath = 'LBNL71T_MPC.MPC'
self.libraries = os.environ.get('MODELICAPATH')
self.estimate_method = models.JModelica
self.validation_method = models.RMSE
# Instantiate exo data sources
self.weather = exodata.WeatherFromEPW(self.weather_path)
self.internal = exodata.InternalFromCSV(self.internal_path,
self.internal_variable_map,
tz_name=self.weather.tz_name)
self.control = exodata.ControlFromCSV(self.control_path,
self.control_variable_map,
tz_name=self.weather.tz_name)
# Parameters
self.parameters = exodata.ParameterFromCSV(
os.path.join(self.get_unittest_path(), 'resources', 'model',
'LBNL71T_Parameters.csv'))
self.parameters.collect_data()
self.parameters.data['lat'] = {}
self.parameters.data['lat']['Value'] = self.weather.lat
# Instantiate test building
self.building_est = systems.RealFromCSV(
self.building_source_file_path_est,
self.measurements,
self.measurement_variable_map,
tz_name=self.weather.tz_name)
# Exogenous collection time
self.start_time_exodata = '1/1/2015'
self.final_time_exodata = '1/30/2015'
# Estimation time
self.start_time_estimation = '1/1/2015'
self.final_time_estimation = '1/4/2015'
# Validation time
self.start_time_validation = '1/4/2015'
self.final_time_validation = '1/5/2015'
# Measurement variables for estimate
self.measurement_variable_list = ['wesTdb', 'easTdb', 'halTdb']
# Exodata
self.weather.collect_data(self.start_time_exodata,
self.final_time_exodata)
self.internal.collect_data(self.start_time_exodata,
self.final_time_exodata)
self.control.collect_data(self.start_time_exodata,
self.final_time_exodata)
# Collect measurement data
self.building_est.collect_measurements(self.start_time_estimation,
self.final_time_estimation)
# Instantiate model
self.model = models.Modelica(self.estimate_method, \
self.validation_method, \
self.building_est.measurements, \
moinfo = (self.mopath, self.modelpath, self.libraries), \
zone_names = self.zone_names, \
weather_data = self.weather.data, \
internal_data = self.internal.data, \
control_data = self.control.data, \
parameter_data = self.parameters.data, \
tz_name = self.weather.tz_name)
# Simulate model with initial guess
self.model.simulate(self.start_time_estimation,
self.final_time_estimation)
def setUp(self):
## Setup building fmu emulation
self.building_source_file_path_est = os.path.join(
self.get_unittest_path(), 'resources', 'building',
'RealMeasurements_est.csv')
self.building_source_file_path_val = os.path.join(
self.get_unittest_path(), 'resources', 'building',
'RealMeasurements_val.csv')
self.zone_names = ['wes', 'hal', 'eas']
self.weather_path = os.path.join(
self.get_unittest_path(), 'resources', 'weather',
'USA_IL_Chicago-OHare.Intl.AP.725300_TMY3.epw')
self.internal_path = os.path.join(self.get_unittest_path(),
'resources', 'internal',
'sampleCSV.csv')
self.internal_variable_map = {'intRad_wes' : ('wes', 'intRad', units.W_m2), \
'intCon_wes' : ('wes', 'intCon', units.W_m2), \
'intLat_wes' : ('wes', 'intLat', units.W_m2), \
'intRad_hal' : ('hal', 'intRad', units.W_m2), \
'intCon_hal' : ('hal', 'intCon', units.W_m2), \
'intLat_hal' : ('hal', 'intLat', units.W_m2), \
'intRad_eas' : ('eas', 'intRad', units.W_m2), \
'intCon_eas' : ('eas', 'intCon', units.W_m2), \
'intLat_eas' : ('eas', 'intLat', units.W_m2)}
self.control_path = os.path.join(self.get_unittest_path(), 'resources',
'building', 'ControlCSV_0.csv')
self.control_variable_map = {'conHeat_wes' : ('conHeat_wes', units.unit1), \
'conHeat_hal' : ('conHeat_hal', units.unit1), \
'conHeat_eas' : ('conHeat_eas', units.unit1)}
# Measurements
self.measurements = {}
self.measurements['wesTdb'] = {
'Sample': variables.Static('wesTdb_sample', 1800, units.s)
}
self.measurements['halTdb'] = {
'Sample': variables.Static('halTdb_sample', 1800, units.s)
}
self.measurements['easTdb'] = {
'Sample': variables.Static('easTdb_sample', 1800, units.s)
}
self.measurements['wesPhvac'] = {
'Sample': variables.Static('easTdb_sample', 1800, units.s)
}
self.measurements['halPhvac'] = {
'Sample': variables.Static('easTdb_sample', 1800, units.s)
}
self.measurements['easPhvac'] = {
'Sample': variables.Static('easTdb_sample', 1800, units.s)
}
self.measurements['Ptot'] = {
'Sample': variables.Static('easTdb_sample', 1800, units.s)
}
self.measurement_variable_map = {
'wesTdb_mea': ('wesTdb', units.K),
'halTdb_mea': ('halTdb', units.K),
'easTdb_mea': ('easTdb', units.K),
'wesPhvac_mea': ('wesPhvac', units.W),
'halPhvac_mea': ('halPhvac', units.W),
'easPhvac_mea': ('easPhvac', units.W),
'Ptot_mea': ('Ptot', units.W)
}
## Setup model
self.mopath = os.path.join(self.get_unittest_path(), 'resources',
'model', 'LBNL71T_MPC.mo')
self.modelpath = 'LBNL71T_MPC.MPC'
self.libraries = os.environ.get('MODELICAPATH')
self.estimate_method = models.JModelica
self.validation_method = models.RMSE
# Instantiate exo data sources
self.weather = exodata.WeatherFromEPW(self.weather_path)
self.internal = exodata.InternalFromCSV(self.internal_path,
self.internal_variable_map,
tz_name=self.weather.tz_name)
self.control = exodata.ControlFromCSV(self.control_path,
self.control_variable_map,
tz_name=self.weather.tz_name)
# Parameters
self.parameters = exodata.ParameterFromCSV(
os.path.join(self.get_unittest_path(), 'resources', 'model',
'LBNL71T_Parameters.csv'))
self.parameters.collect_data()
self.parameters.data['lat'] = {}
self.parameters.data['lat']['Value'] = self.weather.lat
# Instantiate test building
self.building_est = systems.RealFromCSV(
self.building_source_file_path_est,
self.measurements,
self.measurement_variable_map,
tz_name=self.weather.tz_name)
# Instantiate validate building
self.building_val = systems.RealFromCSV(
self.building_source_file_path_val,
self.measurements,
self.measurement_variable_map,
tz_name=self.weather.tz_name)
