def test_estimate_one_par(self):
'''Test the estimation of one parameter of a model.'''
# Set model paths
mopath = os.path.join(self.get_unittest_path(), 'resources', 'model',
'Simple.mo')
modelpath = 'Simple.RC_noinputs'
# Instantiate system
system = systems.EmulationFromFMU(self.measurements, \
moinfo = (mopath, modelpath, {}))
system.collect_measurements(self.start_time, self.final_time)
# Define parameters
parameter_data = {}
parameter_data['heatCapacitor.C'] = {}
parameter_data['heatCapacitor.C']['Value'] = variables.Static(
'C_Value', 55000, units.J_K)
parameter_data['heatCapacitor.C']['Minimum'] = variables.Static(
'C_Min', 10000, units.J_K)
parameter_data['heatCapacitor.C']['Maximum'] = variables.Static(
'C_Max', 1000000, units.J_K)
parameter_data['heatCapacitor.C']['Free'] = variables.Static(
'C_Free', True, units.boolean)
# Instantiate model
self.model = models.Modelica(models.JModelica, \
models.RMSE, \
self.measurements, \
moinfo = (mopath, modelpath, {}), \
parameter_data = parameter_data)
# Estimate models
self.model.estimate(self.start_time, self.final_time, ['T_db'])
# Check references
data = [
self.model.parameter_data['heatCapacitor.C']
['Value'].display_data()
]
index = ['heatCapacitor.C']
df_test = pd.DataFrame(data=data, index=index, columns=['Value'])
self.check_df(df_test, 'estimate_one_par.csv', timeseries=False)
def test_set_parameters(self):
'''Test the dynamic setting of parameters.
'''
modelpath = 'Simple.RC';
# Instantiate model
parameter_data = {'heatCapacitor.C' : {'Free' : variables.Static('C_free', False, units.boolean), \
'Value' : variables.Static('C_new', 1e5, units.J_K)}, \
'To' : {'Free' : variables.Static('To_free', False, units.boolean), \
'Value' : variables.Static('To', 24, units.degC)}};
model = models.Modelica(models.JModelica, \
models.RMSE, \
self.measurements, \
moinfo = (self.mopath, modelpath, {}), \
control_data = self.controls.data,
parameter_data = parameter_data);
# Instantiate optimization problem
opt_problem = optimization.Optimization(model, \
optimization.EnergyMin, \
optimization.JModelica, \
'q_flow', \
constraint_data = self.constraints.data);
# Solve optimization problem
opt_problem.optimize(self.start_time, self.final_time);
# Check references
df_test = opt_problem.display_measurements('Simulated');
self.check_df(df_test, 'optimize_set_parameters_1.csv');
# Set new parameters of model
parameter_data['heatCapacitor.C']['Value'] = variables.Static('C_new', 1e7, units.J_K);
parameter_data['To']['Value'] = variables.Static('To', 22, units.degC);
opt_problem.Model.parameter_data = parameter_data;
# Solve optimization problem
opt_problem.optimize(self.start_time, self.final_time);
# Check references
df_test = opt_problem.display_measurements('Simulated');
self.check_df(df_test, 'optimize_set_parameters_2.csv');
def test_estimate_and_validate(self):
'''Test the estimation of a model's coefficients based on measured data.'''
# Instantiate model
self.model = models.Modelica(models.UKF, \
models.RMSE, \
self.system.measurements, \
moinfo = self.moinfo, \
parameter_data = self.parameters.data, \
control_data = self.controls.data, \
version = '1.0')
# Estimate
self.model.estimate(self.start_time, self.final_time, ['T_db'])
# Validate
self.model.validate(self.start_time,
self.final_time,
'validate',
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.csv', timeseries=False)
def test_estimate_and_validate(self):
'''Test the estimation of a model's coefficients based on measured data.'''
plt.close('all')
# Exogenous collection time
self.start_time_exodata = '1/1/2015'
self.final_time_exodata = '1/30/2015'
# Emulation time
self.start_time_emulation = '1/1/2015'
self.final_time_emulation = '1/4/2015'
# 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)
# Set exodata to building emulation
self.building.weather_data = self.weather.data
self.building.internal_data = self.internal.data
self.building.control_data = self.control.data
self.building.tz_name = self.weather.tz_name
# Collect measurement data
self.building.collect_measurements(self.start_time_emulation,
self.final_time_emulation)
# Instantiate model
self.model = models.Modelica(self.estimate_method, \
self.validation_method, \
self.building.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)
# Estimate model based on emulated data
self.model.estimate(self.start_time_estimation,
self.final_time_estimation,
self.measurement_variable_list)
# Check references
df_test = self.model.display_measurements('Simulated')
self.check_df_timeseries(df_test, 'simulate_estimated_parameters.csv')
# Validate on validation data
self.building.collect_measurements(self.start_time_validation,
self.final_time_validation)
self.model.measurements = self.building.measurements
self.model.validate(self.start_time_validation, self.final_time_validation, \
os.path.join(self.MPCPyPath, 'unittests', 'resources', 'model_validation'))
# 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_general(df_test, 'validate_RMSE.csv')
def test_fmi_version(self):
for version in ['1.0', '2.0']:
building = systems.EmulationFromFMU({}, moinfo = (self.mopath, self.modelpath, {}), version = version);
self.assertEqual(building.input_names, ['q_flow']);
model = models.Modelica(models.JModelica, models.RMSE, {}, moinfo = (self.mopath, self.modelpath, {}), version = version);
self.assertEqual(model.input_names, ['q_flow']);
def test_fmi_default(self):
building = systems.EmulationFromFMU({}, moinfo = (self.mopath, self.modelpath, {}));
self.assertEqual(building.fmu_version, '2.0');
model = models.Modelica(models.JModelica, models.RMSE, {}, moinfo = (self.mopath, self.modelpath, {}));
self.assertEqual(model.fmu_version, '2.0');
def param_estimation(model_param, ADD_UNIT_EP_TZONE):
# Simulation settings
estimation_start_time = model_param['estimation_start_time']
estimation_stop_time = model_param['estimation_stop_time']
validation_start_time = model_param['validation_start_time']
validation_stop_time = model_param['validation_stop_time']
pat_wea = model_param['wea']
variable_map = eval(model_param['var_map'])
con_fil = model_param['con_fil']
obs_var = model_param['obs_var']
step_size = model_param['emulator_step_size']
fmu_path = model_param['fmu_path']
param_fil = model_param['param_fil']
moinfo = model_param['rc_param']
# Setup for running the weather FMU
weather = exodata.WeatherFromEPW(pat_wea)
control = exodata.ControlFromCSV(con_fil,
variable_map,
tz_name=weather.tz_name)
# Running the weather data FMU
weather.collect_data(estimation_start_time, estimation_stop_time)
# Collecting the data from the CSV file
control.collect_data(estimation_start_time, estimation_stop_time)
# Setting up parameters for emulator model (EnergyPlusFMU)
from mpcpy import systems
measurements = {obs_var: {}}
measurements[obs_var]['Sample'] = variables.Static('sample_rate_Tzone',
step_size, units.s)
fmupath = fmu_path
print("=========Run emulation model with FMU={!s}".format(fmu_path))
emulation = systems.EmulationFromFMU(measurements,
fmupath=fmu_path,
control_data=control.data,
tz_name=weather.tz_name)
# Running the emulator EnergyPlus FMU
emulation.collect_measurements(estimation_start_time, estimation_stop_time)
# Add units to EnergyPlus output variables
if ((ADD_UNIT_EP_TZONE == True) and obs_var == 'Tzone'):
print(
"==========WARNING: When using E+FMU, if the output of E+ is the "
"zone temperature, then the next lines will add the unit degC to the "
"output results. This is only valid for E+ and an output which is Tzone."
)
data = measurements[obs_var]["Measured"].display_data()
name = measurements[obs_var]["Measured"].name
# Set new variable with same data and name and units degC
measurements[obs_var]["Measured"] = variables.Timeseries(
name, data, units.degC)
from mpcpy import models
parameters = exodata.ParameterFromCSV(param_fil)
parameters.collect_data()
parameters.display_data()
# Defning model to be use for parameter estimation
model = models.Modelica(models.JModelica,
models.RMSE,
emulation.measurements,
moinfo=moinfo,
parameter_data=parameters.data,
weather_data=weather.data,
control_data=control.data,
tz_name=weather.tz_name)
#print("=========Simulate model with default parameters={!s}".format(moinfo))
#model.simulate('1/1/2017', '1/2/2017')
#model.parameter_data['zone.T0']['Value'].set_data(model.get_base_measurements('Measured')['Tzone'].loc[start_time_est_utc])
#model.display_measurements('Simulated')
print("=========Run parameter estimation for model={!s}".format(moinfo))
print("=========Start time={!s}, Stop time={!s}, Observed variable={!s}".
format(estimation_start_time, estimation_stop_time, obs_var))
model.estimate(estimation_start_time, estimation_stop_time, [obs_var])
# Validate the estimation model by comparing measured vs. simulated data
# IMPORTANT: The accuracy of the validation depends on the initial temperature set
# in the Modelica models. A parameter T0 is defined to set the initial
# temperatures of the room air or internal mass. this should be set to
# to the initial temperatures measured from the emulator.
model.validate(validation_start_time,
validation_stop_time,
'validate_tra',
plot=1)
print("The Root Mean Square Error={!s}".format(
model.RMSE['Tzone'].display_data()))
# Printing simulation results
for key in model.parameter_data.keys():
print(key, model.parameter_data[key]['Value'].display_data())
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 test_estimate_and_validate(self):
'''Test the estimation of a model's coefficients based on measured data.'''
plt.close('all')
# 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)
# Set exodata to building emulation
self.building.weather_data = self.weather.data
self.building.internal_data = self.internal.data
self.building.control_data = self.control.data
self.building.tz_name = self.weather.tz_name
# Collect measurement data
self.building.collect_measurements(self.start_time_estimation,
self.final_time_estimation)
# Instantiate model
self.model = models.Modelica(self.estimate_method, \
self.validation_method, \
self.building.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,
save_parameter_input_data=True)
# Simulate model with initial guess
self.model.simulate(self.start_time_estimation,
self.final_time_estimation)
# Check references
df_test = self.model.display_measurements('Simulated')
self.check_df(df_test, 'simulate_initial_parameters.csv')
# Check parameter and input data were saved
df_test = pd.read_csv('mpcpy_simulation_inputs_model.csv',
index_col='Time')
df_test.index = pd.to_datetime(df_test.index).tz_localize('UTC')
self.check_df(df_test, 'mpcpy_simulation_inputs_model.csv')
df_test = pd.read_csv('mpcpy_simulation_parameters_model.csv',
index_col='parameter')
self.check_df(df_test,
'mpcpy_simulation_parameters_model.csv',
timeseries=False)
# 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'), 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)
# Validate on validation data
self.building.collect_measurements(self.start_time_validation,
self.final_time_validation)
self.model.measurements = self.building.measurements
self.model.validate(self.start_time_validation, self.final_time_validation, \
os.path.join(self.get_unittest_path(), 'outputs', 'model_validation'), 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.csv', timeseries=False)
def test_optimize(self):
'''Test the optimization of a model.
'''
modelpath = 'Simple.RC'
# Instantiate model
model = models.Modelica(models.JModelica, \
models.RMSE, \
self.measurements, \
moinfo = (self.mopath, modelpath, {}), \
control_data = self.controls.data)
# Instantiate optimization problem
opt_problem = optimization.Optimization(model, \
optimization.EnergyMin, \
optimization.JModelica, \
'q_flow', \
constraint_data = self.constraints.data)
# Solve optimization problem with default res_control_step
opt_problem.optimize(self.start_time, self.final_time)
# Check references
df_test = opt_problem.display_measurements('Simulated')
self.check_df(df_test, 'optimize_measurements.csv')
df_test = model.control_data['q_flow'].display_data().to_frame()
df_test.index.name = 'Time'
self.check_df(df_test, 'optimize_control_default.csv')
# Simulate model with optimal control
model.simulate(self.start_time, self.final_time)
# Check references
df_test = model.display_measurements('Simulated')
self.check_df(df_test, 'simulate_optimal_default.csv')
# Solve optimization problem with user-defined res_control_step
opt_problem.optimize(self.start_time,
self.final_time,
res_control_step=60.0)
# Check references
df_test = opt_problem.display_measurements('Simulated')
self.check_df(df_test, 'optimize_measurements.csv')
df_test = model.control_data['q_flow'].display_data().to_frame()
df_test.index.name = 'Time'
self.check_df(df_test, 'optimize_control_userdefined.csv')
# Simulate model with optimal control
model.simulate(self.start_time, self.final_time)
# Check references
df_test = model.display_measurements('Simulated')
self.check_df(df_test, 'simulate_optimal_userdefined.csv')
# Get opt input object and create dataframe
time = np.linspace(0, 3600, 3601)
df_test = pd.DataFrame()
data = []
opt_input_traj = opt_problem.opt_input[1]
names = opt_problem.opt_input[0]
# Create data
for t in time:
data.append(opt_input_traj(t))
# Create index
timedelta = pd.to_timedelta(time, 's')
index = pd.to_datetime(self.start_time) + timedelta
df_test = pd.DataFrame(data=data, index=index, columns=names)
df_test.index.name = 'Time'
# Check references
self.check_df(df_test, 'optimize_opt_input.csv')
def setUp(self):
## 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
self.zone_names = ['wes', 'hal', 'eas']
# 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)
}
## Exodata
# Exogenous collection time
self.start_time_exodata = '1/1/2015'
self.final_time_exodata = '1/30/2015'
# Optimization time
self.start_time_optimization = '1/2/2015'
self.final_time_optimization = '1/3/2015'
# Weather
self.weather_path = os.path.join(
self.get_unittest_path(), 'resources', 'weather',
'USA_IL_Chicago-OHare.Intl.AP.725300_TMY3.epw')
self.weather = exodata.WeatherFromEPW(self.weather_path)
self.weather.collect_data(self.start_time_exodata,
self.final_time_exodata)
# Internal
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.internal = exodata.InternalFromCSV(self.internal_path,
self.internal_variable_map,
tz_name=self.weather.tz_name)
self.internal.collect_data(self.start_time_exodata,
self.final_time_exodata)
# Control (as initialization)
self.control_path = os.path.join(self.get_unittest_path(), 'resources',
'optimization', 'ControlCSV.csv')
self.control_variable_map = {'conHeat_wes' : ('conHeat_wes', units.unit1), \
'conHeat_hal' : ('conHeat_hal', units.unit1), \
'conHeat_eas' : ('conHeat_eas', units.unit1)}
self.control = exodata.ControlFromCSV(self.control_path,
self.control_variable_map,
tz_name=self.weather.tz_name)
self.control.collect_data(self.start_time_exodata,
self.final_time_exodata)
# Parameters
self.parameters_path = os.path.join(self.get_unittest_path(),
'outputs', 'model_parameters.txt')
self.parameters = exodata.ParameterFromCSV(self.parameters_path)
self.parameters.collect_data()
# Constraints
self.constraints_path = os.path.join(
self.get_unittest_path(), 'resources', 'optimization',
'sampleConstraintCSV_Constant.csv')
self.constraints_variable_map = {'wesTdb_min' : ('wesTdb', 'GTE', units.degC), \
'wesTdb_max' : ('wesTdb', 'LTE', units.degC), \
'easTdb_min' : ('easTdb', 'GTE', units.degC), \
'easTdb_max' : ('easTdb', 'LTE', units.degC), \
'halTdb_min' : ('halTdb', 'GTE', units.degC), \
'halTdb_max' : ('halTdb', 'LTE', units.degC), \
'der_wesTdb_min' : ('wesTdb', 'dGTE', units.K), \
'der_wesTdb_max' : ('wesTdb', 'dLTE', units.K), \
'der_easTdb_min' : ('easTdb', 'dGTE', units.K), \
'der_easTdb_max' : ('easTdb', 'dLTE', units.K), \
'der_halTdb_min' : ('halTdb', 'dGTE', units.K), \
'der_halTdb_max' : ('halTdb', 'dLTE', units.K), \
'conHeat_wes_min' : ('conHeat_wes', 'GTE', units.unit1), \
'conHeat_wes_max' : ('conHeat_wes', 'LTE', units.unit1), \
'conHeat_hal_min' : ('conHeat_hal', 'GTE', units.unit1), \
'conHeat_hal_max' : ('conHeat_hal', 'LTE', units.unit1), \
'conHeat_eas_min' : ('conHeat_eas', 'GTE', units.unit1), \
'conHeat_eas_max' : ('conHeat_eas', 'LTE', units.unit1)}
self.constraints = exodata.ConstraintFromCSV(
self.constraints_path,
self.constraints_variable_map,
tz_name=self.weather.tz_name)
self.constraints.collect_data(self.start_time_exodata,
self.final_time_exodata)
self.constraints.data['wesTdb']['Cyclic'] = variables.Static(
'wesTdb_cyclic', 1, units.boolean_integer)
self.constraints.data['easTdb']['Cyclic'] = variables.Static(
'easTdb_cyclic', 1, units.boolean_integer)
self.constraints.data['halTdb']['Cyclic'] = variables.Static(
'halTdb_cyclic', 1, units.boolean_integer)
# Prices
self.prices_path = os.path.join(self.get_unittest_path(), 'resources',
'optimization', 'PriceCSV.csv')
self.price_variable_map = {
'pi_e': ('pi_e', units.unit1)
}
self.prices = exodata.PriceFromCSV(self.prices_path,
self.price_variable_map,
tz_name=self.weather.tz_name)
self.prices.collect_data(self.start_time_exodata,
self.final_time_exodata)
## Parameters
self.parameters.data['lat'] = {}
self.parameters.data['lat']['Value'] = self.weather.lat
## Instantiate model
self.model = models.Modelica(self.estimate_method, \
self.validation_method, \
self.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)
