def setUp(self):
self.start_time = '1/1/2017'
self.final_time = '1/2/2017'
# Set .mo path
self.mopath = os.path.join(utility.get_MPCPy_path(), 'resources',
'model', 'Simple.mo')
# Gather inputs
control_csv_filepath = utility.get_MPCPy_path(
) + os.sep + 'resources' + os.sep + 'model' + os.sep + 'SimpleRC_Input.csv'
control_variable_map = {
'q_flow_csv': ('q_flow', units.W)
}
self.controls = exodata.ControlFromCSV(control_csv_filepath,
control_variable_map)
self.controls.collect_data(self.start_time, self.final_time)
# Set measurements
self.measurements = {}
self.measurements['T_db'] = {
'Sample': variables.Static('T_db_sample', 1800, units.s)
}
self.measurements['q_flow'] = {
'Sample': variables.Static('q_flow_sample', 1800, units.s)
}
# Gather constraints
constraint_csv_filepath = utility.get_MPCPy_path(
) + os.sep + 'resources' + os.sep + 'optimization' + os.sep + 'SimpleRC_Constraints.csv'
constraint_variable_map = {'q_flow_min' : ('q_flow', 'GTE', units.W), \
'T_db_min' : ('T_db', 'GTE', units.K), \
'T_db_max' : ('T_db', 'LTE', units.K)}
self.constraints = exodata.ConstraintFromCSV(constraint_csv_filepath,
constraint_variable_map)
self.constraints.collect_data(self.start_time, self.final_time)
self.constraints.data['T_db']['Initial'] = variables.Static(
'T_db_start', 295, units.K)
def get_constraints(self,start,end, **kwargs):
self.constraints = exodata.ConstraintFromCSV(self.constraint_csv, self.optcon_varmap)
index = pd.date_range(start, end, freq = str(self.meas_sampl) +'S')
# Set points based on UCL paper on set points!!! High variability? Commercial?
mor_start = np.random.randint(7,8)
mor_end = np.random.randint(7,8)
eve_start = np.random.randint(17,18)
eve_end = np.random.randint(19,23)
dem_temp = np.random.randint(19,21)
t_min = pd.Series(16+273.15,index=index) # Contracted?
t_max = pd.Series(24+273.15,index=index) # Contracted?
ppl_nr = np.random.randint(1,5)
radgain = pd.Series(np.random.rand(len(index))*100,index=index)
zero_signal = pd.Series(np.zeros(len(index)),index=index)
control_signal1 = pd.Series(np.random.rand(len(index))*0,index=index)
for i in index:
if i.hour >= mor_start and i.hour <= mor_end:
t_min[i] = dem_temp-1+273.15
t_max[i] = dem_temp+1+273.15
radgain[i] = np.random.uniform(0,1)*50*np.random.randint(0,ppl_nr)
control_signal1[i] = np.random.uniform(0.2,0.6)
if i.hour >= eve_start and i.hour <= eve_end:
t_min[i] = dem_temp-1+273.15
t_max[i] = dem_temp+1+273.15
radgain[i] = np.random.uniform(0,1)*50*np.random.randint(0,ppl_nr)
control_signal1[i] = np.random.uniform(0.2,0.6)
pheat_min = pd.Series(0,index=index)
pheat_max = pd.Series(1,index=index)
if 'set_change' in kwargs.keys():
set_change = kwargs['set_change']
else:
set_change = 1
#print(t_min)
self.constraints.data = {
'HPPower':
{'GTE': variables.Timeseries('HPPower_GTE', pheat_min, units.W, tz_name=self.weather.tz_name),
'LTE': variables.Timeseries('HPPower_LTE', pheat_max, units.W,tz_name=self.weather.tz_name)},
'TAir':
{'Slack_GTE': variables.Timeseries('TAir_Slack_GTE', t_min, units.K, tz_name=self.weather.tz_name),
'Slack_LTE': variables.Timeseries('TAir_Slack_LTE', t_max, units.K, tz_name=self.weather.tz_name)}
}
self.constraints_reg = self.constraints # Regular constraints
self.other_input.data = {"ConvGain1": variables.Timeseries('ConvGain1', zero_signal,units.W,tz_name=self.weather.tz_name),
"RadGain": variables.Timeseries('RadGain', radgain,units.W,tz_name=self.weather.tz_name)
}
if "upd_control" in kwargs.keys():
self.control.data = {"HPPower": variables.Timeseries('HPPower', control_signal1,units.W,tz_name=self.weather.tz_name)}
store_namespace('control_'+self.building,self.control)
store_namespace('constraints_'+self.building,self.constraints)
store_namespace('other_input_'+self.building,self.other_input)
def get_constraints(self, start, end):
self.constraints = exodata.ConstraintFromCSV(self.constraint_csv,
self.optcon_varmap)
#print("%%%% constraint data %%%%")
#print(self.constraints)
#print(self.constraints.data)
index = pd.date_range(start, end, freq=str(self.meas_sampl) + 'S')
# Set points based on UCL paper on set points!!! High variability? Commercial?
mor_start = np.random.randint(5, 9)
mor_end = np.random.randint(9, 12)
eve_start = np.random.randint(15, 19)
eve_end = np.random.randint(19, 23)
dem_temp = np.random.randint(18, 23)
t_min = pd.Series(14 + 273.15, index=index) # Contracted?
t_max = pd.Series(28 + 273.15, index=index) # Contracted?
for i in index:
if i.hour >= mor_start and i.hour <= mor_end:
t_min[i] = dem_temp - 1 + 273.15
t_max[i] = dem_temp + 1 + 273.15
if i.hour >= eve_start and i.hour <= eve_end:
t_min[i] = dem_temp - 1 + 273.15
t_max[i] = dem_temp + 1 + 273.15
pheat_min = pd.Series(0, index=index)
pheat_max = pd.Series(10000, index=index)
#print(t_min)
self.constraints.data = {
'TAir': {
'GTE':
variables.Timeseries('TAir_GTE',
t_min,
units.K,
tz_name=self.weather.tz_name),
'LTE':
variables.Timeseries('TAir_LTE',
t_max,
units.K,
tz_name=self.weather.tz_name)
},
'ConvGain2': {
'GTE':
variables.Timeseries('ConvGain2_GTE',
pheat_min,
units.W,
tz_name=self.weather.tz_name),
'LTE':
variables.Timeseries('ConvGain2_LTE',
pheat_max,
units.W,
tz_name=self.weather.tz_name)
}
}
store_namespace('constraints_' + self.building, self.constraints)
def setUp(self):
csv_filepath = os.path.join(self.get_unittest_path(), 'resources', 'optimization', 'sampleConstraintCSV_Setback.csv');
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), \
'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', 'sGTE', units.unit1, 100), \
'conHeat_eas_max' : ('conHeat_eas', 'sLTE', units.unit1, 200)};
# Instantiate weather object
self.constraints = exodata.ConstraintFromCSV(csv_filepath, \
variable_map);
def setUp(self):
self.start_time = '1/1/2017';
self.final_time = '1/2/2017';
# Set .mo path
self.mopath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'Simple.mo');
# Gather inputs
start_time_exo = '1/1/2017';
final_time_exo = '1/10/2017';
control_csv_filepath = os.path.join(self.get_unittest_path(), 'resources', 'model', 'SimpleRC_Input.csv');
control_variable_map = {'q_flow_csv' : ('q_flow', units.W)};
self.controls = exodata.ControlFromCSV(control_csv_filepath, control_variable_map);
self.controls.collect_data(start_time_exo, final_time_exo);
# Set measurements
self.measurements = {};
self.measurements['T_db'] = {'Sample' : variables.Static('T_db_sample', 1800, units.s)};
self.measurements['q_flow'] = {'Sample' : variables.Static('q_flow_sample', 1800, units.s)};
# Gather constraints
constraint_csv_filepath = os.path.join(self.get_unittest_path(), 'resources', 'optimization', 'SimpleRC_Constraints.csv');
constraint_variable_map = {'q_flow_min' : ('q_flow', 'GTE', units.W), \
'T_db_min' : ('T_db', 'GTE', units.K), \
'T_db_max' : ('T_db', 'LTE', units.K)};
self.constraints = exodata.ConstraintFromCSV(constraint_csv_filepath, constraint_variable_map);
self.constraints.collect_data(start_time_exo, final_time_exo);
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)
