def init_refmodel(self, use_fmu, use_const, const_path):
print("%%%---Initialising Reference Simulation---%%%")
# Define control profile for reference
self.control_ref = exodata.ControlFromCSV(self.control_file,
self.contr_varmap,
tz_name=self.weather.tz_name)
index = pd.date_range(self.sim_start,
self.sim_end,
freq=str(self.meas_sampl_ref) + 'S')
if use_const == 1:
t_set = load_namespace(
os.path.join(const_path, 'constraints_' + self.building)
).data['TAir']['Slack_GTE'].get_base_data() + 1.0
else:
t_set = pd.Series(20 + 273.15, index=index)
for i in index:
if i.hour >= 6 and i.hour <= 9:
t_set[i] = 20 + 273.15
if i.hour >= 17 and i.hour <= 23:
t_set[i] = 20 + 273.15
# Define control data
self.control_ref.data = {
"SetPoint":
variables.Timeseries('SetPoint',
t_set,
units.K,
tz_name=self.weather.tz_name)
}
if use_fmu == 0:
self.emuref = systems.EmulationFromFMU(
self.meas_vars_ref,
moinfo=self.moinfo_emu_ref,
weather_data=self.weather.data,
control_data=self.control_ref.data,
other_inputs=self.other_input.data,
tz_name=self.weather.tz_name)
else:
self.emuref = systems.EmulationFromFMU(
self.meas_vars_ref,
fmupath=self.fmupath_ref,
weather_data=self.weather.data,
control_data=self.control_ref.data,
other_inputs=self.other_input.data,
tz_name=self.weather.tz_name)
import pandas as pd
#from multiprocessing import Pool
from mpcpy import units
from mpcpy import variables
from mpcpy import models_mod as models
from Simulator_HP_mod2 import SimHandler
if __name__ == "__main__":
# Naming conventions for the simulation
community = 'ResidentialCommunityUK_rad_2elements'
sim_id = 'MinEne'
model_id = 'R2CW_HP'
bldg_list = load_namespace(
os.path.join('path_to_models',
'teaser_bldgs_residentialUK_10bldgs_fallback'))
folder = 'results'
bldg_index_start = 0
bldg_index_end = 10
# Overall options
date = '11/20/2017 '
start = date + '16:30:00'
end = date + '19:00:00'
meas_sampl = '300'
horizon = 2 * 3600 / float(
meas_sampl) #time horizon for optimization in multiples of the sample
mon = 'nov'
DRstart = datetime.datetime.strptime(
from mpcpy import variables
from mpcpy import models_mod as models
from Simulator_HP_mod3 import SimHandler
from pulp_funcs import *
# In[116]:
community = 'ResidentialCommunityUK_rad_2elements'
sim_id = 'MinEne'
model_id = 'R2CW_HP'
arx_model = 'ARX_lag_4_exog4'
bldg_list = load_namespace(os.path.join('path_to_folder', 'teaser_bldgs_residential'))
folder = 'path_to_folder'
bldg_index_start = 0
bldg_index_end = 30
# Overall options
date = '11/20/2017 '
start = date + '16:30:00'
end = date + '19:00:00'
meas_sampl = '300'
horizon = 3.0*3600.0/float(meas_sampl) #time horizon for optimization in multiples of the sample
mon = 'nov'
DRstart = datetime.datetime.strptime(date + '18:00:00', '%m/%d/%Y %H:%M:%S') # hour to start DR - ramp down 30 mins before
DRend = datetime.datetime.strptime(date + '18:30:00', '%m/%d/%Y %H:%M:%S') # hour to end DR - ramp 30 mins later
DR_call_start = datetime.datetime.strptime(date + '17:00:00', '%m/%d/%Y %H:%M:%S') # Round of loop to implement the call
ref_profile = {}
controlseq = {}
opt_control = {}
emutemps = {}
mpctemps = {}
opt_stats = {}
flex_down = {}
flex_up = {}
power = {}
i = 0
for bldg in bldg_list:
building = bldg+'_'+model_id
flex_cost[building] = load_namespace(os.path.join(simu_path, folder, 'flex_cost_AggrMPC')).data['flex_cost'].get_base_data()
flex_cost[building] = flex_cost[building].resample(str(step)+'S').mean().shift(-1, freq=str(step) + 'S')
ref_profile[building] = load_namespace(os.path.join("'file_path_to_folder'", folder,'ref_profile_AggrMPC')).data['ref_profile'].get_base_data()*tot_cap
i += 1
for sim_id in sim_ids:
opt_stats[sim_id] = {}
controlseq[sim_id] = {}
mpctemps[sim_id] = {}
emutemps[sim_id] = {}
power[sim_id] = {}
for time_idx in sim_range:
# The analyser
import pandas as pd
import matplotlib.pyplot as plt
import dill
import os
import numpy as np
from funcs import store_namespace
from funcs import load_namespace
import datetime
community = 'ResidentialCommunity'
sim_id = 'MinEne'
model_id = 'R2CW_HP'
bldg_list = load_namespace(
os.path.join('path_to_models',
'teaser_bldgs_residentialUK_10bldgs_fallback'))
print(bldg_list)
folder = '10bldgs_centr_dyn_fin_jan'
mon = 'jan'
nodynprice = 0
step = 300
constr_folder = 'decentr_enemin_' + mon
if mon == 'jan':
start = '1/7/2017 16:30:00'
end = '1/7/2017 19:00:00'
controlseq_time = '01/07/2017 16:55:00'
elif mon == 'mar':
start = '3/1/2017 16:30:00'
end = '3/1/2017 19:00:00'
def update_DRinfo(self, start, end, **kwargs):
index = pd.date_range(start, end, freq=str(self.meas_sampl) + 'S')
# Random control signal
flex_signal = pd.Series(0, index=index)
#price_signal = pd.Series(np.random.rand(len(index))*5,index=index)
if 'use_ref_file' in kwargs:
ref_signal = load_namespace(
kwargs['profile_file']).display_data()['ref_profile']
else:
ref_signal = pd.Series(10000, index=index)
#print(type(ref_signal))
#print(ref_signal)
flex_signal = pd.Series(0, index=index)
if kwargs['DRevent_check'] == 1:
print('%%%%%%%%% DR event triggered %%%%%%%%%%')
for i in index:
if i.hour >= kwargs['DRstart'] and i.hour < kwargs['DRend']:
flex_signal.loc[i] = kwargs['flex_cost']
flex_signal.sort_index()
'''
for i in index:
if i.hour >= 7 and i.hour <= 10:
price_signal[i] = np.random.uniform(0.8,1)*40
if i.hour >= 17 and i.hour <= 23:
price_signal[i] = np.random.uniform(0.8,1)*40
'''
# Create DR event or not
if 'DRevent_check' == 1 and use_ref_file == 0:
print('%%%%%%%%% DR event triggered %%%%%%%%%%')
#index = ref_signal.index.tz_convert(None)
k = pd.Series(1, index=index)
#flex_signal = pd.Series(0,index=index)
#ref_signal.index = index
if kwargs['DRevent_check'] == 1:
for i in index:
if i.hour >= kwargs['DRstart'] and i.hour <= kwargs[
'DRend']:
k.loc[i] = 0.8
print(k)
print('Reference profile before modification:')
print(ref_signal)
# Sort the indices
ref_signal.sort_index()
k.sort_index()
#flex_signal.sort_index()
# Define the reference signal
ref_signal = ref_signal * k * 30
print('Reference profile after modification:')
print(ref_signal)
print('Flex cost:')
print(flex_signal)
#print(price_signal)
# Define control data
'''
self.price.data = {"pi_e": variables.Timeseries('pi_e', price_signal,units.cents_kWh,tz_name=self.weather.tz_name)
}
'''
self.flex_cost.data = {
"flex_cost":
variables.Timeseries('flex_cost',
flex_signal,
units.cents_kWh,
tz_name=self.weather.tz_name)
}
self.ref_profile.data = {
"ref_profile":
variables.Timeseries('ref_profile',
ref_signal,
units.W,
tz_name=self.weather.tz_name)
}
#pheat_max = pd.Series(10000,index=index)
#self.control.collect_data(self.sim_start, self.sim_end)
#print(self.price.display_data())
#print(self.flex_cost.display_data())
#print(self.ref_profile.display_data())
#store_namespace('price_upd_'+self.building,self.price)
store_namespace('flex_cost_upd_' + self.building, self.flex_cost)
store_namespace('ref_profile_upd_' + self.building, self.ref_profile)
def do_plot(self):
print(
'%%%%%%%%%----Plot Optimized Control Signal and resulting temperature----%%%%%%'
)
'''
print("----Optimization temps based on MPC model----")
b = opt_problem.Model.display_measurements('Simulated')['TAir']
print(b)
print("----Emulated temps----")
c = opt_problem.Model.display_measurements('Measured')['TAir']
a1_plot = opt_controlseq['ConvGain2'].display_data().plot(figsize=(11.69,8.27))
#a2_plot = opt_controlseq['Qflow2'].display_data().plot(figsize=(11.69,8.27))
a1_fig = a1_plot.get_figure()
a1_fig.savefig("plot_convgain.pdf")
a1_fig.clf()
'''
index = pd.date_range(self.opt_start,
self.opt_end,
freq=str(self.meas_sampl) + 'S')
sim = load_namespace('sim_val')[self.val_start:self.val_end]
meas = load_namespace('meas_val')[self.val_start:self.val_end]
opt_control_energy = load_namespace('opt_control_minenergy')
opt_control_cost = load_namespace('opt_control_mincost')
opt_control_DR = load_namespace('opt_control_DRcost')
ref_heatinput = load_namespace('ref_heatinput')
ref_temp = load_namespace('ref_temp')
opt_temp = load_namespace('optemu')
#print(opt_control['ConvGain2'].display_data())
#print(ref_heatinput)
opt_control_series_energy = opt_control_energy[
'ConvGain2'].display_data()
opt_control_series_cost = opt_control_cost['ConvGain2'].display_data()
opt_control_series_DR = opt_control_DR['ConvGain2'].display_data()
#print(opt_temp)
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Validation figure
plt.figure(figsize=(11.69, 8.27))
plot_real = plt.plot(meas.index, meas['TAir'], '-x', label="Real")
plot_mpc = plt.plot(sim.index, sim['TAir'], '--o', label="MPC model")
plt.legend(fontsize=14)
plt.xlabel("Time", fontsize=18)
plt.ylabel("Temperature [C]", fontsize=18)
plt.title("Validation of MPC model", fontsize=22)
plt.xticks(rotation=45)
# We change the fontsize of minor ticks label
plt.tick_params(axis='both', which='major', labelsize=12)
plt.tick_params(axis='both', which='minor', labelsize=12)
plt.savefig("validation")
plt.clf()
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Optimization figure
plt.figure(figsize=(11.69, 8.27))
plt.plot(opt_control_series_energy.index,
opt_control_series_energy.values,
'-o',
label="MPC-EneMin")
#plt.plot(opt_control_series_cost.index, opt_control_series_cost.values,'-+', label="MPC-CostMin")
#plt.plot(opt_control_series_DR.index, opt_control_series_DR.values,'--o', label="MPC-DR")
plt.plot(ref_heatinput.index,
ref_heatinput.values,
'--x',
label="PI-ref")
plt.legend(fontsize=14)
plt.xlabel("Time", fontsize=18)
plt.ylabel("Heat Input [W]", fontsize=18)
plt.title("Optimized Control Sequences vs. PI controller", fontsize=22)
plt.xticks(rotation=35)
# We change the fontsize of minor ticks label
plt.tick_params(axis='both', which='major', labelsize=12)
plt.tick_params(axis='both', which='minor', labelsize=12)
plt.savefig("optimized_heatinput.pdf")
plt.clf()
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Optimization temps
plt.figure(figsize=(11.69, 8.27))
plot_optimized = plt.plot(opt_temp.index,
opt_temp.values,
'-o',
label="MPC")
plot_ref = plt.plot(ref_temp.index,
ref_temp.values,
'-x',
label="PI-ref")
plt.legend(fontsize=14)
plt.xlabel("Time", fontsize=18)
plt.ylabel("Temperature [C]", fontsize=18)
plt.title("Inside Temperatures with different controls", fontsize=22)
plt.xticks(rotation=35)
# We change the fontsize of minor ticks label
plt.tick_params(axis='both', which='major', labelsize=12)
plt.tick_params(axis='both', which='minor', labelsize=12)
plt.savefig("optimized_temps")
plt.clf()
#print(ref_heatinput)
print('%%%% --- Optimised heat consumption (Wh) --- %%%%')
print(
sum(opt_control_energy['ConvGain2'].get_base_data().resample(
'3600S').mean()))
print('%%%% --- Reference (PI) heat consumption (Wh) --- %%%%')
print(sum(ref_heatinput.resample('3600S').mean()))
import datetime
import time
import pandas as pd
#rom multiprocessing import Pool
import numpy as np
import gc
from Simulator import SimHandler
from funcs import emulate_jmod
from mpcpy import units
from mpcpy import variables
# Naming conventions for the simulation
community = 'ResidentialCommunityUK'
sim_id = 'MinCost_Centr_Res'
bldg_list = load_namespace(os.path.join('path to models'))
#print(bldg_list)
# Overall options
start = '1/7/2017 13:00:00'
end = '1/7/2017 19:00:00'
meas_sampl = '1800'
horizon = 10 #time horizon for optimization, multiple of the measurement sample
use_previous = 0
emus = 10
sim_range = pd.date_range(start, end, freq = meas_sampl+'S')
opt_start_str = start
opt_end = datetime.datetime.strptime(end, '%m/%d/%Y %H:%M:%S') + datetime.timedelta(seconds = horizon*int(meas_sampl))
opt_end_str = opt_end.strftime('%m/%d/%Y %H:%M:%S')
# Instantiate Simulator for aggregated optimisation
import matplotlib.pyplot as plt
import dill
import os
import numpy as np
from funcs import store_namespace
from funcs import load_namespace
import datetime
from matplotlib.font_manager import FontProperties
from matplotlib import rc
community = 'ResidentialCommunity'
sim_ids = ['MinEne_0-2']
model_id = 'R2CW_HP'
bldg_list = load_namespace(
os.path.join('path to models', 'teaser_bldgs_residential'))
#
bldg_list = [bldg_list[0], bldg_list[1]]
print(bldg_list)
folder = 'results'
step = 300
nodynprice = 0
mon = 'jan'
constr_folder = 'decentr_enemin_constr_' + mon
#bldg_list = bldg_list[0:1]
if mon == 'jan':
start = '1/7/2017 16:30:00'
end = '1/7/2017 19:00:00'
controlseq_time = '01/07/2017 16:55:00'
elif mon == 'mar':
start = '3/1/2017 16:30:00'
import os
import datetime
import time
import pandas as pd
#from multiprocessing import Pool
from mpcpy import units
from Simulator import SimHandler
if __name__ == "__main__":
# Naming conventions for the simulation
community = 'ResidentialCommunityUK'
sim_id = 'MinCost'
model_id = 'R2CW'
bldg_list = load_namespace(os.path.join('path to models'))
# Overall options
start = '1/7/2017 13:00:00'
end = '1/7/2017 19:00:00'
meas_sampl = '1800'
horizon = 10 #time horizon for optimization in hours
sim_range = pd.date_range(start, end, freq = meas_sampl+'S')
opt_start_str = start
opt_end = datetime.datetime.strptime(end, '%m/%d/%Y %H:%M:%S') + datetime.timedelta(seconds = horizon*int(meas_sampl))
opt_end_str = opt_end.strftime('%m/%d/%Y %H:%M:%S')
bldg_index_start = 0
bldg_index_end = 10
# Instantiate Simulator
from scipy.linalg import expm
from numpy.linalg import inv
from sklearn.metrics import mean_squared_error, r2_score
from statsmodels.graphics.tsaplots import plot_pacf
from statsmodels.graphics.tsaplots import plot_acf
from Simulator_HP_mod3 import SimHandler
if __name__ == "__main__":
# Naming conventions for the simulation
community = 'ResidentialCommunityUK_rad_2elements'
sim_id = 'MinEne'
model_id = 'R2CW_HP'
bldg_list = load_namespace(os.path.join('file_path_to_folder', 'teaser_bldgs_residential'))
bldg_index_start = 0
bldg_index_end = 30
emulate = 0 # Emulate or not?
old_sim_param = 1 # Choose initial guesses
# Overall options
date = '1/1/2017 '
start = '1/1/2017 00:00:00'
end = '1/9/2017 00:00:00'
train_start = start
valid_start = '1/6/2017 00:00:00'
train_end = valid_start
import matplotlib.pyplot as plt
import dill
import os
import numpy as np
from funcs import store_namespace
from funcs import load_namespace
import datetime
from matplotlib.font_manager import FontProperties
from matplotlib import rc
community = 'ResidentialCommunity'
sim_ids = ['MinEne_0-30']
model_id = 'R2CW_HP'
bldg_list = load_namespace(
os.path.join('file_path_to_folder', 'teaser_bldgs_residential'))
compr_capacity_list = [float(4500.0)] * 10 + [float(3000.0)] * 20
print(bldg_list)
folder = 'results'
step = 300
nodynprice = 0
mon = 'nov'
constr_folder = 'decentr_enemin_constr_' + mon
if mon == 'jan':
start = '1/7/2017 16:30:00'
end = '1/7/2017 19:00:00'
controlseq_time = '01/07/2017 16:55:00'
elif mon == 'mar':
start = '3/1/2017 16:30:00'
end = '3/1/2017 19:00:00'
controlseq_time = '03/01/2017 16:55:00'
import time
import pandas as pd
#from multiprocessing import Pool
from mpcpy import units
from mpcpy import variables
from mpcpy import models_mod as models
from Simulator_HP_mod2 import SimHandler
if __name__ == "__main__":
# Naming conventions for the simulation
community = 'ResidentialCommunityUK_rad_2elements'
sim_id = 'MinEne'
model_id = 'R2CW_HP'
bldg_list = load_namespace(os.path.join('path_to_models', 'teaser_bldgs_residentialUK_10bldgs_fallback'))
folder = 'results'
bldg_index_start = 0
bldg_index_end = 10
mon = 'mar'
# Overall options
start = '3/1/2017 16:30:00'
end = '3/1/2017 19:00:00'
meas_sampl = '300'
horizon = 2*3600/float(meas_sampl) #time horizon for optimization in multiples of the sample
DRstart = datetime.datetime.strptime('1/7/2017 19:30:00', '%m/%d/%Y %H:%M:%S') # hour to start DR - ramp down 30 mins before
DRend = datetime.datetime.strptime('1/7/2017 19:30:00', '%m/%d/%Y %H:%M:%S') # hour to end DR - ramp 30 mins later
DR_call_start = datetime.datetime.strptime('1/7/2017 19:30:00', '%m/%d/%Y %H:%M:%S') # Round of loop to implement the call
DR_ramp_start = datetime.datetime.strptime('1/7/2017 19:30:00', '%m/%d/%Y %H:%M:%S')
def main():
community = 'ResidentialCommunityUK_rad_2elements'
sim_id = 'RBC'
model_id = 'R2CW_HP'
bldg_list = load_namespace(
os.path.join('path_to_models',
'teaser_bldgs_residentialUK_10bldgs_fallback'))
folder = 'results'
bldg_index_start = 0
bldg_index_end = 10
mon = 'mar'
print(bldg_list)
# Overall options
start = '3/1/2017 12:00:00'
end = '3/1/2017 19:10:00'
meas_sampl = '300' # Daylight saving time hours 26/3 and 29/10
index = pd.date_range(start, end, freq=meas_sampl + 'S')
SimJmod_list = []
SimDym_list = []
i = 0
index = pd.date_range(start, end, freq=meas_sampl + 'S')
for bldg in bldg_list[bldg_index_start:bldg_index_end]:
print('In loop: ' + str(i))
i = i + 1
# Then JModelica model
Sim = SimHandler(sim_start=start, sim_end=end, meas_sampl=meas_sampl)
Sim.moinfo_mpc = (os.path.join(Sim.simu_path, 'Tutorial_R2CW.mo'),
'Tutorial_R2CW.R2CW', {})
Sim.building = bldg + '_' + model_id
Sim.fmupath_mpc = os.path.join(
Sim.simu_path, 'fmus', community,
'Tutorial_' + model_id + '_' + model_id + '.fmu')
Sim.fmupath_emu = os.path.join(
Sim.simu_path, 'fmus', community, community + '_' + bldg + '_' +
bldg + '_Models_' + bldg + '_House_mpc.fmu')
Sim.fmupath_ref = os.path.join(
Sim.simu_path, 'fmus', community, community + '_' + bldg + '_' +
bldg + '_Models_' + bldg + '_House_PI.fmu')
Sim.moinfo_emu = (os.path.join(Sim.mod_path, community, bldg,
bldg + '_Models',
bldg + '_House_mpc.mo'),
community + '.' + bldg + '.' + bldg + '_Models.' +
bldg + '_House_mpc', {})
Sim.moinfo_emu_ref = (os.path.join(Sim.mod_path, community, bldg,
bldg + '_Models',
bldg + '_House_PI.mo'),
community + '.' + bldg + '.' + bldg +
'_Models.' + bldg + '_House_PI', {})
if i == 1:
Sim.update_weather(start, end)
else:
Sim.weather = SimJmod_list[i - 2].weather
Sim.get_other_input(start, end)
Sim.get_constraints(start, end)
Sim.param_file = os.path.join(Sim.simu_path, 'csvs',
'Parameters_R2CW.csv')
Sim.get_params()
Sim.parameters.data = load_namespace(
os.path.join(Sim.simu_path, 'sysid',
'sysid_HPrad_2element_' + mon + '_600S',
'est_params_' + bldg + '_' + model_id))
Sim.other_input = load_namespace(
os.path.join(Sim.simu_path, 'ibpsa_paper', 'decentr_enemin_' + mon,
'other_input_' + bldg + '_' + model_id))
Sim.constraints = load_namespace(
os.path.join(Sim.simu_path, 'ibpsa_paper', 'decentr_enemin_' + mon,
'constraints_' + bldg + '_' + model_id))
store_namespace(os.path.join(folder, 'params_' + Sim.building),
Sim.parameters)
store_namespace(os.path.join(folder, 'constraints_' + Sim.building),
Sim.constraints)
store_namespace(os.path.join(folder, 'other_input_' + Sim.building),
Sim.other_input)
# Add to list of simulations
SimJmod_list.append(Sim)
for Sim in SimJmod_list:
Sim.init_refmodel(use_fmu=1,
use_const=1,
const_path=os.path.join(Sim.simu_path, 'ibpsa_paper',
'decentr_enemin_' + mon))
store_namespace(os.path.join(folder, 'control_ref_' + Sim.building),
Sim.control_ref)
for Sim in SimJmod_list:
Sim.run_reference(start, end)
