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data_simulator_monthly.py
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import numpy as np
import pandas as pd
from eppy import modeleditor
from eppy.modeleditor import IDF
import subprocess
import csv
def LHSample( D,bounds,N):
''' :param D:参数个数 :param bounds:参数对应范围（list） :param N:拉丁超立方层数 :return:样本数据 '''
result = np.empty([N, D])
temp = np.empty([N])
d = 1.0 / N
for i in range(D):
for j in range(N):
temp[j] = np.random.uniform(
low=j * d, high=(j + 1) * d, size = 1)[0]
np.random.shuffle(temp)
for j in range(N):
result[j, i] = temp[j]
#对样本数据进行拉伸
b = np.array(bounds)
lower_bounds = b[:,0]
upper_bounds = b[:,1]
if np.any(lower_bounds > upper_bounds):
print('范围出错')
return None
np.add(np.multiply(result,
(upper_bounds - lower_bounds),
out=result),
lower_bounds,
out=result)
return result
def comp_data_reader(eso_file, yc_keys, xc_keys):
with open(eso_file) as csvfile:
readCSV = csv.reader(csvfile, delimiter=',')
Eplusout = []
for row in readCSV:
Eplusout.append(row)
yc_mtr_number=[]
for i in range(len(yc_keys)):
for row in Eplusout:
if len(row)>3:
if yc_keys[i] in row[2]:
yc_mtr_number.append(row[0])
yc_mtr_values=[]
for i in range(len(yc_mtr_number)):
yc_mtr_value=[]
for row in Eplusout:
if len(row)>3:
if yc_mtr_number[i] == row[0]:
if yc_keys[i] not in row[2]:
yc_mtr_value.append(float(row[1])/3600000)
yc_mtr_values.append(yc_mtr_value)
xc_mtr_number=[]
for i in range(len(xc_keys)):
for row in Eplusout:
if len(row)>3:
if xc_keys[i] in row[3]:
xc_mtr_number.append(row[0])
xc_mtr_values=[]
for i in range(len(xc_mtr_number)):
xc_mtr_value=[]
for row in Eplusout:
if len(row)>3:
if xc_mtr_number[i] == row[0]:
if xc_keys[i] not in row[3]:
xc_mtr_value.append(float(row[1]))
xc_mtr_values.append(xc_mtr_value)
return [yc_mtr_values,xc_mtr_values]
def datafield(yc_keys,xc_keys):
iddfile = "./Energy+9.1.idd"
IDF.setiddname(iddfile)
idfname = "./RefBldgLargeOfficeNew2004_Chicago.idf"
idf = IDF(idfname)
# change the output variable and meter
# output_frequency='Daily'
output_frequency='Monthly'
variable=[]
for i in range(len(xc_keys)):
variable1 = idf.newidfobject("Output:Variable".upper())
variable1.Key_Value = '*'
variable1.Variable_Name = xc_keys[i]
variable1.Reporting_Frequency = output_frequency
variable.append(variable1)
idf.idfobjects['Output:Variable'.upper()]=variable
meter=[]
for i in range(len(yc_keys)):
meter1 = idf.newidfobject("Output:Meter".upper())
meter1.Key_Name = yc_keys[i]
meter1.Reporting_Frequency = output_frequency
meter.append(meter1)
idf.idfobjects['Output:Meter'.upper()]=meter
idf.idfobjects['RUNPERIOD'][0].Begin_Month=1
idf.idfobjects['RUNPERIOD'][0].Begin_Day_of_Month=1
idf.idfobjects['RUNPERIOD'][0].End_Month=12
idf.idfobjects['RUNPERIOD'][0].End_Day_of_Month=31
idf.saveas('C:/Users/songc/Desktop/work file/Updated_Model.idf')
idfname1 = 'C:/Users/songc/Desktop/work file/Updated_Model.idf' # This IDF file is updated at each iteration.
epwfile = './SPtMasterTable_52384_2011_amy.epw'
subprocess.call(['C:/EnergyPlusV9-1-0/energyplus.exe', '-d', "C:/Users/songc/Desktop/work file/result_folder", '-w', epwfile, idfname1])
eso_file='./result_folder/eplusout.eso'
[ycoutput,xcoutput]=comp_data_reader(eso_file, yc_keys, xc_keys)
yc_df1 = pd.DataFrame(ycoutput, index=yc_keys).T
xc_df1 = pd.DataFrame(xcoutput, index=xc_keys).T
epwfile2 = './SPtMasterTable_52384_2012_amy.epw'
subprocess.call(['C:/EnergyPlusV9-1-0/energyplus.exe', '-d', "C:/Users/songc/Desktop/work file/result_folder", '-w', epwfile2, idfname1])
eso_file='./result_folder/eplusout.eso'
[ycoutput,xcoutput]=comp_data_reader(eso_file, yc_keys, xc_keys)
yc_df2 = pd.DataFrame(ycoutput, index=yc_keys).T
xc_df2 = pd.DataFrame(xcoutput, index=xc_keys).T
yc_df = pd.concat([yc_df1,yc_df2],axis=0)
xc_df = pd.concat([xc_df1,xc_df2],axis=0)
df = pd.concat([yc_df,xc_df],axis=1)
df.to_csv('DATAFIELD_Multi.csv',index=False)
df_single = pd.concat([yc_df.iloc[:,0],xc_df],axis=1)
df_single.to_csv('DATAFIELD_Single.csv',index=False)
def datacomp(yc_keys,xc_keys,tc_keys):
iddfile = "./Energy+9.1.idd"
IDF.setiddname(iddfile)
yc_df = pd.DataFrame(columns=yc_keys)
xc_df = pd.DataFrame(columns=xc_keys)
tc_df = pd.DataFrame(columns=tc_keys)
for n in range(len(LHS_result)):
idfname = "./RefBldgLargeOfficeNew2004_Chicago.idf"
idf = IDF(idfname)
# change the output variable and meter
# output_frequency='Daily'
output_frequency='Monthly'
variable=[]
for i in range(len(xc_keys)):
variable1 = idf.newidfobject("Output:Variable".upper())
variable1.Key_Value = '*'
variable1.Variable_Name = xc_keys[i]
variable1.Reporting_Frequency = output_frequency
variable.append(variable1)
idf.idfobjects['Output:Variable'.upper()]=variable
meter=[]
for i in range(len(yc_keys)):
meter1 = idf.newidfobject("Output:Meter".upper())
meter1.Key_Name = yc_keys[i]
meter1.Reporting_Frequency = output_frequency
meter.append(meter1)
idf.idfobjects['Output:Meter'.upper()]=meter
# change the runperiod and other idf objects
for i in range(len(idf.idfobjects['LIGHTS'])):
idf.idfobjects['LIGHTS'][i].Watts_per_Zone_Floor_Area=LHS_result[n][0]
for i in range(len(idf.idfobjects['ELECTRICEQUIPMENT'])):
idf.idfobjects['ELECTRICEQUIPMENT'][i].Watts_per_Zone_Floor_Area=LHS_result[n][1]
for i in range(len(idf.idfobjects['FAN:VARIABLEVOLUME'])):
# idf.idfobjects['FAN:VARIABLEVOLUME'][i].Pressure_Rise=LHS_result[n][2]
idf.idfobjects['FAN:VARIABLEVOLUME'][i].Fan_Total_Efficiency=LHS_result[n][2]
for i in range(len(idf.idfobjects['ZONEINFILTRATION:DESIGNFLOWRATE'])):
idf.idfobjects['ZONEINFILTRATION:DESIGNFLOWRATE'][i].Flow_per_Exterior_Surface_Area=LHS_result[n][3]
for i in range(len(idf.idfobjects['CHILLER:ELECTRIC:REFORMULATEDEIR'])):
idf.idfobjects['CHILLER:ELECTRIC:REFORMULATEDEIR'][i].Reference_COP=LHS_result[n][4]
for i in range(len(idf.idfobjects['BOILER:HOTWATER'])):
idf.idfobjects['BOILER:HOTWATER'][i].Nominal_Thermal_Efficiency=LHS_result[n][5]
# for i in range(len(idf.idfobjects['SCHEDULE:COMPACT'])):
# if 'Building_Cooling_Sp_Schedule' in idf.idfobjects['SCHEDULE:COMPACT'][i].Name:
# idf.idfobjects['SCHEDULE:COMPACT'][i].Field_4=LHS_result[n][4]
idf.idfobjects['RUNPERIOD'][0].Begin_Month=1
idf.idfobjects['RUNPERIOD'][0].Begin_Day_of_Month=1
idf.idfobjects['RUNPERIOD'][0].End_Month=12
idf.idfobjects['RUNPERIOD'][0].End_Day_of_Month=31
idf.saveas('C:/Users/songc/Desktop/work file/Updated_Model.idf')
idfname1 = 'C:/Users/songc/Desktop/work file/Updated_Model.idf' # This IDF file is updated at each iteration.
epwfile = './SPtMasterTable_52384_2011_amy.epw'
subprocess.call(['C:/EnergyPlusV9-1-0/energyplus.exe', '-d', "C:/Users/songc/Desktop/work file/result_folder", '-w', epwfile, idfname1])
eso_file='./result_folder/eplusout.eso'
[ycoutput,xcoutput]=comp_data_reader(eso_file, yc_keys, xc_keys)
yc_df = yc_df.append(pd.DataFrame(ycoutput, index=yc_keys).T)
xc_df = xc_df.append(pd.DataFrame(xcoutput, index=xc_keys).T)
tc_df = tc_df.append(pd.DataFrame(np.reshape(list(LHS_result[n])*len(ycoutput[0]),(len(ycoutput[0]),len(tc_keys))),columns=tc_keys))
df = pd.concat([yc_df,xc_df,tc_df],axis=1)
df.to_csv('DATACOMP_Multi.csv',index=False)
df_single = pd.concat([yc_df.iloc[:,0],xc_df,tc_df],axis=1)
df_single.to_csv('DATACOMP_Single.csv',index=False)
bounds=[[10.76*0.8,10.76*1.2],[10.76*0.8,10.76*1.2],[0.605*0.8,0.605*1.2],[0.000302*0.8,0.000302*1.2],[5.5*0.8,5.5*1.2],[0.78*0.8,0.78*1.2]]
LHS_result=LHSample(6,bounds,30)
# bounds=[[10.76*0.8,10.76*1.2],[10.76*0.8,10.76*1.2],[0.605*0.8,0.605*1.2],[0.000302*0.8,0.000302*1.2]]
# LHS_result=LHSample(4,bounds,30)
# bounds=[[10.76*0.8,10.76*1.2],[10.76*0.8,10.76*1.2],[0.605*0.8,0.605*1.2]]
# LHS_result=LHSample(3,bounds,30)
# bounds=[[10.76*0.8,10.76*1.2],[10.76*0.8,10.76*1.2]]
# LHS_result=LHSample(2,bounds,30)
# yc_keys=['Electricity:Facility','InteriorLights:Electricity','Fans:Electricity','InteriorEquipment:Electricity']
yc_keys=['Electricity:Facility','InteriorLights:Electricity','InteriorEquipment:Electricity','Electricity:HVAC','Heating:Gas']
# yc_keys=['Electricity:Facility','InteriorEquipment:Electricity']
# yc_keys=['Electricity:Facility','Cooling:Electricity']
xc_keys=['Site Outdoor Air Drybulb Temperature','Site Outdoor Air Relative Humidity','Site Direct Solar Radiation Rate per Area']
tc_keys=['tc1','tc2','tc3','tc4','tc5','tc6']
datafield(yc_keys, xc_keys)
datacomp(yc_keys, xc_keys, tc_keys)