How to use uwg - 10 common examples
To help you get started, we’ve selected a few uwg examples, based on popular ways it is used in public projects.
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chriswmackey / Dragonfly / src / DF Run Urban Weather Generator.py View on Github 
def create_uwg(epw_file, end_folder, name):
start_folder, epw_name = os.path.split(epw_file)
epw_name = epw_name.replace('.epw', '')
if end_folder is None:
end_folder = start_folder + '\\URBAN\\'
if name is None:
name = epw_name + '_URBAN.epw'
if not os.path.isdir(end_folder):
os.mkdir(end_folder)
return uwg(epw_name, None, start_folder, None, end_folder, name), end_folder + '\\' + name# Read location summary (Sheet 3)
file_doe_name_location = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_LocationSummary.csv".format(i+1))
list_doe3 = read_csv(file_doe_name_location)
#(listof (listof 3 eras (listof 16 climate types)))
TypeWall = [list_doe3[3][4:20],list_doe3[14][4:20],list_doe3[25][4:20]] # Construction type
RvalWall = str2fl([list_doe3[4][4:20],list_doe3[15][4:20],list_doe3[26][4:20]]) # [m2*K/W] R-value
TypeRoof = [list_doe3[5][4:20],list_doe3[16][4:20],list_doe3[27][4:20]] # Construction type
RvalRoof = str2fl([list_doe3[6][4:20],list_doe3[17][4:20],list_doe3[28][4:20]]) # [m2*K/W] R-value
Uwindow = str2fl([list_doe3[7][4:20],list_doe3[18][4:20],list_doe3[29][4:20]]) # [W/m2*K] U-factor
SHGC = str2fl([list_doe3[8][4:20],list_doe3[19][4:20],list_doe3[30][4:20]]) # [-] coefficient
HVAC = str2fl([list_doe3[9][4:20],list_doe3[20][4:20],list_doe3[31][4:20]]) # [kW] Air Conditioning
HEAT = str2fl([list_doe3[10][4:20],list_doe3[21][4:20],list_doe3[32][4:20]]) # [kW] Heating
COP = str2fl([list_doe3[11][4:20],list_doe3[22][4:20],list_doe3[33][4:20]]) # [-] Air Conditioning COP
EffHeat = str2fl([list_doe3[12][4:20],list_doe3[23][4:20],list_doe3[34][4:20]]) # [%] Heating Efficiency
FanFlow = str2fl([list_doe3[13][4:20],list_doe3[24][4:20],list_doe3[35][4:20]]) # [m3/s] Fan Max Flow Rate
# Read Schedules (Sheet 4)
file_doe_name_schedules = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_Schedules.csv".format(i+1))
list_doe4 = read_csv(file_doe_name_schedules)
#listof(listof weekday, sat, sun (list of 24 fractions)))
SchEquip = str2fl([list_doe4[1][6:30],list_doe4[2][6:30],list_doe4[3][6:30]]) # Equipment Schedule 24 hrs
SchLight = str2fl([list_doe4[4][6:30],list_doe4[5][6:30],list_doe4[6][6:30]]) # Light Schedule 24 hrs; Wkday=Sat=Sun=Hol
SchOcc = str2fl([list_doe4[7][6:30],list_doe4[8][6:30],list_doe4[9][6:30]]) # Occupancy Schedule 24 hrs
SetCool = str2fl([list_doe4[10][6:30],list_doe4[11][6:30],list_doe4[12][6:30]]) # Cooling Setpoint Schedule 24 hrs
SetHeat = str2fl([list_doe4[13][6:30],list_doe4[14][6:30],list_doe4[15][6:30]]) # Heating Setpoint Schedule 24 hrs; summer design
SchGas = str2fl([list_doe4[16][6:30],list_doe4[17][6:30],list_doe4[18][6:30]]) # Gas Equipment Schedule 24 hrs; wkday=sat
SchSWH = str2fl([list_doe4[19][6:30],list_doe4[20][6:30],list_doe4[21][6:30]]) # Solar Water Heating Schedule 24 hrs; wkday=summerdesign, sat=winterdesgin
for j in range(3):#Purpose: Loop through every DOE reference csv and extract building data
#Nested loop = 16 types, 3 era, 16 zones = time complexity O(n*m*k) = 768
for i in range(16):
#i = 16 types of buildings
#print "\tType: {} @i={}".format(BLDTYPE[i], i)
# Read building summary (Sheet 1)
file_doe_name_bld = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_BuildingSummary.csv".format(i+1))
list_doe1 = read_csv(file_doe_name_bld)
#listof(listof 3 era values)
nFloor = str2fl(list_doe1[3][3:6]) # Number of Floors, this will be list of floats and str if "basement"
glazing = str2fl(list_doe1[4][3:6]) # [?] Total
hCeiling = str2fl(list_doe1[5][3:6]) # [m] Ceiling height
ver2hor = str2fl(list_doe1[7][3:6]) # Wall to Skin Ratio
AreaRoof = str2fl(list_doe1[8][3:6]) # [m2] Gross Dimensions - Total area
# Read zone summary (Sheet 2)
file_doe_name_zone = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_ZoneSummary.csv".format(i+1))
list_doe2 = read_csv(file_doe_name_zone)
#listof(listof 3 eras)
AreaFloor = str2fl([list_doe2[2][5],list_doe2[3][5],list_doe2[4][5]]) # [m2]
Volume = str2fl([list_doe2[2][6],list_doe2[3][6],list_doe2[4][6]]) # [m3]
AreaWall = str2fl([list_doe2[2][8],list_doe2[3][8],list_doe2[4][8]]) # [m2]
AreaWindow = str2fl([list_doe2[2][9],list_doe2[3][9],list_doe2[4][9]]) # [m2]
Occupant = str2fl([list_doe2[2][11],list_doe2[3][11],list_doe2[4][11]]) # Number of People
Light = str2fl([list_doe2[2][12],list_doe2[3][12],list_doe2[4][12]]) # [W/m2]
Elec = str2fl([list_doe2[2][13],list_doe2[3][13],list_doe2[4][13]]) # [W/m2] Electric Plug and Process
Gas = str2fl([list_doe2[2][14],list_doe2[3][14],list_doe2[4][14]]) # [W/m2] Gas Plug and Process
SHW = str2fl([list_doe2[2][15],list_doe2[3][15],list_doe2[4][15]]) # [Litres/hr] Peak Service Hot Water
Vent = str2fl([list_doe2[2][17],list_doe2[3][17],list_doe2[4][17]]) # [L/s/m2] Ventilation
Infil = str2fl([list_doe2[2][20],list_doe2[3][20],list_doe2[4][20]]) # Air Changes Per Hour (ACH) InfiltrationInfil = str2fl([list_doe2[2][20],list_doe2[3][20],list_doe2[4][20]]) # Air Changes Per Hour (ACH) Infiltration
# Read location summary (Sheet 3)
file_doe_name_location = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_LocationSummary.csv".format(i+1))
list_doe3 = read_csv(file_doe_name_location)
#(listof (listof 3 eras (listof 16 climate types)))
TypeWall = [list_doe3[3][4:20],list_doe3[14][4:20],list_doe3[25][4:20]] # Construction type
RvalWall = str2fl([list_doe3[4][4:20],list_doe3[15][4:20],list_doe3[26][4:20]]) # [m2*K/W] R-value
TypeRoof = [list_doe3[5][4:20],list_doe3[16][4:20],list_doe3[27][4:20]] # Construction type
RvalRoof = str2fl([list_doe3[6][4:20],list_doe3[17][4:20],list_doe3[28][4:20]]) # [m2*K/W] R-value
Uwindow = str2fl([list_doe3[7][4:20],list_doe3[18][4:20],list_doe3[29][4:20]]) # [W/m2*K] U-factor
SHGC = str2fl([list_doe3[8][4:20],list_doe3[19][4:20],list_doe3[30][4:20]]) # [-] coefficient
HVAC = str2fl([list_doe3[9][4:20],list_doe3[20][4:20],list_doe3[31][4:20]]) # [kW] Air Conditioning
HEAT = str2fl([list_doe3[10][4:20],list_doe3[21][4:20],list_doe3[32][4:20]]) # [kW] Heating
COP = str2fl([list_doe3[11][4:20],list_doe3[22][4:20],list_doe3[33][4:20]]) # [-] Air Conditioning COP
EffHeat = str2fl([list_doe3[12][4:20],list_doe3[23][4:20],list_doe3[34][4:20]]) # [%] Heating Efficiency
FanFlow = str2fl([list_doe3[13][4:20],list_doe3[24][4:20],list_doe3[35][4:20]]) # [m3/s] Fan Max Flow Rate
# Read Schedules (Sheet 4)
file_doe_name_schedules = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_Schedules.csv".format(i+1))
list_doe4 = read_csv(file_doe_name_schedules)
#listof(listof weekday, sat, sun (list of 24 fractions)))
SchEquip = str2fl([list_doe4[1][6:30],list_doe4[2][6:30],list_doe4[3][6:30]]) # Equipment Schedule 24 hrs
SchLight = str2fl([list_doe4[4][6:30],list_doe4[5][6:30],list_doe4[6][6:30]]) # Light Schedule 24 hrs; Wkday=Sat=Sun=Hol
SchOcc = str2fl([list_doe4[7][6:30],list_doe4[8][6:30],list_doe4[9][6:30]]) # Occupancy Schedule 24 hrs
SetCool = str2fl([list_doe4[10][6:30],list_doe4[11][6:30],list_doe4[12][6:30]]) # Cooling Setpoint Schedule 24 hrs
SetHeat = str2fl([list_doe4[13][6:30],list_doe4[14][6:30],list_doe4[15][6:30]]) # Heating Setpoint Schedule 24 hrs; summer design
SchGas = str2fl([list_doe4[16][6:30],list_doe4[17][6:30],list_doe4[18][6:30]]) # Gas Equipment Schedule 24 hrs; wkday=sat
SchSWH = str2fl([list_doe4[19][6:30],list_doe4[20][6:30],list_doe4[21][6:30]]) # Solar Water Heating Schedule 24 hrs; wkday=summerdesign, sat=winterdesginrefBEM = [[[None]*16 for k_ in range(3)] for j_ in range(16)] #refBEM (16,3,16) = BEMDef
#Purpose: Loop through every DOE reference csv and extract building data
#Nested loop = 16 types, 3 era, 16 zones = time complexity O(n*m*k) = 768
for i in range(16):
#i = 16 types of buildings
#print "\tType: {} @i={}".format(BLDTYPE[i], i)
# Read building summary (Sheet 1)
file_doe_name_bld = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_BuildingSummary.csv".format(i+1))
list_doe1 = read_csv(file_doe_name_bld)
#listof(listof 3 era values)
nFloor = str2fl(list_doe1[3][3:6]) # Number of Floors, this will be list of floats and str if "basement"
glazing = str2fl(list_doe1[4][3:6]) # [?] Total
hCeiling = str2fl(list_doe1[5][3:6]) # [m] Ceiling height
ver2hor = str2fl(list_doe1[7][3:6]) # Wall to Skin Ratio
AreaRoof = str2fl(list_doe1[8][3:6]) # [m2] Gross Dimensions - Total area
# Read zone summary (Sheet 2)
file_doe_name_zone = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_ZoneSummary.csv".format(i+1))
list_doe2 = read_csv(file_doe_name_zone)
#listof(listof 3 eras)
AreaFloor = str2fl([list_doe2[2][5],list_doe2[3][5],list_doe2[4][5]]) # [m2]
Volume = str2fl([list_doe2[2][6],list_doe2[3][6],list_doe2[4][6]]) # [m3]
AreaWall = str2fl([list_doe2[2][8],list_doe2[3][8],list_doe2[4][8]]) # [m2]
AreaWindow = str2fl([list_doe2[2][9],list_doe2[3][9],list_doe2[4][9]]) # [m2]
Occupant = str2fl([list_doe2[2][11],list_doe2[3][11],list_doe2[4][11]]) # Number of People
Light = str2fl([list_doe2[2][12],list_doe2[3][12],list_doe2[4][12]]) # [W/m2]
Elec = str2fl([list_doe2[2][13],list_doe2[3][13],list_doe2[4][13]]) # [W/m2] Electric Plug and Process
Gas = str2fl([list_doe2[2][14],list_doe2[3][14],list_doe2[4][14]]) # [W/m2] Gas Plug and Process
SHW = str2fl([list_doe2[2][15],list_doe2[3][15],list_doe2[4][15]]) # [Litres/hr] Peak Service Hot Water# Read building summary (Sheet 1)
file_doe_name_bld = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_BuildingSummary.csv".format(i+1))
list_doe1 = read_csv(file_doe_name_bld)
#listof(listof 3 era values)
nFloor = str2fl(list_doe1[3][3:6]) # Number of Floors, this will be list of floats and str if "basement"
glazing = str2fl(list_doe1[4][3:6]) # [?] Total
hCeiling = str2fl(list_doe1[5][3:6]) # [m] Ceiling height
ver2hor = str2fl(list_doe1[7][3:6]) # Wall to Skin Ratio
AreaRoof = str2fl(list_doe1[8][3:6]) # [m2] Gross Dimensions - Total area
# Read zone summary (Sheet 2)
file_doe_name_zone = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_ZoneSummary.csv".format(i+1))
list_doe2 = read_csv(file_doe_name_zone)
#listof(listof 3 eras)
AreaFloor = str2fl([list_doe2[2][5],list_doe2[3][5],list_doe2[4][5]]) # [m2]
Volume = str2fl([list_doe2[2][6],list_doe2[3][6],list_doe2[4][6]]) # [m3]
AreaWall = str2fl([list_doe2[2][8],list_doe2[3][8],list_doe2[4][8]]) # [m2]
AreaWindow = str2fl([list_doe2[2][9],list_doe2[3][9],list_doe2[4][9]]) # [m2]
Occupant = str2fl([list_doe2[2][11],list_doe2[3][11],list_doe2[4][11]]) # Number of People
Light = str2fl([list_doe2[2][12],list_doe2[3][12],list_doe2[4][12]]) # [W/m2]
Elec = str2fl([list_doe2[2][13],list_doe2[3][13],list_doe2[4][13]]) # [W/m2] Electric Plug and Process
Gas = str2fl([list_doe2[2][14],list_doe2[3][14],list_doe2[4][14]]) # [W/m2] Gas Plug and Process
SHW = str2fl([list_doe2[2][15],list_doe2[3][15],list_doe2[4][15]]) # [Litres/hr] Peak Service Hot Water
Vent = str2fl([list_doe2[2][17],list_doe2[3][17],list_doe2[4][17]]) # [L/s/m2] Ventilation
Infil = str2fl([list_doe2[2][20],list_doe2[3][20],list_doe2[4][20]]) # Air Changes Per Hour (ACH) Infiltration
# Read location summary (Sheet 3)
file_doe_name_location = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_LocationSummary.csv".format(i+1))
list_doe3 = read_csv(file_doe_name_location)
#(listof (listof 3 eras (listof 16 climate types)))
TypeWall = [list_doe3[3][4:20],list_doe3[14][4:20],list_doe3[25][4:20]] # Construction type
RvalWall = str2fl([list_doe3[4][4:20],list_doe3[15][4:20],list_doe3[26][4:20]]) # [m2*K/W] R-valueAreaWall = str2fl([list_doe2[2][8],list_doe2[3][8],list_doe2[4][8]]) # [m2]
AreaWindow = str2fl([list_doe2[2][9],list_doe2[3][9],list_doe2[4][9]]) # [m2]
Occupant = str2fl([list_doe2[2][11],list_doe2[3][11],list_doe2[4][11]]) # Number of People
Light = str2fl([list_doe2[2][12],list_doe2[3][12],list_doe2[4][12]]) # [W/m2]
Elec = str2fl([list_doe2[2][13],list_doe2[3][13],list_doe2[4][13]]) # [W/m2] Electric Plug and Process
Gas = str2fl([list_doe2[2][14],list_doe2[3][14],list_doe2[4][14]]) # [W/m2] Gas Plug and Process
SHW = str2fl([list_doe2[2][15],list_doe2[3][15],list_doe2[4][15]]) # [Litres/hr] Peak Service Hot Water
Vent = str2fl([list_doe2[2][17],list_doe2[3][17],list_doe2[4][17]]) # [L/s/m2] Ventilation
Infil = str2fl([list_doe2[2][20],list_doe2[3][20],list_doe2[4][20]]) # Air Changes Per Hour (ACH) Infiltration
# Read location summary (Sheet 3)
file_doe_name_location = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_LocationSummary.csv".format(i+1))
list_doe3 = read_csv(file_doe_name_location)
#(listof (listof 3 eras (listof 16 climate types)))
TypeWall = [list_doe3[3][4:20],list_doe3[14][4:20],list_doe3[25][4:20]] # Construction type
RvalWall = str2fl([list_doe3[4][4:20],list_doe3[15][4:20],list_doe3[26][4:20]]) # [m2*K/W] R-value
TypeRoof = [list_doe3[5][4:20],list_doe3[16][4:20],list_doe3[27][4:20]] # Construction type
RvalRoof = str2fl([list_doe3[6][4:20],list_doe3[17][4:20],list_doe3[28][4:20]]) # [m2*K/W] R-value
Uwindow = str2fl([list_doe3[7][4:20],list_doe3[18][4:20],list_doe3[29][4:20]]) # [W/m2*K] U-factor
SHGC = str2fl([list_doe3[8][4:20],list_doe3[19][4:20],list_doe3[30][4:20]]) # [-] coefficient
HVAC = str2fl([list_doe3[9][4:20],list_doe3[20][4:20],list_doe3[31][4:20]]) # [kW] Air Conditioning
HEAT = str2fl([list_doe3[10][4:20],list_doe3[21][4:20],list_doe3[32][4:20]]) # [kW] Heating
COP = str2fl([list_doe3[11][4:20],list_doe3[22][4:20],list_doe3[33][4:20]]) # [-] Air Conditioning COP
EffHeat = str2fl([list_doe3[12][4:20],list_doe3[23][4:20],list_doe3[34][4:20]]) # [%] Heating Efficiency
FanFlow = str2fl([list_doe3[13][4:20],list_doe3[24][4:20],list_doe3[35][4:20]]) # [m3/s] Fan Max Flow Rate
# Read Schedules (Sheet 4)
file_doe_name_schedules = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_Schedules.csv".format(i+1))
list_doe4 = read_csv(file_doe_name_schedules)
#listof(listof weekday, sat, sun (list of 24 fractions)))
SchEquip = str2fl([list_doe4[1][6:30],list_doe4[2][6:30],list_doe4[3][6:30]]) # Equipment Schedule 24 hrs# Read zone summary (Sheet 2)
file_doe_name_zone = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_ZoneSummary.csv".format(i+1))
list_doe2 = read_csv(file_doe_name_zone)
#listof(listof 3 eras)
AreaFloor = str2fl([list_doe2[2][5],list_doe2[3][5],list_doe2[4][5]]) # [m2]
Volume = str2fl([list_doe2[2][6],list_doe2[3][6],list_doe2[4][6]]) # [m3]
AreaWall = str2fl([list_doe2[2][8],list_doe2[3][8],list_doe2[4][8]]) # [m2]
AreaWindow = str2fl([list_doe2[2][9],list_doe2[3][9],list_doe2[4][9]]) # [m2]
Occupant = str2fl([list_doe2[2][11],list_doe2[3][11],list_doe2[4][11]]) # Number of People
Light = str2fl([list_doe2[2][12],list_doe2[3][12],list_doe2[4][12]]) # [W/m2]
Elec = str2fl([list_doe2[2][13],list_doe2[3][13],list_doe2[4][13]]) # [W/m2] Electric Plug and Process
Gas = str2fl([list_doe2[2][14],list_doe2[3][14],list_doe2[4][14]]) # [W/m2] Gas Plug and Process
SHW = str2fl([list_doe2[2][15],list_doe2[3][15],list_doe2[4][15]]) # [Litres/hr] Peak Service Hot Water
Vent = str2fl([list_doe2[2][17],list_doe2[3][17],list_doe2[4][17]]) # [L/s/m2] Ventilation
Infil = str2fl([list_doe2[2][20],list_doe2[3][20],list_doe2[4][20]]) # Air Changes Per Hour (ACH) Infiltration
# Read location summary (Sheet 3)
file_doe_name_location = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_LocationSummary.csv".format(i+1))
list_doe3 = read_csv(file_doe_name_location)
#(listof (listof 3 eras (listof 16 climate types)))
TypeWall = [list_doe3[3][4:20],list_doe3[14][4:20],list_doe3[25][4:20]] # Construction type
RvalWall = str2fl([list_doe3[4][4:20],list_doe3[15][4:20],list_doe3[26][4:20]]) # [m2*K/W] R-value
TypeRoof = [list_doe3[5][4:20],list_doe3[16][4:20],list_doe3[27][4:20]] # Construction type
RvalRoof = str2fl([list_doe3[6][4:20],list_doe3[17][4:20],list_doe3[28][4:20]]) # [m2*K/W] R-value
Uwindow = str2fl([list_doe3[7][4:20],list_doe3[18][4:20],list_doe3[29][4:20]]) # [W/m2*K] U-factor
SHGC = str2fl([list_doe3[8][4:20],list_doe3[19][4:20],list_doe3[30][4:20]]) # [-] coefficient
HVAC = str2fl([list_doe3[9][4:20],list_doe3[20][4:20],list_doe3[31][4:20]]) # [kW] Air Conditioning
HEAT = str2fl([list_doe3[10][4:20],list_doe3[21][4:20],list_doe3[32][4:20]]) # [kW] Heating
COP = str2fl([list_doe3[11][4:20],list_doe3[22][4:20],list_doe3[33][4:20]]) # [-] Air Conditioning COP
EffHeat = str2fl([list_doe3[12][4:20],list_doe3[23][4:20],list_doe3[34][4:20]]) # [%] Heating Efficiency
FanFlow = str2fl([list_doe3[13][4:20],list_doe3[24][4:20],list_doe3[35][4:20]]) # [m3/s] Fan Max Flow RateHVAC = str2fl([list_doe3[9][4:20],list_doe3[20][4:20],list_doe3[31][4:20]]) # [kW] Air Conditioning
HEAT = str2fl([list_doe3[10][4:20],list_doe3[21][4:20],list_doe3[32][4:20]]) # [kW] Heating
COP = str2fl([list_doe3[11][4:20],list_doe3[22][4:20],list_doe3[33][4:20]]) # [-] Air Conditioning COP
EffHeat = str2fl([list_doe3[12][4:20],list_doe3[23][4:20],list_doe3[34][4:20]]) # [%] Heating Efficiency
FanFlow = str2fl([list_doe3[13][4:20],list_doe3[24][4:20],list_doe3[35][4:20]]) # [m3/s] Fan Max Flow Rate
# Read Schedules (Sheet 4)
file_doe_name_schedules = os.path.join("{}".format(DIR_DOE_PATH), "BLD{}".format(i+1),"BLD{}_Schedules.csv".format(i+1))
list_doe4 = read_csv(file_doe_name_schedules)
#listof(listof weekday, sat, sun (list of 24 fractions)))
SchEquip = str2fl([list_doe4[1][6:30],list_doe4[2][6:30],list_doe4[3][6:30]]) # Equipment Schedule 24 hrs
SchLight = str2fl([list_doe4[4][6:30],list_doe4[5][6:30],list_doe4[6][6:30]]) # Light Schedule 24 hrs; Wkday=Sat=Sun=Hol
SchOcc = str2fl([list_doe4[7][6:30],list_doe4[8][6:30],list_doe4[9][6:30]]) # Occupancy Schedule 24 hrs
SetCool = str2fl([list_doe4[10][6:30],list_doe4[11][6:30],list_doe4[12][6:30]]) # Cooling Setpoint Schedule 24 hrs
SetHeat = str2fl([list_doe4[13][6:30],list_doe4[14][6:30],list_doe4[15][6:30]]) # Heating Setpoint Schedule 24 hrs; summer design
SchGas = str2fl([list_doe4[16][6:30],list_doe4[17][6:30],list_doe4[18][6:30]]) # Gas Equipment Schedule 24 hrs; wkday=sat
SchSWH = str2fl([list_doe4[19][6:30],list_doe4[20][6:30],list_doe4[21][6:30]]) # Solar Water Heating Schedule 24 hrs; wkday=summerdesign, sat=winterdesgin
for j in range(3):
# j = 3 built eras
#print"\tEra: {} @j={}".format(BUILTERA[j], j)
for k in range(16):
# k = 16 climate zones
#print "\tClimate zone: {} @k={}".format(ZONETYPE[k], k)
B = Building(
hCeiling[j], # floorHeight by erathickness = [0.0254,0.0508,0.0508,0.0508,0.0508,D_ins,0.0127]
layers = [Stucco,Concrete,Concrete,Concrete,Concrete,Insulation,Gypsum]
else:
#if it's less then 1 cm don't include in layers
thickness = [0.0254,0.0508,0.0508,0.0508,0.0508,0.0127]
layers = [Stucco,Concrete,Concrete,Concrete,Concrete,Gypsum]
wall = Element(0.08,0.92,thickness,layers,0.,293.,0.,"MassWall")
# If mass wall, assume mass floor (4" concrete)
# Mass (assume 4" concrete);
alb = 0.2
emis = 0.9
thickness = [0.054,0.054]
concrete = Material (1.31, 2240.0*836.8)
mass = Element(alb,emis,thickness,[concrete,concrete],0,293,1,"MassFloor")
elif TypeWall[j][k] == "WoodFrame":
# 0.01m wood siding, tbd insulation, 1/2" gypsum
Rbase = 0.170284091 # based on wood siding, gypsum
Rins = RvalWall[j][k] - Rbase
D_ins = Rins * Insulation.thermalCond #depth of insulatino
if D_ins > 0.01:
thickness = [0.01,D_ins,0.0127]
layers = [Wood,Insulation,Gypsum]
else:
thickness = [0.01,0.0127]
layers = [Wood,Gypsum]
wall = Element(0.22,0.92,thickness,layers,0.,293.,0.,"WoodFrameWall")uwg
Python application for modeling the urban heat island effect.
Package Health Score
61 / 100Popular uwg functions
- uwg.BEMDef.BEMDef
- uwg.building.Building
- uwg.element.Element
- uwg.forcing.Forcing
- uwg.infracalcs.infracalcs
- uwg.material.Material
- uwg.param.Param
- uwg.psychrometrics.psychrometrics
- uwg.RSMDef.RSMDef
- uwg.simparam.SimParam
- uwg.solarcalcs.SolarCalcs
- uwg.UBLDef.UBLDef
- uwg.UCMDef.UCMDef
- uwg.urbflux.urbflux
- uwg.utilities
- uwg.utilities.read_csv
- uwg.utilities.str2fl
- uwg.utilities.zeros
- uwg.uwg
- uwg.weather.Weather