def readDOE(serialize_output=True):
"""
Read csv files of DOE buildings
Sheet 1 = BuildingSummary
Sheet 2 = ZoneSummary
Sheet 3 = LocationSummary
Sheet 4 = Schedules
Note BLD8 & 10 = school
Then make matrix of ref data as nested nested lists [16, 3, 16]:
matrix refDOE = Building objs
matrix Schedule = SchDef objs
matrix refBEM (16,3,16) = BEMDef
where:
[16,3,16] is Type = 1-16, Era = 1-3, climate zone = 1-16
i.e.
Type: FullServiceRestaurant, Era: Pre80, Zone: 6A Minneapolis
Nested tree:
[TYPE_1:
ERA_1:
CLIMATE_ZONE_1
...
CLIMATE_ZONE_16
ERA_2:
CLIMATE_ZONE_1
...
CLIMATE_ZONE_16
...
ERA_3:
CLIMATE_ZONE_1
...
CLIMATE_ZONE_16]
"""
#Nested, nested lists of Building, SchDef, BEMDef objects
refDOE = map(lambda j_: map(lambda k_: [None] * 16, [None] * 3),
[None] * 16) #refDOE(16,3,16) = Building;
Schedule = map(lambda j_: map(lambda k_: [None] * 16, [None] * 3),
[None] * 16) #Schedule (16,3,16) = SchDef;
refBEM = map(lambda j_: map(lambda k_: [None] * 16, [None] * 3),
[None] * 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 xrange(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) 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=winterdesgin
for j in xrange(3):
# j = 3 built eras
#print"\tEra: {} @j={}".format(BUILTERA[j], j)
for k in xrange(16):
# k = 16 climate zones
#print "\tClimate zone: {} @k={}".format(ZONETYPE[k], k)
B = Building(
hCeiling[j], # floorHeight by era
1, # intHeatNight
1, # intHeatDay
0.1, # intHeatFRad
0.1, # intHeatFLat
Infil[j], # infil (ACH) by era
Vent[j] /
1000., # vent (m^3/s/m^2) by era, converted from liters
glazing[j], # glazing ratio by era
Uwindow[j][k], # uValue by era, by climate type
SHGC[j][k], # SHGC, by era, by climate type
'AIR', # cooling condensation system type: AIR, WATER
COP[j][k], # cop by era, climate type
297, # coolSetpointDay = 24 C
297, # coolSetpointNight
293, # heatSetpointDay = 20 C
293, # heatSetpointNight
(HVAC[j][k] * 1000.0) / AreaFloor[
j], # coolCap converted to W/m2 by era, climate type
EffHeat[j][k], # heatEff by era, climate type
293) # initialTemp at 20 C
#Not defined in the constructor
B.heatCap = (HEAT[j][k] * 1000.0) / AreaFloor[
j] # heating Capacity converted to W/m2 by era, climate type
B.Type = BLDTYPE[i]
B.Era = BUILTERA[j]
B.Zone = ZONETYPE[k]
refDOE[i][j][k] = B
# Define wall, mass(floor), roof
# Reference from E+ for conductivity, thickness (reference below)
# Material: (thermalCond, volHeat = specific heat * density)
Concrete = Material(1.311, 836.8 * 2240, "Concrete")
Insulation = Material(0.049, 836.8 * 265.0, "Insulation")
Gypsum = Material(0.16, 830.0 * 784.9, "Gypsum")
Wood = Material(0.11, 1210.0 * 544.62, "Wood")
Stucco = Material(0.6918, 837.0 * 1858.0, "Stucco")
# Wall (1 in stucco, concrete, insulation, gypsum)
# Check TypWall by era, by climate
if TypeWall[j][k] == "MassWall":
#Construct wall based on R value of Wall from refDOE and properties defined above
# 1" stucco, 8" concrete, tbd insulation, 1/2" gypsum
Rbase = 0.271087 # R val based on stucco, concrete, gypsum
Rins = RvalWall[j][k] - Rbase #find insulation value
D_ins = Rins * Insulation.thermalCond # depth of ins from m2*K/W * W/m*K = m
if D_ins > 0.01:
thickness = [
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")
# If wood frame wall, assume wooden floor
alb = 0.2
emis = 0.9
thickness = [0.05, 0.05]
wood = Material(1.31, 2240.0 * 836.8)
mass = Element(alb, emis, thickness, [wood, wood], 0.,
293., 1., "WoodFloor")
elif TypeWall[j][k] == "SteelFrame":
# 1" stucco, 8" concrete, tbd insulation, 1/2" gypsum
Rbase = 0.271087 # based on stucco, concrete, gypsum
Rins = RvalWall[j][k] - Rbase
D_ins = Rins * Insulation.thermalCond
if D_ins > 0.01:
thickness = [
0.0254, 0.0508, 0.0508, 0.0508, 0.0508, D_ins,
0.0127
]
layers = [
Stucco, Concrete, Concrete, Concrete, Concrete,
Insulation, Gypsum
]
else: # If insulation is too thin, assume no insulation
thickness = [
0.0254, 0.0508, 0.0508, 0.0508, 0.0508, 0.0127
]
layers = [
Stucco, Concrete, Concrete, Concrete, Concrete,
Gypsum
]
wall = Element(0.15, 0.92, thickness, layers, 0., 293., 0.,
"SteelFrame")
# If mass wall, assume mass foor
# Mass (assume 4" concrete),
alb = 0.2
emis = 0.93
thickness = [0.05, 0.05]
mass = Element(alb, emis, thickness, [Concrete, Concrete],
0., 293., 1., "MassFloor")
elif TypeWall[j][k] == "MetalWall":
# metal siding, insulation, 1/2" gypsum
alb = 0.2
emis = 0.9
D_ins = max(
(RvalWall[j][k] * Insulation.thermalCond) / 2, 0.01
) #use derived insul thickness or 0.01 based on max
thickness = [D_ins, D_ins, 0.0127]
materials = [Insulation, Insulation, Gypsum]
wall = Element(alb, emis, thickness, materials, 0, 293, 0,
"MetalWall")
# Mass (assume 4" concrete);
alb = 0.2
emis = 0.9
thickness = [0.05, 0.05]
concrete = Material(1.31, 2240.0 * 836.8)
mass = Element(alb, emis, thickness, [concrete, concrete],
0., 293., 1., "MassFloor")
# Roof
if TypeRoof[j][k] == "IEAD": #Insulation Entirely Above Deck
# IEAD-> membrane, insulation, decking
alb = 0.2
emis = 0.93
D_ins = max(RvalRoof[j][k] * Insulation.thermalCond / 2.,
0.01)
roof = Element(alb, emis, [D_ins, D_ins],
[Insulation, Insulation], 0., 293., 0.,
"IEAD")
elif TypeRoof[j][k] == "Attic":
# IEAD-> membrane, insulation, decking
alb = 0.2
emis = 0.9
D_ins = max(RvalRoof[j][k] * Insulation.thermalCond / 2.,
0.01)
roof = Element(alb, emis, [D_ins, D_ins],
[Insulation, Insulation], 0., 293., 0.,
"Attic")
elif TypeRoof[j][k] == "MetalRoof":
# IEAD-> membrane, insulation, decking
alb = 0.2
emis = 0.9
D_ins = max(RvalRoof[j][k] * Insulation.thermalCond / 2.,
0.01)
roof = Element(alb, emis, [D_ins, D_ins],
[Insulation, Insulation], 0., 293., 0.,
"MetalRoof")
# Define bulding energy model, set fraction of the urban floor space of this typology to zero
refBEM[i][j][k] = BEMDef(B, mass, wall, roof, 0.0)
refBEM[i][j][k].building.FanMax = FanFlow[j][
k] # max fan flow rate (m^3/s) per DOE
Schedule[i][j][k] = SchDef()
Schedule[i][j][
k].Elec = SchEquip # 3x24 matrix of schedule for fraction electricity (WD,Sat,Sun)
Schedule[i][j][
k].Light = SchLight # 3x24 matrix of schedule for fraction light (WD,Sat,Sun)
Schedule[i][j][
k].Gas = SchGas # 3x24 matrix of schedule for fraction gas (WD,Sat,Sun)
Schedule[i][j][
k].Occ = SchOcc # 3x24 matrix of schedule for fraction occupancy (WD,Sat,Sun)
Schedule[i][j][
k].Cool = SetCool # 3x24 matrix of schedule for fraction cooling temp (WD,Sat,Sun)
Schedule[i][j][
k].Heat = SetHeat # 3x24 matrix of schedule for fraction heating temp (WD,Sat,Sun)
Schedule[i][j][
k].SWH = SchSWH # 3x24 matrix of schedule for fraction SWH (WD,Sat,Sun
Schedule[i][j][k].Qelec = Elec[
j] # W/m^2 (max) for electrical plug process
Schedule[i][j][k].Qlight = Light[j] # W/m^2 (max) for light
Schedule[i][j][k].Nocc = Occupant[j] / AreaFloor[
j] # Person/m^2
Schedule[i][j][k].Qgas = Gas[j] # W/m^2 (max) for gas
Schedule[i][j][k].Vent = Vent[j] / 1000.0 # m^3/m^2 per person
Schedule[i][j][k].Vswh = SHW[j] / AreaFloor[
j] # litres per hour per m^2 of floor
# if not test serialize refDOE,refBEM,Schedule and store in resources
if serialize_output:
# create a binary file for serialized obj
pkl_file_path = os.path.join(DIR_CURR, '..', 'resources',
'readDOE.pkl')
pickle_readDOE = open(pkl_file_path, 'wb')
# dump in ../resources
# Pickle objects, protocol 1 b/c binary file
cPickle.dump(refDOE, pickle_readDOE, 1)
cPickle.dump(refBEM, pickle_readDOE, 1)
cPickle.dump(Schedule, pickle_readDOE, 1)
pickle_readDOE.close()
return refDOE, refBEM, Schedule
