'''JSON parameters'''
with open('jsonOUTPUT_PMVOpt10.txt') as jsonParams:
paramSet = json.load(jsonParams)
'''files used for energy plus simulation'''
iddfile = "C:\EnergyPlusV8-5-0\Energy+.idd"
fname = "SmallOffice.idf"
weatherfile = "USA_UT_Salt.Lake.City.Intl.AP.725720_TMY3.epw"
'''initialize idf file'''
IDF.setiddname(iddfile)
idfdevice = IDF(fname, weatherfile)
'''declare simulation run period'''
Begin_Month = '1'
Begin_Day_of_Month = '14'
End_Month = '1'
End_Day_of_Month = '24'
'''configure the idf file that will be used for simulations'''
configuresmalloffice(idfdevice, Begin_Month, Begin_Day_of_Month, End_Month,
End_Day_of_Month)
'''run parametric simulations'''
for i in range(N):
# for i in range(50):
'''update JSON file and input parameter array for training meta model'''
runJSON = {}
for obj in paramSet['input']:
runJSON[obj['eppy json string']] = obj['Sample Values'][i]
json_functions.updateidf(idfdevice, runJSON)
'''run IDF and the associated batch file to export the custom csv output'''
idfdevice.run(verbose='q')
def test_updateidf():
"""py.test for updateidf"""
iddtxt = """!IDD_Version 8.4.0"""
data = (
(
"""Version,
8.3; !- Version Identifier
""",
{"idf.version..Version_Identifier":"0.1"},
"version",
"Version_Identifier",
"0.1"), # idftxt, dct, key, field, fieldval
(
"""SimulationControl,
No, !- Do Zone Sizing Calculation
No, !- Do System Sizing Calculation
No, !- Do Plant Sizing Calculation
No, !- Run Simulation for Sizing Periods
Yes; !- Run Simulation for Weather File Run Periods
""",
{"idf.SimulationControl..Do_Zone_Sizing_Calculation":"Yes"},
"SimulationControl",
"Do_Zone_Sizing_Calculation",
"Yes"), # idftxt, dct, key, field, fieldval
(
"""Building,
Untitled, !- Name
0.0, !- North Axis {deg}
City, !- Terrain
0.04, !- Loads Convergence Tolerance Value
0.4, !- Temperature Convergence Tolerance Value {deltaC}
FullInteriorAndExterior, !- Solar Distribution
25, !- Maximum Number of Warmup Days
; !- Minimum Number of Warmup Days
""",
{"idf.BUilding.Untitled.Terrain":"Rural"},
"Building",
"Terrain",
"Rural"), # idftxt, dct, key, field, fieldval
# make a new object
(
"""
""",
{"idf.BUilding.Taj.Terrain":"Rural"},
"Building",
"Terrain",
"Rural"), # idftxt, dct, key, field, fieldval
# make a new object with no Name field
(
"""
""",
{"idf.GlobalGeometryRules..Starting_Vertex_Position":"UpperLeftCorner"},
"GlobalGeometryRules",
"Starting_Vertex_Position",
"UpperLeftCorner"), # idftxt, dct, key, field, fieldval
(
"""Building,
Name.name, !- Name
0.0, !- North Axis {deg}
City, !- Terrain
0.04, !- Loads Convergence Tolerance Value
0.4, !- Temperature Convergence Tolerance Value {deltaC}
FullInteriorAndExterior, !- Solar Distribution
25, !- Maximum Number of Warmup Days
; !- Minimum Number of Warmup Days
""",
{"idf.BUilding.Name.name.Terrain":"Rural"},
"Building",
"Terrain",
"Rural"), # idftxt, dct, key, field, fieldval
(
"""Building,
Name.name, !- Name
0.0, !- North Axis {deg}
City, !- Terrain
0.04, !- Loads Convergence Tolerance Value
0.4, !- Temperature Convergence Tolerance Value {deltaC}
FullInteriorAndExterior, !- Solar Distribution
25, !- Maximum Number of Warmup Days
; !- Minimum Number of Warmup Days
""",
{"idf.BUilding.'Name.name'.Terrain":"Rural"},
"Building",
"Terrain",
"Rural"), # idftxt, dct, key, field, fieldval
)
for idftxt, dct, key, field, fieldval in data:
idfhandle = StringIO(idftxt)
idf = IDF(idfhandle)
json_functions.updateidf(idf, dct)
assert idf.idfobjects[key.upper()][0][field] ==fieldval
def __init__(self, idfdevice, CLG_SETPOINT, HTG_SETPOINT):
# modify idf with inputs
self.runJSON = {
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_1':
'Through: %s/%s' % ('12', '31'),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_2':
'For: Weekday',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_3':
'Until: 6:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_4':
str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_5':
'Until: 7:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_6':
str(CLG_SETPOINT[1]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_7':
'Until: 8:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_8':
str(CLG_SETPOINT[2]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_9':
'Until: 9:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_10':
str(CLG_SETPOINT[3]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_11':
'Until: 10:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_12':
str(CLG_SETPOINT[4]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_13':
'Until: 11:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_14':
str(CLG_SETPOINT[5]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_15':
'Until: 12:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_16':
str(CLG_SETPOINT[6]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_17':
'Until: 13:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_18':
str(CLG_SETPOINT[7]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_19':
'Until: 14:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_20':
str(CLG_SETPOINT[8]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_21':
'Until: 15:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_22':
str(CLG_SETPOINT[9]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_23':
'Until: 16:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_24':
str(CLG_SETPOINT[10]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_25':
'Until: 17:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_26':
str(CLG_SETPOINT[11]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_27':
'Until: 18:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_28':
str(CLG_SETPOINT[12]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_29':
'Until: 19:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_30':
str(CLG_SETPOINT[13]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_31':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_32':
str(CLG_SETPOINT[14]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_33':
'For: Weekend',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_34':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_35':
str(29.44),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_36':
'For: Holiday',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_37':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_38':
str(29.44),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_39':
'For: WinterDesignDay',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_40':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_41':
str(29.44),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_42':
'For: SummerDesignDay',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_43':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_44':
str(29.44),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_45':
'For: CustomDay1',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_46':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_47':
str(29.44),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_48':
'For: CustomDay2',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_49':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_50':
str(29.44),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_1':
'Through: %s/%s' % ('12', '31'),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_2':
'For: Weekday',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_3':
'Until: 6:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_4':
str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_5':
'Until: 7:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_6':
str(HTG_SETPOINT[1]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_7':
'Until: 8:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_8':
str(HTG_SETPOINT[2]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_9':
'Until: 9:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_10':
str(HTG_SETPOINT[3]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_11':
'Until: 10:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_12':
str(HTG_SETPOINT[4]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_13':
'Until: 11:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_14':
str(HTG_SETPOINT[5]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_15':
'Until: 12:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_16':
str(HTG_SETPOINT[6]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_17':
'Until: 13:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_18':
str(HTG_SETPOINT[7]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_19':
'Until: 14:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_20':
str(HTG_SETPOINT[8]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_21':
'Until: 15:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_22':
str(HTG_SETPOINT[9]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_23':
'Until: 16:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_24':
str(HTG_SETPOINT[10]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_25':
'Until: 17:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_26':
str(HTG_SETPOINT[11]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_27':
'Until: 18:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_28':
str(HTG_SETPOINT[12]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_29':
'Until: 19:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_30':
str(HTG_SETPOINT[13]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_31':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_32':
str(HTG_SETPOINT[14]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_33':
'For: Weekend',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_34':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_35':
str(29.44),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_36':
'For: Holiday',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_37':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_38':
str(29.44),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_39':
'For: WinterDesignDay',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_40':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_41':
str(29.44),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_42':
'For: SummerDesignDay',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_43':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_44':
str(29.44),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_45':
'For: CustomDay1',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_46':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_47':
str(29.44),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_48':
'For: CustomDay2',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_49':
'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_50':
str(29.44)
}
# self.runJSON = {'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_1': 'Through: 12/31',
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_2': 'For: Weekday',
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_3': 'Until: 6:00',
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_4': str(HTG_SETPOINT[0]),
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_5': 'Until: 7:00',
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_6': str(HTG_SETPOINT[1]),
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_7': 'Until: 8:00',
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_8': str(HTG_SETPOINT[2]),
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_9': 'Until: 19:00',
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_10': str(HTG_SETPOINT[3]),
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_11': 'Until: 24:00',
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_12': str(HTG_SETPOINT[4]),
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_13': 'For: Weekend',
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_14': 'Until: 24:00',
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_15': str(HTG_SETPOINT[5]),
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_16': 'For: Holiday',
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_17': 'Until: 24:00',
# 'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_18': str(HTG_SETPOINT[6]),
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_1': 'Through: 12/31',
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_2': 'For: Weekday',
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_3': 'Until: 6:00',
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_4': str(CLG_SETPOINT[0]),
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_5': 'Until: 7:00',
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_6': str(CLG_SETPOINT[1]),
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_7': 'Until: 8:00',
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_8': str(CLG_SETPOINT[2]),
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_9': 'Until: 19:00',
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_10': str(CLG_SETPOINT[3]),
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_11': 'Until: 24:00',
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_12': str(CLG_SETPOINT[4]),
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_13': 'For: Weekend',
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_14': 'Until: 24:00',
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_15': str(CLG_SETPOINT[5]),
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_16': 'For: Holiday',
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_17': 'Until: 24:00',
# 'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_18': str(CLG_SETPOINT[6])}
json_functions.updateidf(idfdevice, self.runJSON)
# run IDF and the associated batch file to export the custom csv output
# self, idf='SmallOffice.idf', weather='USA_UT_Salt.Lake.City.Intl.AP.725720_TMY3.epw', ep_version='8-5-0'
idfdevice.run(verbose='q')
os.system(r'CD E:\Masters Thesis\EnergyPlus MPC\Simulations\Baseline')
os.system('CustomCSV SO OUTPUT')
# self.smallofficeoutputs('SO_OUTPUT_hourly.csv')
# Read csv file into new data dictionary
newEntry = defaultdict(list)
with open('SO_OUTPUT_hourly.csv', newline='') as newFile:
newData = csv.DictReader(newFile)
for row in newData:
[newEntry[key].append(value) for key, value in row.items()]
'''Date/Time array'''
self.DateTime = np.asarray(newEntry['Date/Time'], dtype=str)
'''PMV values for core zone'''
self.corePMV = np.asarray(newEntry[
'CORE_ZN:Zone Thermal Comfort Fanger Model PMV [](Hourly)'],
dtype=np.float32)
self.corePMV_mean = np.mean(self.corePMV)
self.corePMV_max = np.max(self.corePMV)
self.corePMV_min = np.min(self.corePMV)
'''PMV values for zone 1'''
self.zn1PMV = np.asarray(newEntry[
'PERIMETER_ZN_1:Zone Thermal Comfort Fanger Model PMV [](Hourly)'],
dtype=np.float32)
self.zn1PMV_mean = np.mean(self.zn1PMV)
self.zn1PMV_max = np.max(self.zn1PMV)
self.zn1PMV_min = np.min(self.zn1PMV)
'''PMV values for zone 2'''
self.zn2PMV = np.asarray(newEntry[
'PERIMETER_ZN_2:Zone Thermal Comfort Fanger Model PMV [](Hourly)'],
dtype=np.float32)
self.zn2PMV_mean = np.mean(self.zn2PMV)
self.zn2PMV_max = np.max(self.zn2PMV)
self.zn2PMV_min = np.min(self.zn2PMV)
'''PMV values for zone 3'''
self.zn3PMV = np.asarray(newEntry[
'PERIMETER_ZN_3:Zone Thermal Comfort Fanger Model PMV [](Hourly)'],
dtype=np.float32)
self.zn3PMV_mean = np.mean(self.zn3PMV)
self.zn3PMV_max = np.max(self.zn3PMV)
self.zn3PMV_min = np.min(self.zn3PMV)
'''PMV values for zone 4'''
self.zn4PMV = np.asarray(newEntry[
'PERIMETER_ZN_4:Zone Thermal Comfort Fanger Model PMV [](Hourly)'],
dtype=np.float32)
self.zn4PMV_mean = np.mean(self.zn4PMV)
self.zn4PMV_max = np.max(self.zn4PMV)
self.zn4PMV_min = np.min(self.zn4PMV)
'''PMV values for all zones'''
self.allPMV = np.asarray([[self.corePMV], [self.zn1PMV], [self.zn2PMV],
[self.zn3PMV], [self.zn4PMV]])
self.allPMV_mean1 = np.mean(self.allPMV, 0)
self.allPMV_mean2 = np.mean(self.allPMV_mean1)
self.allPMV_max = np.amax(self.allPMV)
self.allPMV_min = np.amin(self.allPMV)
'''HVAC power demand (kW)'''
self.hvacPower = np.asarray(newEntry[
'Whole Building:Facility Total HVAC Electric Demand Power [W](Hourly)'],
dtype=np.float32)
self.hvacPower_ave = np.mean(self.hvacPower)
self.hvacPower_max = np.max(self.hvacPower)
def test_updateidf():
"""py.test for updateidf"""
iddtxt = """!IDD_Version 8.4.0"""
data = (
(
"""Version,
8.3; !- Version Identifier
""",
{
"idf.version..Version_Identifier": "0.1"
},
"version",
"Version_Identifier",
"0.1",
), # idftxt, dct, key, field, fieldval
(
"""SimulationControl,
No, !- Do Zone Sizing Calculation
No, !- Do System Sizing Calculation
No, !- Do Plant Sizing Calculation
No, !- Run Simulation for Sizing Periods
Yes; !- Run Simulation for Weather File Run Periods
""",
{
"idf.SimulationControl..Do_Zone_Sizing_Calculation": "Yes"
},
"SimulationControl",
"Do_Zone_Sizing_Calculation",
"Yes",
), # idftxt, dct, key, field, fieldval
(
"""Building,
Untitled, !- Name
0.0, !- North Axis {deg}
City, !- Terrain
0.04, !- Loads Convergence Tolerance Value
0.4, !- Temperature Convergence Tolerance Value {deltaC}
FullInteriorAndExterior, !- Solar Distribution
25, !- Maximum Number of Warmup Days
; !- Minimum Number of Warmup Days
""",
{
"idf.BUilding.Untitled.Terrain": "Rural"
},
"Building",
"Terrain",
"Rural",
), # idftxt, dct, key, field, fieldval
# make a new object
(
"""
""",
{
"idf.BUilding.Taj.Terrain": "Rural"
},
"Building",
"Terrain",
"Rural",
), # idftxt, dct, key, field, fieldval
# make a new object with no Name field
(
"""
""",
{
"idf.GlobalGeometryRules..Starting_Vertex_Position":
"UpperLeftCorner"
},
"GlobalGeometryRules",
"Starting_Vertex_Position",
"UpperLeftCorner",
), # idftxt, dct, key, field, fieldval
(
"""Building,
Name.name, !- Name
0.0, !- North Axis {deg}
City, !- Terrain
0.04, !- Loads Convergence Tolerance Value
0.4, !- Temperature Convergence Tolerance Value {deltaC}
FullInteriorAndExterior, !- Solar Distribution
25, !- Maximum Number of Warmup Days
; !- Minimum Number of Warmup Days
""",
{
"idf.BUilding.Name.name.Terrain": "Rural"
},
"Building",
"Terrain",
"Rural",
), # idftxt, dct, key, field, fieldval
(
"""Building,
Name.name, !- Name
0.0, !- North Axis {deg}
City, !- Terrain
0.04, !- Loads Convergence Tolerance Value
0.4, !- Temperature Convergence Tolerance Value {deltaC}
FullInteriorAndExterior, !- Solar Distribution
25, !- Maximum Number of Warmup Days
; !- Minimum Number of Warmup Days
""",
{
"idf.BUilding.'Name.name'.Terrain": "Rural"
},
"Building",
"Terrain",
"Rural",
), # idftxt, dct, key, field, fieldval
)
for idftxt, dct, key, field, fieldval in data:
idfhandle = StringIO(idftxt)
idf = IDF(idfhandle)
json_functions.updateidf(idf, dct)
assert idf.idfobjects[key][0][field] == fieldval
def __init__(self, idfdevice, CLG_SETPOINT, HTG_SETPOINT):
'''update setpoints and run energyplus simulation'''
'''append setpoints from optimizer to the optimized list'''
# OptCS[(24 - len(CLG_SETPOINT)):] = CLG_SETPOINT
# OptHS[(24 - len(HTG_SETPOINT)):] = HTG_SETPOINT
'''update idf with uncertain parameters for the parameter file listed'''
runJSON = {}
for object in paramSet['input']: runJSON[object['eppy json string']] = object['Sample Values'][random.randint(0, len(object['Sample Values'])-1)]
json_functions.updateidf(idfdevice, runJSON)
'''modify idf with inputs'''
self.runJSON = {'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_1': 'Through: %s/%s' % ('12', '31'),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_2': 'For: Weekday',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_3': 'Until: 1:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_4': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_5': 'Until: 2:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_6': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_7': 'Until: 3:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_8': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_9': 'Until: 4:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_10': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_11': 'Until: 5:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_12': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_13': 'Until: 6:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_14': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_15': 'Until: 7:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_16': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_17': 'Until: 8:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_18': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_19': 'Until: 9:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_20': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_21': 'Until: 10:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_22': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_23': 'Until: 11:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_24': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_25': 'Until: 12:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_26': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_27': 'Until: 13:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_28': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_29': 'Until: 14:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_30': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_31': 'Until: 15:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_32': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_33': 'Until: 16:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_34': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_35': 'Until: 17:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_36': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_37': 'Until: 18:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_38': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_39': 'Until: 19:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_40': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_41': 'Until: 20:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_42': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_43': 'Until: 21:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_44': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_45': 'Until: 22:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_46': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_47': 'Until: 23:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_48': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_49': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_50': str(CLG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_51': 'For: Weekend',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_52': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_53': str(29.44),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_54': 'For: Holiday',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_55': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_56': str(29.44),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_57': 'For: WinterDesignDay',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_58': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_59': str(29.44),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_60': 'For: SummerDesignDay',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_61': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_62': str(29.44),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_63': 'For: CustomDay1',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_64': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_65': str(29.44),
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_66': 'For: CustomDay2',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_67': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.CLGSETP_SCH_YES_OPTIMUM.Field_68': str(29.44),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_1': 'Through: %s/%s' % ('12', '31'),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_2': 'For: Weekday',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_3': 'Until: 1:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_4': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_5': 'Until: 2:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_6': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_7': 'Until: 3:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_8': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_9': 'Until: 4:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_10': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_11': 'Until: 5:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_12': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_13': 'Until: 6:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_14': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_15': 'Until: 7:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_16': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_17': 'Until: 8:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_18': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_19': 'Until: 9:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_20': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_21': 'Until: 10:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_22': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_23': 'Until: 11:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_24': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_25': 'Until: 12:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_26': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_27': 'Until: 13:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_28': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_29': 'Until: 14:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_30': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_31': 'Until: 15:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_32': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_33': 'Until: 16:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_34': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_35': 'Until: 17:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_36': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_37': 'Until: 18:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_38': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_39': 'Until: 19:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_40': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_41': 'Until: 20:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_42': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_43': 'Until: 21:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_44': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_45': 'Until: 22:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_46': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_47': 'Until: 23:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_48': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_49': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_50': str(HTG_SETPOINT[0]),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_51': 'For: Weekend',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_52': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_53': str(29.44),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_54': 'For: Holiday',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_55': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_56': str(29.44),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_57': 'For: WinterDesignDay',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_58': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_59': str(29.44),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_60': 'For: SummerDesignDay',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_61': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_62': str(29.44),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_63': 'For: CustomDay1',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_64': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_65': str(29.44),
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_66': 'For: CustomDay2',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_67': 'Until: 24:00',
'idf.SCHEDULE:COMPACT.HTGSETP_SCH_YES_OPTIMUM.Field_68': str(29.44)
}
json_functions.updateidf(idfdevice, self.runJSON)
'''run IDF and the associated batch file to export the custom csv output'''
'''self, idf='SmallOffice.idf', weather='USA_UT_Salt.Lake.City.Intl.AP.725720_TMY3.epw', ep_version='8-5-0'''
idfdevice.run(verbose='q')
os.system(r'CD E:\Masters Thesis\EnergyPlus MPC\Simulations\Baseline')
os.system('CustomCSV SO OUTPUT')
# self.smallofficeoutputs('SO_OUTPUT_hourly.csv')
'''Read csv file into new data dictionary'''
newEntry = defaultdict(list)
with open('SO_OUTPUT_hourly.csv', newline='') as newFile:
newData = csv.DictReader(newFile)
for row in newData:
[newEntry[key].append(value) for key, value in row.items()]
'''Date/Time array'''
self.DateTime = np.asarray(newEntry['Date/Time'], dtype=str)
'''Outdoor dry bulb temperature'''
self.outdoorT = np.asarray(newEntry['Environment:Site Outdoor Air Drybulb Temperature [C](Hourly)'],
dtype=np.float32)
'''PMV values for core zone'''
self.corePMV = np.asarray(newEntry['CORE_ZN:Zone Thermal Comfort Fanger Model PMV [](Hourly)'],
dtype=np.float32)
self.corePMV_mean = np.mean(self.corePMV)
self.corePMV_max = np.max(self.corePMV)
self.corePMV_min = np.min(self.corePMV)
'''PMV values for zone 1'''
self.zn1PMV = np.asarray(newEntry['PERIMETER_ZN_1:Zone Thermal Comfort Fanger Model PMV [](Hourly)'],
dtype=np.float32)
self.zn1PMV_mean = np.mean(self.zn1PMV)
self.zn1PMV_max = np.max(self.zn1PMV)
self.zn1PMV_min = np.min(self.zn1PMV)
'''PMV values for zone 2'''
self.zn2PMV = np.asarray(newEntry['PERIMETER_ZN_2:Zone Thermal Comfort Fanger Model PMV [](Hourly)'],
dtype=np.float32)
self.zn2PMV_mean = np.mean(self.zn2PMV)
self.zn2PMV_max = np.max(self.zn2PMV)
self.zn2PMV_min = np.min(self.zn2PMV)
'''PMV values for zone 3'''
self.zn3PMV = np.asarray(newEntry['PERIMETER_ZN_3:Zone Thermal Comfort Fanger Model PMV [](Hourly)'],
dtype=np.float32)
self.zn3PMV_mean = np.mean(self.zn3PMV)
self.zn3PMV_max = np.max(self.zn3PMV)
self.zn3PMV_min = np.min(self.zn3PMV)
'''PMV values for zone 4'''
self.zn4PMV = np.asarray(newEntry['PERIMETER_ZN_4:Zone Thermal Comfort Fanger Model PMV [](Hourly)'],
dtype=np.float32)
self.zn4PMV_mean = np.mean(self.zn4PMV)
self.zn4PMV_max = np.max(self.zn4PMV)
self.zn4PMV_min = np.min(self.zn4PMV)
'''PMV values for all zones'''
self.allPMV = np.asarray([[self.corePMV], [self.zn1PMV], [self.zn2PMV], [self.zn3PMV], [self.zn4PMV]])
self.allPMV_mean1 = np.mean(self.allPMV, 0)
self.allPMV_mean2 = np.mean(self.allPMV_mean1)
self.allPMV_max = np.amax(self.allPMV)
self.allPMV_min = np.amin(self.allPMV)
'''HVAC power demand (kW)'''
self.hvacPower = np.asarray(newEntry['Whole Building:Facility Total HVAC Electric Demand Power [W](Hourly)'],
dtype=np.float32)
self.hvacPower_ave = np.mean(self.hvacPower)
self.hvacPower_max = np.max(self.hvacPower)
'''Core Zone Cooling Setpoint (C)'''
self.coreCS = np.asarray(newEntry['CORE_ZN:Zone Thermostat Cooling Setpoint Temperature [C](Hourly)'],
dtype=np.float32)
self.coreCS_mean = np.mean(self.coreCS)
self.coreCS_max = np.max(self.coreCS)
self.coreCS_min = np.min(self.coreCS)
'''Zone 1 Cooling Setpoint (C)'''
self.zn1CS = np.asarray(newEntry['PERIMETER_ZN_1:Zone Thermostat Cooling Setpoint Temperature [C](Hourly)'],
dtype=np.float32)
self.zn1CS_mean = np.mean(self.zn1CS)
self.zn1CS_max = np.max(self.zn1CS)
self.zn1CS_min = np.min(self.zn1CS)
'''Zone 2 Cooling Setpoint (C)'''
self.zn2CS = np.asarray(newEntry['PERIMETER_ZN_2:Zone Thermostat Cooling Setpoint Temperature [C](Hourly)'],
dtype=np.float32)
self.zn2CS_mean = np.mean(self.zn2CS)
self.zn2CS_max = np.max(self.zn2CS)
self.zn2CS_min = np.min(self.zn2CS)
'''Zone 3 Cooling Setpoint (C)'''
self.zn3CS = np.asarray(newEntry['PERIMETER_ZN_3:Zone Thermostat Cooling Setpoint Temperature [C](Hourly)'],
dtype=np.float32)
self.zn3CS_mean = np.mean(self.zn3CS)
self.zn3CS_max = np.max(self.zn3CS)
self.zn3CS_min = np.min(self.zn3CS)
'''Zone 4 Cooling Setpoint (C)'''
self.zn4CS = np.asarray(newEntry['PERIMETER_ZN_4:Zone Thermostat Cooling Setpoint Temperature [C](Hourly)'],
dtype=np.float32)
self.zn4CS_mean = np.mean(self.zn4CS)
self.zn4CS_max = np.max(self.zn4CS)
self.zn4CS_min = np.min(self.zn4CS)
'''All Zones Cooling Setpoint (C)'''
self.allCS = np.asarray([[self.coreCS], [self.zn1CS], [self.zn2CS], [self.zn3CS], [self.zn4CS]])
self.allCS_mean1 = np.mean(self.allCS, 1)
self.allCS_mean2 = np.mean(self.allCS_mean1)
self.allCS_max = np.max(self.allCS)
self.allCS_min = np.min(self.allCS)
'''Core Zone Heating Setpoint (C)'''
self.coreHS = np.asarray(newEntry['CORE_ZN:Zone Thermostat Heating Setpoint Temperature [C](Hourly)'],
dtype=np.float32)
self.coreHS_mean = np.mean(self.coreHS)
self.coreHS_max = np.max(self.coreHS)
self.coreHS_min = np.min(self.coreHS)
'''Zone 1 Heating Setpoint (C)'''
self.zn1HS = np.asarray(newEntry['PERIMETER_ZN_1:Zone Thermostat Heating Setpoint Temperature [C](Hourly)'],
dtype=np.float32)
self.zn1HS_mean = np.mean(self.zn1HS)
self.zn1HS_max = np.max(self.zn1HS)
self.zn1HS_min = np.min(self.zn1HS)
'''Zone 2 Heating Setpoint (C)'''
self.zn2HS = np.asarray(newEntry['PERIMETER_ZN_2:Zone Thermostat Heating Setpoint Temperature [C](Hourly)'],
dtype=np.float32)
self.zn2HS_mean = np.mean(self.zn2HS)
self.zn2HS_max = np.max(self.zn2HS)
self.zn2HS_min = np.min(self.zn2HS)
'''Zone 3 Heating Setpoint (C)'''
self.zn3HS = np.asarray(newEntry['PERIMETER_ZN_3:Zone Thermostat Heating Setpoint Temperature [C](Hourly)'],
dtype=np.float32)
self.zn3HS_mean = np.mean(self.zn3HS)
self.zn3HS_max = np.max(self.zn3HS)
self.zn3HS_min = np.min(self.zn3HS)
'''Zone 4 Heating Setpoint (C)'''
self.zn4HS = np.asarray(newEntry['PERIMETER_ZN_4:Zone Thermostat Heating Setpoint Temperature [C](Hourly)'],
dtype=np.float32)
self.zn4HS_mean = np.mean(self.zn4HS)
self.zn4HS_max = np.max(self.zn4HS)
self.zn4HS_min = np.min(self.zn4HS)
'''All Zones Heating Setpoint (C)'''
self.allHS = np.asarray([[self.coreHS], [self.zn1HS], [self.zn2HS], [self.zn3HS], [self.zn4HS]])
self.allHS_mean1 = np.mean(self.allHS, 1)
self.allHS_mean2 = np.mean(self.allHS_mean1)
self.allHS_max = np.max(self.allHS)
self.allHS_min = np.min(self.allHS)
'''Core Zone Thermostat Temperature (C)'''
self.coreT = np.asarray(newEntry['CORE_ZN:Zone Thermostat Air Temperature [C](Hourly)'],
dtype=np.float32)
self.coreT_mean = np.mean(self.coreT)
self.coreT_max = np.max(self.coreT)
self.coreT_min = np.min(self.coreT)
'''Zone 1 Thermostat Temperature (C)'''
self.zn1T = np.asarray(newEntry['PERIMETER_ZN_1:Zone Thermostat Air Temperature [C](Hourly)'],
dtype=np.float32)
self.zn1T_mean = np.mean(self.zn1T)
self.zn1T_max = np.max(self.zn1T)
self.zn1T_min = np.min(self.zn1T)
'''Zone 2 Thermostat Temperature (C)'''
self.zn2T = np.asarray(newEntry['PERIMETER_ZN_2:Zone Thermostat Air Temperature [C](Hourly)'],
dtype=np.float32)
self.zn2T_mean = np.mean(self.zn2T)
self.zn2T_max = np.max(self.zn2T)
self.zn2T_min = np.min(self.zn2T)
'''Zone 3 Thermostat Temperature (C)'''
self.zn3T = np.asarray(newEntry['PERIMETER_ZN_3:Zone Thermostat Air Temperature [C](Hourly)'],
dtype=np.float32)
self.zn3T_mean = np.mean(self.zn3T)
self.zn3T_max = np.max(self.zn3T)
self.zn3T_min = np.min(self.zn3T)
'''Zone 4 Thermostat Temperature (C)'''
self.zn4T = np.asarray(newEntry['PERIMETER_ZN_4:Zone Thermostat Air Temperature [C](Hourly)'],
dtype=np.float32)
self.zn4T_mean = np.mean(self.zn4T)
self.zn4T_max = np.max(self.zn4T)
self.zn4T_min = np.min(self.zn4T)
'''All Zones Thermostat Temperature (C)'''
self.allT = np.asarray([[self.coreT], [self.zn1T], [self.zn2T], [self.zn3T], [self.zn4T]])
self.allT_mean1 = np.mean(self.allT, 1)
self.allT_mean2 = np.mean(self.allT_mean1)
self.allT_max = np.max(self.allT)
self.allT_min = np.min(self.allT)