def test_t_globe():
assert (t_mrt(tg=53.2, tdb=30, v=0.3, d=0.1, emissivity=0.95)) == 74.8
assert (t_mrt(tg=55, tdb=30, v=0.3, d=0.1, emissivity=0.95)) == 77.8
def comfortAnalysis(count, tdb, tg, rh, tout, occupancy):
#START###########################################
#sensor data here to be removed once the data is read in
#tdb = 80# dry-bulb air temperature, [$^{\circ}$C]
#tg = 78#globe temperature
#rh = 50 # relative humidity, [%]
#tout = 90 #outdoor temperature
#occupancy = 1 #occupancy
print(count, tdb, tg, rh, tout, occupancy)
#formula to convert to Fahrenheit from Celsius
#F = (Cx1.8) + 32
#reset all error flags, needed all error variables in case more than one is true
error_1 = ""
error_2 = ""
error_3 = ""
error_4 = ""
error_5 = ""
if tdb <= 0 or tdb >= 110: #test logic behind the measurements
error_1 = "Malfunction indoor temp"
if tdb <= 55 or tdb >= 90:
error_2 = "Potential unsafe conditions"
if rh < 0 or rh > 100:
error_3 = "Malfunction rh"
if tout < -50 or tout > 110:
error_4 = "Malfunction outdoor temp"
if tg <= 0 or tg >= 110:
error_5 = "Malfunction gt"
#clothing level prediction
if tout >= 75: #summer
icl = 0.5 # clo_typical_ensembles('Typical summer indoor clothing') # calculate total clothing insulation
if tout < 75 and tout >= 60: #spring
icl = 0.61 #clo_typical_ensembles('Trousers, long-sleeve sweatshirt')
if tout < 60 and tout >= 45: #fall
icl = 0.74 #clo_typical_ensembles('Sweat pants, long-sleeve sweatshirt')
if tout < 45: #winter
icl = 1.0 #clo_typical_ensembles('Typical winter indoor clothing')
#imput from user - recieved from GUI
activity = "moderately active" # "resting", "moderately active", "active"
if activity == "resting":
met = met_typical_tasks['Seated, quiet'] #1.0 met range
if activity == "moderately active":
met = 1.5 #walking around
if activity == "active":
met = 2 #light exercise
if tout < 50:
clo = .5
else:
clo = 0.36 #workout clothes
print(met)
#Held Constant for residencies
v = 0.1 # average air velocity, [m/s]
#MRT Calculation
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
#operative temp calulation
op = 0.5 * (tdb + tr)
#print(op)
# calculate the relative air velocity
vr = v_relative(v=v, met=met)
# calculate PMV in accordance with the ASHRAE 55 2017
results = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
def get_first_key(dictionary):
for key in dictionary:
return key
raise IndexError
key1 = get_first_key(results)
pmv = results[key1]
##################################################################
#finding setpoints with iterative loop
setpoint_options = np.array([
60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78,
79, 80
])
pmv_options = np.zeros(len(setpoint_options))
global setpoint_temp_prev
if occupancy: #someone is home Occupancy = True
if pmv >= -0.5 and pmv <= 0.5:
pmv_bool = 1 #comfort conditions met
print("Comfort Conditions met")
print(pmv)
setpoint_temp = setpoint_temp_prev #maintain the same temperature
else:
pmv_bool = 0 #comfrot conditons are not met
print("Comfort Conditions NOT met")
print(pmv)
#for loop to collect the mathematicaly PMV that would result from each setpoint option
for x in range(len(setpoint_options)):
results = pmv_ppd(tdb=setpoint_options[x],
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
key1 = get_first_key(results)
pmv_options[x] = results[key1]
pmv_minimum = 5 #nonsensical value to be replaced by the loop
pmv_minimum_index = 0 #returned to isolate the temperaure setpoint that goes with the minimum PMV
for x in range(len(pmv_options)):
if pmv_options[x] <= 0.5 and pmv_options[x] >= -0.5:
if pmv > 0:
if 0.5 - pmv_options[
x] < pmv_minimum: #want to have to change the pmv the least amount
pmv_minimum = 0.5 - pmv_options[x]
pmv_minimum_index = x
if pmv < 0:
if -0.5 + pmv_options[x] < pmv_minimum:
pmv_minimum = -0.5 + pmv_options[x]
pmv_minimum_index = x
setpoint_temp = setpoint_options[pmv_minimum_index]
print("PMV selected")
print(pmv_options[pmv_minimum_index])
print("setpoint selected:")
print(setpoint_temp)
else: #someone is not home Occupancy = False
print("No one home")
if pmv >= -1.0 and pmv <= 1.0:
pmv_bool = 1 #comfort conditions met
print("Comfort Conditions met")
print(pmv)
setpoint_temp = setpoint_temp_prev #maintain the same temperature
print(setpoint_temp_prev)
if pmv <= 0.75 and pmv >= -0.75:
print('but want to relax comfort conditons to save energy')
for x in range(len(setpoint_options)):
results = pmv_ppd(tdb=setpoint_options[x],
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
key1 = get_first_key(results)
pmv_options[x] = results[key1]
pmv_minimum = 5 #nonsensical value to be replaced by the loop
pmv_minimum_index = 0 #returned to isolate the temperaure setpoint that goes with the minimum PMV
for x in range(len(pmv_options)):
if pmv_options[x] <= 1.0 and pmv_options[x] >= -1.0:
if pmv > 0:
if 1 - pmv_options[x] < pmv_minimum:
pmv_minimum = 1 - pmv_options[x]
pmv_minimum_index = x
if pmv < 0:
if 1 + pmv_options[x] < pmv_minimum:
pmv_minimum = 1 + pmv_options[x]
pmv_minimum_index = x
setpoint_temp = setpoint_options[pmv_minimum_index]
print("PMV selected")
print(pmv_options[pmv_minimum_index])
print("setpoint selected:")
print(setpoint_temp)
else:
pmv_bool = 0 #comfrot conditons are not met
print("Comfort Conditions NOT met")
#for loop to collect the mathematicaly PMV that would result from each setpoint option
for x in range(len(setpoint_options)):
results = pmv_ppd(tdb=setpoint_options[x],
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
key1 = get_first_key(results)
pmv_options[x] = results[key1]
pmv_minimum = 5 #nonsensical value to be replaced by the loop
pmv_minimum_index = 0 #returned to isolate the temperaure setpoint that goes with the minimum PMV
for x in range(len(pmv_options)):
if pmv_options[x] <= 0.5 and pmv_options[x] >= -0.5:
if 0.5 - abs(pmv_options[x]) < pmv_minimum:
pmv_minimum = pmv_options[x]
pmv_minimum_index = x
setpoint_temp = setpoint_options[pmv_minimum_index]
print("PMV minimum")
print(pmv_minimum)
print("setpoint selected:")
print(setpoint_temp)
setpoint_temp_prev = setpoint_temp #saves the previous setpoint
#END###########################################
return pmv, setpoint_temp
met = met_typical_tasks['Seated, quiet']
if activity == "moderately active":
met = met_typical_tasks['Walking about']
if activity == "active":
met = met_typical_tasks['Calisthenics']
if tout < 50:
clo = .5
else:
clo = 0.36 #workout clothes
print(met)
#Held Constant for residencies
v = 0.1 # average air velocity, [m/s]
#MRT Calculation
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
#operative temp calulation
op = 0.5 * (tdb + tr)
#print(op)
# calculate the relative air velocity
vr = v_relative(v=v, met=met)
# calculate PMV in accordance with the ASHRAE 55 2017
results = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
def comfortAnalysis(tdb, tg, rh, tout, occupancy, desiredTemp, activity):
start_time = time.time()
#clothing level prediction
if tout >= 75: #summer
icl = 0.5 # clo_typical_ensembles('Typical summer indoor clothing') # calculate total clothing insulation
if tout < 75 and tout >= 60: #spring
icl = 0.61 #clo_typical_ensembles('Trousers, long-sleeve sweatshirt')
if tout < 60 and tout >= 45: #fall
icl = 0.74 #clo_typical_ensembles('Sweat pants, long-sleeve sweatshirt')
if tout < 45: #winter
icl = 1.0 #clo_typical_ensembles('Typical winter indoor clothing')
#imput from user - recieved from GUI
#activity = "moderately active" # "resting", "moderately active", "active"
if activity == 0: # resting
met = met_typical_tasks['Seated, quiet'] #1.0 met range
if activity == 1: # moderately active
met = 1.5 #walking around
if activity == 2: # active
met = 2 #light exercise
if tout < 50:
clo = .5
else:
clo = 0.36 #workout clothes
#print(" comfortAnalysis.py",met)
#Held Constant for residencies
v = 0.1 # average air velocity, [m/s]
#MRT Calculation
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
#print("tr:")
#print(tr)
#operative temp calulation
op = 0.5 * (tdb + tr)
#print(" comfortAnalysis.py",op)
# calculate the relative air velocity
vr = v_relative(v=v, met=met)
############################################################################
#check data for errors
#reset all error flags, needed all error variables in case more than one is true
error_1 = False
error_2 = False
error_3 = False
error_4 = False
error_5 = False
error_6 = False
error_7 = False
if tdb <= 0 or tdb >= 110: #test logic behind the measurements
#error_1 = "Malfunction indoor temp"
error_1 = True
if tdb <= 55 or tdb >= 90:
print(" comfortAnalysis.py",
"WARNING: Potential unsafe conditions")
error_2 = True
if rh < 0 or rh > 100:
print(" comfortAnalysis.py", "Malfunction RH sensor")
error_3 = True
if tout < -50 or tout > 110:
print(" comfortAnalysis.py", "Malfunction Outdoor Temp sensor")
error_4 = True
if tg <= 0 or tg >= 110:
print(" comfortAnalysis.py", "Malfunction GT Sensor")
error_5 = True
if tr <= 50 or tr >= 104:
print(
" comfortAnalysis.py",
"ERROR: Mean radiant temperature outside ASHRAE operating conditions"
)
error_6 = True
if tdb <= 50 or tdb >= 104:
print(" comfortAnalysis.py",
"ERROR: Indoor temperature outside ASHRAE operating conditions")
error_7 = True
if error_1 == False and error_2 == False and error_3 == False and error_4 == False and error_5 == False and error_6 == False and error_7 == False:
#only enter the ashrae calcs if error free
# calculate PMV in accordance with the ASHRAE 55 2017
results = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
key1 = get_first_key(
results
) #function from above to use python variable type dictionary
pmv = results[key1]
##################################################################
#finding setpoints with iterative loop
setpoint_options = np.arange(
60, 80, 0.1) # want to test setpoints between 60 and 80
pmv_options = np.zeros(
len(setpoint_options)
) #will run a hypothetical PMV calc with that temperature to select the best one
global setpoint_temp_prev #needs to be accessabile in all parts of the code
pmv_minimum_index = 0
if occupancy == False: #someone is home Occupancy = True
if pmv >= -0.5 and pmv <= 0.5:
pmv_bool = 1 #comfort conditions met
#print("Comfort Conditions are met")
#print(pmv)
#print(" comfortAnalysis.py","Comfort Conditions met")
#print(" comfortAnalysis.py",pmv)
setpoint_temp = setpoint_temp_prev #maintain the same temperature
# print(setpoint_temp)
pmv_options[pmv_minimum_index] = pmv
# print( pmv_options[pmv_minimum_index])
else:
pmv_bool = 0 #comfort conditons are not met
print("Comfort conditions NOT met")
#print(pmv)
#print(" comfortAnalysis.py","Comfort Conditions NOT met")
#print(" comfortAnalysis.py",pmv)
#for loop to iteratively test which PMV that would result from each setpoint option
for x in range(len(setpoint_options)):
results = pmv_ppd(tdb=setpoint_options[x],
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
key1 = get_first_key(results)
pmv_options[x] = results[key1]
for x in range(len(pmv_options)):
if pmv > 0.5: #throw out options too cold
if pmv_options[x] < 0:
pmv_options[x] = -100
else: #throw out options too warm
if pmv_options[x] > 0:
pmv_options[x] = 100
pmv_minimum = 5 #nonsensical value to be replaced by the loop
pmv_minimum_index = 0 #returned to isolate the temperaure setpoint that goes with the minimum PMV
for x in range(
len(pmv_options)
): #check each potential PMV to find the one that best meets the range -0.5 to 0.5
if pmv_options[x] <= 0.5 and pmv_options[x] >= -0.5:
if pmv > 0: # already know heat or cool must come on because it is outside the range so isolate which one and find the closest value to the edge of comfort range
if 0 + pmv_options[
x] < pmv_minimum: #want to have to change the pmv to the closest to 0.5 to save energy and still be comfortable
pmv_minimum = 0 - pmv_options[x]
pmv_minimum_index = x
if pmv < 0: # already know heat or cool must come on because it is outside the range so isolate which one and find the closest value to the edge of comfort range
if 0.5 + pmv_options[x] < pmv_minimum:
pmv_minimum = 0.5 + pmv_options[x]
pmv_minimum_index = x
setpoint_temp = round(setpoint_options[pmv_minimum_index], 1)
# print("Potential PMV selected")
# print(pmv_options[pmv_minimum_index])
# print("setpoint selected:")
# print(setpoint_temp)
#print(" comfortAnalysis.py","PMV selected")
#print(" comfortAnalysis.py",pmv_options[pmv_minimum_index])
#print(" comfortAnalysis.py","setpoint selected:")
#print(" comfortAnalysis.py",setpoint_temp)
else: #someone is not home Occupancy = True
# print("No one home")
#print(" comfortAnalysis.py","No one home")
if pmv >= -1.0 and pmv <= 1.0:
pmv_bool = 1 #comfort conditions met
# print("comfort conditions met")
# print(pmv)
#print(" comfortAnalysis.py","Comfort Conditions met")
#print(" comfortAnalysis.py",pmv)
setpoint_temp = setpoint_temp_prev #maintain the same temperature
#print(" comfortAnalysis.py",setpoint_temp_prev)
if pmv <= 0.75 and pmv >= 0: #pmv is warm but could be relaxed to be warmer
#print(" comfortAnalysis.py",'but want to relax comfort conditons to save energy')
for x in range(len(setpoint_options)
): #get potential PMV for setpoint options
if setpoint_options[
x] >= tout or tout > 80: #need to make sure heat does not turn on past conditions outside
results = pmv_ppd(tdb=setpoint_options[x],
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
key1 = get_first_key(results)
pmv_options[x] = results[key1]
else:
pmv_options[
x] = 100 #value will never be picked essentially removing those options
pmv_minimum = 5 #nonsensical value to be replaced by the loop
pmv_minimum_index = 0 #returned to isolate the temperaure setpoint that goes with the minimum PMV
for x in range(
len(pmv_options)
): #for loop to collect iteratively the PMV that would result from each setpoint option
if pmv_options[
x] <= 1.0: #only compare the PMV possibilities within the range we want
if 1 - pmv_options[x] < pmv_minimum:
pmv_minimum = 1 - pmv_options[x]
pmv_minimum_index = x
setpoint_temp = round(setpoint_options[pmv_minimum_index],
1)
# print("PMV selected")
# print(pmv_options[pmv_minimum_index])
# print("setpoint selected:")
# print(setpoint_temp)
#print(" comfortAnalysis.py","PMV selected")
#print(" comfortAnalysis.py",pmv_options[pmv_minimum_index])
#print(" comfortAnalysis.py","setpoint selected:")
#print(" comfortAnalysis.py",setpoint_temp)
if pmv >= -0.75 and pmv <= 0: ##pmv is cold but could be relaxed to be cooler
#print(" comfortAnalysis.py",'but want to relax comfort conditons to save energy')
for x in range(len(setpoint_options)
): #get potential PMV for setpoint options
if setpoint_options[
x] >= tout or tout < 60: #need to make sure cooling does not turn on if the temperature is relaxed
results = pmv_ppd(tdb=setpoint_options[x],
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
key1 = get_first_key(results)
pmv_options[x] = results[key1]
else:
pmv_options[
x] = 100 #value will never be picked essentially removing those options (next if statement will skip all these)
pmv_minimum = 5 #nonsensical value to be replaced by the loop
pmv_minimum_index = 0 #returned to isolate the temperaure setpoint that goes with the minimum PMV
for x in range(len(pmv_options)):
if pmv_options[
x] >= -1.0: # make sure the potential PMV is within the range
if 1 + pmv_options[
x] < pmv_minimum: #pmv will be a negative number so adding to find the minimum distance from edge of range
pmv_minimum = 1 + pmv_options[x]
pmv_minimum_index = x
setpoint_temp = round(setpoint_options[pmv_minimum_index],
1)
# print("PMV selected")
# print(pmv_options[pmv_minimum_index])
# print("setpoint selected:")
##print(setpoint_temp)
#print(" comfortAnalysis.py","PMV selected")
#print(" comfortAnalysis.py",pmv_options[pmv_minimum_index])
#print(" comfortAnalysis.py","setpoint selected:")
#print(" comfortAnalysis.py",setpoint_temp)
else:
pmv_bool = 0 #comfort conditons are not met in case no one is home
#print(" comfortAnalysis.py","Comfort Conditions NOT met")
if pmv >= 0: #pmv is too warm
#print(" comfortAnalysis.py",'but want to relax comfort conditons to save energy')
for x in range(len(setpoint_options)
): #get potential PMV for setpoint options
if setpoint_options[
x] >= tout or tout: #need to make sure heat does not turn on past conditions outside
results = pmv_ppd(tdb=setpoint_options[x],
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
key1 = get_first_key(results)
pmv_options[x] = results[key1]
else:
pmv_options[
x] = 100 #value will never be picked essentially removing those options
pmv_minimum = 5 #nonsensical value to be replaced by the loop
pmv_minimum_index = 0 #returned to isolate the temperaure setpoint that goes with the minimum PMV
for x in range(
len(pmv_options)
): #for loop to collect iteratively the PMV that would result from each setpoint option
if pmv_options[
x] <= 1.0: #only compare the PMV possibilities within the range we want
if 1 - pmv_options[x] < pmv_minimum:
pmv_minimum = 1 - pmv_options[x]
pmv_minimum_index = x
setpoint_temp = round(setpoint_options[pmv_minimum_index],
1)
#print("PMV selected")
# print(pmv_options[pmv_minimum_index])
#print("setpoint selected:")
#print(setpoint_temp)
#print(" comfortAnalysis.py","PMV selected")
#print(" comfortAnalysis.py",pmv_options[pmv_minimum_index])
#print(" comfortAnalysis.py","setpoint selected:")
#print(" comfortAnalysis.py",setpoint_temp)
if pmv <= 0: ##pmv is too cold
#print(" comfortAnalysis.py",'but want to relax comfort conditons to save energy')
for x in range(len(setpoint_options)
): #get potential PMV for setpoint options
if setpoint_options[
x] >= tout or tout > 60: #need to make sure heat does not turn on if the temperature is relaxed
results = pmv_ppd(tdb=setpoint_options[x],
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
key1 = get_first_key(results)
pmv_options[x] = results[key1]
else:
pmv_options[
x] = 100 #value will never be picked essentially removing those options (next if statement will skip all these)
pmv_minimum = 5 #nonsensical value to be replaced by the loop
pmv_minimum_index = 0 #returned to isolate the temperaure setpoint that goes with the minimum PMV
for x in range(len(pmv_options)):
if pmv_options[
x] >= -1.0: # make sure the potential PMV is within the range
if 1 + pmv_options[
x] < pmv_minimum: #pmv will be a negative number so adding to find the minimum distance from edge of range
pmv_minimum = 1 + pmv_options[x]
pmv_minimum_index = x
setpoint_temp = round(setpoint_options[pmv_minimum_index],
1)
# print("PMV selected")
# print(pmv_options[pmv_minimum_index])
# print("setpoint selected:")
# print(setpoint_temp)
#print(" comfortAnalysis.py","PMV selected")
#print(" comfortAnalysis.py",pmv_options[pmv_minimum_index])
#print(" comfortAnalysis.py","setpoint selected:")
#print(" comfortAnalysis.py",setpoint_temp)
else:
#print(" comfortAnalysis.py",'Error Detected. Operating to entered Desired Temp')
setpoint_temp = setpoint_temp_prev
pmv_bool = False
pmv = 0.00
pmv_options = [5]
pmv_minimum_index = 0
pmv_options[pmv_minimum_index] = 5
setpoint_temp_prev = setpoint_temp #saves the previous setpoint
#print(" comfortAnalysis.py",pmv, setpoint_temp, pmv_options[pmv_minimum_index])
#print(" comfortAnalysis.py","ComfortAnalysis Code took", time.time() - start_time, "to run")
#return (pmv,pmv_bool,setpoint_temp, error_1, error_2, error_3, error_4, error_5, error_6, error_7)
return (pmv, setpoint_temp, pmv_options[pmv_minimum_index])
def comfortAnalysis(count, tdb, tg, rh, tout, occupancy):
#START###########################################
#TODO remove this
tout_saved = np.random.random_sample(
(1440 * 30 +
5 * 1440), ) + 60 #Only for testing that the indexing is done right
count = 1440 * 30 + 1440
#tout_saved = np.zeros(1440*365)
#count = 1440*30 + 1440
#setpoint_temp = 67
############################################################################################
if count > 365 * 1440: #keep the number of saved data points from being too large - can adjust if 1 year is too many
tout_reset = np.zeros(1440 * 365)
tout_reset[0:1440 * 30 - 1] = tout_saved[365 * 1440 - 30 * 1440:365 *
1440]
tout_saved = tout_reset
count = 1440 * 30 + 1 #starts at zero, count number represents the number of run about to happen adn sensor data just received
#sensor data here to be removed once the data is read in
tdb = 75 # dry-bulb air temperature, [$^{\circ}$C]
tg = 76 #globe temperature
rh = 50 # relative humidity, [%]
tout = 40 #outdoor temperature
occupancy = 1 #occupancy
#formula to convert to Fahrenheit from Celsius
#F = (Cx1.8) + 32
#reset all error flags, needed all error variables in case more than one is true
error_1 = ""
error_2 = ""
error_3 = ""
error_4 = ""
error_5 = ""
if tdb <= 0 | tdb >= 110: #test logic behind the measurements
error_1 = "Malfunction indoor temp"
if tdb <= 55 | tdb >= 90:
error_2 = "Potential unsafe conditions"
if rh < 0 | rh > 100:
error_3 = "Malfunction rh"
if tout < -50 | tout > 110:
error_4 = "Malfunction outdoor temp"
if tg <= 0 | tg >= 110:
error_5 = "Malfunction gt"
#clothing level prediction
if tout >= 75: #summer
icl = 0.5 # clo_typical_ensembles('Typical summer indoor clothing') # calculate total clothing insulation
if tout < 75 & tout >= 60: #spring
icl = 0.61 #clo_typical_ensembles('Trousers, long-sleeve sweatshirt')
if tout < 60 & tout >= 45: #fall
icl = 0.74 #clo_typical_ensembles('Sweat pants, long-sleeve sweatshirt')
if tout < 45: #winter
icl = 1.0 #clo_typical_ensembles('Typical winter indoor clothing')
#imput from user - recieved from GUI
activity = "moderately active" # "resting", "moderately active", "active"
if activity == "resting":
met = met_typical_tasks['Seated, quiet']
if activity == "moderately active":
met = met_typical_tasks['Walking about']
if activity == "active":
met = met_typical_tasks['Calisthenics']
if tout < 50:
clo = .5
else:
clo = 0.36 #workout clothes
print(met)
#Held Constant for residencies
v = 0.1 # average air velocity, [m/s]
#MRT Calculation
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
#operative temp calulation
op = 0.5 * (tdb + tr)
#print(op)
# calculate the relative air velocity
vr = v_relative(v=v, met=met)
# calculate PMV in accordance with the ASHRAE 55 2017
results = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
def get_first_key(dictionary):
for key in dictionary:
return key
raise IndexError
key1 = get_first_key(results)
pmv = results[key1]
#print(pmv)
# print PMV value- this whole group can be eventually deleted
print(f"pmv={results['pmv']}, ppd={results['ppd']}%")
if pmv <= float(0.5) and pmv >= float(-0.5):
print('Acceptable!')
if pmv >= 0.5:
print('Too warm!Turn on fan')
if pmv <= -0.5:
print('Too cold! Turn on heater! or Grab Blanket')
##################################################################
#finding the setpoints
num_runs = count #get this value from pipes
print("Count:", count)
if num_runs <= 1440 * 30: #for when less than a months worth of data is collected
t_running_mean = 45 #average temperature in March in Lexington, KY
tout_saved[num_runs] = tout
else:
t_running_mean = sum(
tout_saved[(num_runs - 30 * 1440):num_runs]) / (30 * 1440)
print("Running mean:", t_running_mean)
setpoints = adaptive_ashrae(tdb, tr, t_running_mean, v, units="IP")
#print(setpoints)
print(
f"low={setpoints['tmp_cmf_90_low']}, high={setpoints['tmp_cmf_90_up']}%"
)
if pmv < -0.5 and pmv > 0.5: #check if outside the comfortable range
if occupancy == 1:
#checking if the change fixed it summer
PMV_bool = 0
setpoint_low = setpoints['tmp_cmf_90_low']
tg = tdb + 2 #can adjust this guess based on how the gt in the fridge changes with the heating or cooling
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
results_low = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
print('Using low setpoint')
print(f"pmv={results['pmv']}, ppd={results['ppd']}%")
setpoint_high = setpoints['tmp_cmf_90_up']
tg = setpoint_high + 2 #can adjust based on how the gt in the fridge changes with the heating or cooling
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
results_high = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
print('Using high setpoint')
print(f"pmv={results['pmv']}, ppd={results['ppd']}%")
tdb = (setpoints['tmp_cmf_90_up'] +
setpoints['tmp_cmf_90_low']) / 2
tg = tdb + 2 #can adjust based on how the gt in the fridge changes with the heating or cooling
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
print('mid setpoint')
print(tdb)
results_mid = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
print('Using mid setpoint')
print(f"pmv={results['pmv']}, ppd={results['ppd']}%")
#want the set point closest to the middle neutral temperature for optimal comfort
set_point_PMV = min(
abs(0 - results_low), abs(0 - results_high),
abs(0 - results_mid)) #this setpoint will be sent to Joseph
#select the temperature that corresponds to the PMV setpoint
if set_point_PMV == results_high:
set_point_temp = setpoints['tmp_cmf_80_up']
if set_point_PMV == results_mid:
set_point_temp = (setpoints['tmp_cmf_80_up'] +
setpoints['tmp_cmf_80_low']) / 2
if set_point_PMV == results_low:
set_point_temp = setpoints['tmp_cmf_80_low']
if occupancy == 0: #relaxed comfort parameters
#checking if the change fixed it summer
if pmv > 1 or pmv < -1:
PMV_bool = 0
setpoint_low = setpoints['tmp_cmf_80_low']
tg = tdb + 2 #can adjust based on how the gt in the fridge changes with the heating or cooling
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
results_low = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
print('Using low setpoint')
print(f"pmv={results['pmv']}, ppd={results['ppd']}%")
setpoint_high = setpoints['tmp_cmf_80_up']
tg = setpoint_high + 2 #can adjust based on how the gt in the fridge changes with the heating or cooling
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
results_high = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
print('Using high setpoint')
print(f"pmv={results['pmv']}, ppd={results['ppd']}%")
tdb = (setpoints['tmp_cmf_80_up'] +
setpoints['tmp_cmf_80_low']) / 2
tg = tdb + 2 #can adjust based on how the gt in the fridge changes with the heating or cooling
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
print('mid setpoint')
print(tdb)
results_mid = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
print('Using mid setpoint')
print(f"pmv={results['pmv']}, ppd={results['ppd']}%")
if results_high < -1 or results_high > 1:
results_high = 0 #really low compared to the other options and wont be selected as it is an infeasible option
if results_low < -1 or results_low > 1:
result_low = 0
if results_mid < -1 or results_mid > 1:
results_mid = 0
#comparing the setpoints to select the one closest to the edge of the comfortable window to lead to maximum savings in energy costs
set_point_PMV = max(
abs(0 - results_low), abs(0 - results_high),
abs(0 -
results_mid)) #this setpoint will be sent to Joseph
#select the temperature that corresponds to the PMV setpoint
if set_point_PMV == results_high:
set_point_temp = setpoints['tmp_cmf_80_up']
if set_point_PMV == results_mid:
set_point_temp = (setpoints['tmp_cmf_80_up'] +
setpoints['tmp_cmf_80_low']) / 2
if set_point_PMV == results_low:
set_point_temp = setpoints['tmp_cmf_80_low']
else:
PMV_bool = 1
setpoint_temp = setpoint_temp
else:
PMV_bool = 1
if occupancy == 1:
setpoint_temp = setpoint_temp # keep the same setpoint if it is in the comfortable range no need to cause the hvac to turn on if it is comfortable
else: #allow the comfort conditions to relax to with the (-1 to 1 range)
setpoint_low = setpoints['tmp_cmf_80_low']
tg = tdb + 2 #can adjust based on how the gt in the fridge changes with the heating or cooling
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
results_low = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
print('Using low setpoint')
print(f"pmv={results['pmv']}, ppd={results['ppd']}%")
setpoint_high = setpoints['tmp_cmf_80_up']
tg = setpoint_high + 2 #can adjust based on how the gt in the fridge changes with the heating or cooling
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
results_high = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
print('Using high setpoint')
print(f"pmv={results['pmv']}, ppd={results['ppd']}%")
tdb = (setpoints['tmp_cmf_80_up'] +
setpoints['tmp_cmf_80_low']) / 2
tg = tdb + 2 #can adjust based on how the gt in the fridge changes with the heating or cooling
tr = t_mrt(tg, tdb, v, d=0.075, emissivity=0.95)
print('mid setpoint')
print(tdb)
results_mid = pmv_ppd(tdb=tdb,
tr=tr,
vr=vr,
rh=rh,
met=met,
clo=icl,
standard="ASHRAE",
units="IP")
print('Using mid setpoint')
print(f"pmv={results['pmv']}, ppd={results['ppd']}%")
if results_high < -1 or results_high > 1:
results_high = 0 #really low compared to the other options and wont be selected as it is an infeasible option
if results_low < -1 or results_low > 1:
result_low = 0
if results_mid < -1 or results_mid > 1:
results_mid = 0
#comparing the setpoints to select the one closest to the edge of the comfortable window to lead to maximum savings in energy costs
set_point_PMV = max(
abs(0 - results_low), abs(0 - results_high),
abs(0 - results_mid)) #this setpoint will be sent to Joseph
#select the temperature that corresponds to the PMV setpoint
if set_point_PMV == results_high:
set_point_temp = setpoints['tmp_cmf_80_up']
if set_point_PMV == results_mid:
set_point_temp = (setpoints['tmp_cmf_80_up'] +
setpoints['tmp_cmf_80_low']) / 2
if set_point_PMV == results_low:
set_point_temp = setpoints['tmp_cmf_80_low']
count = count + 1
#END###########################################
return pmv, setpoint_temp