def test_adaptive_ashrae():
data_test_adaptive_ashrae = [ # I have commented the lines of code that don't pass the test
{'tdb': 19.6, 'tr': 19.6, 't_running_mean': 17, 'v': 0.1, 'return': {'acceptability_80': True}},
{'tdb': 19.6, 'tr': 19.6, 't_running_mean': 17, 'v': 0.1, 'return': {'acceptability_90': False}},
{'tdb': 19.6, 'tr': 19.6, 't_running_mean': 25, 'v': 0.1, 'return': {'acceptability_80': False}},
{'tdb': 19.6, 'tr': 19.6, 't_running_mean': 25, 'v': 0.1, 'return': {'acceptability_80': False}},
{'tdb': 26, 'tr': 26, 't_running_mean': 16, 'v': 0.1, 'return': {'acceptability_80': True}},
{'tdb': 26, 'tr': 26, 't_running_mean': 16, 'v': 0.1, 'return': {'acceptability_90': False}},
{'tdb': 30, 'tr': 26, 't_running_mean': 16, 'v': 0.1, 'return': {'acceptability_80': False}},
{'tdb': 25, 'tr': 25, 't_running_mean': 23, 'v': 0.1, 'return': {'acceptability_80': True}},
{'tdb': 25, 'tr': 25, 't_running_mean': 23, 'v': 0.1, 'return': {'acceptability_90': True}},
]
for row in data_test_adaptive_ashrae:
assert (adaptive_ashrae(row['tdb'], row['tr'], row['t_running_mean'], row['v'])[list(row['return'].keys())[0]]) == row['return'][list(row['return'].keys())[0]]
assert (adaptive_ashrae(77, 77, 68, 0.3, units='ip')['tmp_cmf']) == 75.2
with pytest.raises(ValueError):
adaptive_ashrae(20, 20, 9, 0.1)
with pytest.raises(ValueError):
adaptive_ashrae(20, 20, 34, 0.1)
from pythermalcomfort.models import adaptive_ashrae
from pythermalcomfort.psychrometrics import running_mean_outdoor_temperature
from pprint import pprint
result = adaptive_ashrae(tdb=25, tr=25, t_running_mean=23, v=0.3)
pprint(result)
print(result['acceptability_80'])
result = adaptive_ashrae(tdb=77, tr=77, t_running_mean=73.5, v=1, units='IP')
pprint(result)
print(result['acceptability_80'])
rmt_value = running_mean_outdoor_temperature([29, 28, 30, 29, 28, 30, 27],
alpha=0.9)
result = adaptive_ashrae(tdb=25, tr=25, t_running_mean=rmt_value, v=0.3)
pprint(result)
def test_adaptive_ashrae():
data_test_adaptive_ashrae = (
[ # I have commented the lines of code that don't pass the test
{
"tdb": 19.6,
"tr": 19.6,
"t_running_mean": 17,
"v": 0.1,
"return": {
"acceptability_80": True
},
},
{
"tdb": 19.6,
"tr": 19.6,
"t_running_mean": 17,
"v": 0.1,
"return": {
"acceptability_90": False
},
},
{
"tdb": 19.6,
"tr": 19.6,
"t_running_mean": 25,
"v": 0.1,
"return": {
"acceptability_80": False
},
},
{
"tdb": 19.6,
"tr": 19.6,
"t_running_mean": 25,
"v": 0.1,
"return": {
"acceptability_80": False
},
},
{
"tdb": 26,
"tr": 26,
"t_running_mean": 16,
"v": 0.1,
"return": {
"acceptability_80": True
},
},
{
"tdb": 26,
"tr": 26,
"t_running_mean": 16,
"v": 0.1,
"return": {
"acceptability_90": False
},
},
{
"tdb": 30,
"tr": 26,
"t_running_mean": 16,
"v": 0.1,
"return": {
"acceptability_80": False
},
},
{
"tdb": 25,
"tr": 25,
"t_running_mean": 23,
"v": 0.1,
"return": {
"acceptability_80": True
},
},
{
"tdb": 25,
"tr": 25,
"t_running_mean": 23,
"v": 0.1,
"return": {
"acceptability_90": True
},
},
])
for row in data_test_adaptive_ashrae:
assert (adaptive_ashrae(
row["tdb"], row["tr"], row["t_running_mean"],
row["v"])[list(row["return"].keys())[0]]) == row["return"][list(
row["return"].keys())[0]]
assert (adaptive_ashrae(77, 77, 68, 0.3, units="ip")["tmp_cmf"]) == 75.2
with pytest.raises(ValueError):
adaptive_ashrae(20, 20, 9, 0.1)
with pytest.raises(ValueError):
adaptive_ashrae(20, 20, 34, 0.1)
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
t_running_mean = 68 ### this will need to be calculated based on the past couple of hours
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 & pmv > 0.5:
if occupancy == 1:
#checking if the change fixed it summer
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,
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
def PythonFunction():
#model performance
deltaT = 0.5
orientation = 0 #0 for vertical panel, 1 for horizontal
#Variables for multiple modes of heat transfer
panel_height = 18 #[m]
panel_width = 10 #[m]
insulation_thicknes = 0.15 #[m]
A_cs = panel_height * panel_width #area of cold panel [m]
P_cs = (panel_height * 2) + (panel_width * 2) #perimeter of cold panel [m]
#Variables for convective heat transfer
S = 0.2 #characteristic length (distance between cold panel and film [m])
E_window = 0.02 #emissivity of windows
Cp = 4.184 #[KJ/Kg]
T_air1 = TRNSYS.getInputValue(1) #air temperature surrounding the panel
RH1 = TRNSYS.getInputValue(2) #relative humidity %
DeHumid_Sched = TRNSYS.getInputValue(3) #wind speed
T_dewpoint1 = TRNSYS.getInputValue(4)
T_wallN = TRNSYS.getInputValue(5)
T_wallS = TRNSYS.getInputValue(6)
T_wallE = TRNSYS.getInputValue(7)
T_wallW = TRNSYS.getInputValue(8)
T_wallF = TRNSYS.getInputValue(9)
T_wallR = TRNSYS.getInputValue(10)
T_inlet1 = TRNSYS.getInputValue(11)
hot_side_flow_rate = TRNSYS.getInputValue(12)
mass_flow_rate = (hot_side_flow_rate / (3600)) #[Kg/sec]
Day_of_year = int(TRNSYS.getInputValue(13))
Occ_Schedual_input = TRNSYS.getInputValue(14)
Heatpump_temp_signal = TRNSYS.getInputValue(15)
wind = 0.5
err_inc = 1.0
h_int_err = err_inc * 0.981474747
h_ext_err = err_inc * 1.082080808
em_cs_err = err_inc
Trans_error = err_inc * 0.864969697
T_cs_err = err_inc
T_ss_err = err_inc * 1.013050505
T_air_err = err_inc
B_err = err_inc
u_err = err_inc
p_err = err_inc
a_err = err_inc
k_err_int = err_inc
k_err_ext = err_inc * 1.039676768
wind_err = err_inc
n_err = 0.705393939
# =============================================================================
# err_inc = 1.0
# h_int_err = err_inc
# h_ext_err = err_inc
# em_cs_err = err_inc
# Trans_error = err_inc
# #T_cs_err = err_inc
# T_ss_err = err_inc
# T_air_err = err_inc
# B_err = err_inc
# u_err = err_inc
# p_err = err_inc
# a_err = err_inc
# k_err_int = err_inc
# k_err_ext = err_inc
# #wind_err = err_inc
# =============================================================================
FTIR_out = FTIR()
trans = FTIR_out[0]
reflect = FTIR_out[1]
wave_len2 = FTIR_out[2]
trans = [(x * Trans_error) for x in trans]
absorb = list(range(0, len(trans)))
absorb = [(1 - trans[x] - reflect[x]) for x in absorb]
T_air = (T_air1 *
T_air_err) + 273.15 #air temperature surrounding the panel
E_cs = 0.95 * em_cs_err #emissivity of cold surface
RH = RH1 / 100 #relative humidity to decimal
#=================Step 0: End ============================================
average_wall_temp = (T_wallN + T_wallS + T_wallE + T_wallW + T_wallF +
T_wallR) / 6
path = r"D:\Masters related\Python Code\Daily_Temps\Singapore_ave_daily_temp.csv"
file = open(path, newline='')
reader = csv.reader(file)
Ave_Temp = []
header = next(reader)
for row in reader:
temp = float(row[0])
Ave_Temp.append(temp)
Trm = (Ave_Temp[Day_of_year - 2] + Ave_Temp[Day_of_year - 3] +
Ave_Temp[Day_of_year - 4] + Ave_Temp[Day_of_year - 5] +
Ave_Temp[Day_of_year - 6] + Ave_Temp[Day_of_year - 7] +
Ave_Temp[Day_of_year -
8]) / 7 #running mean outdoor temp for adaptive model
if Occ_Schedual_input == 1:
x = 1
#=====================Step 3: Calculate View Factors between panels and human============================================
P1_Panel_toHumans_F = [0.00088, 0.00088, 0.00088, 0.00088, 0.0027]
total_Panel_toHumans_F = 3 * sum(
P1_Panel_toHumans_F) * Occ_Schedual_input
ave_panel_toHumans = (
total_Panel_toHumans_F /
(3 * len(P1_Panel_toHumans_F))) * Occ_Schedual_input
ave_human_toPanels_F = ((ave_panel_toHumans * A_cs) / 1.551)
#===========================Step 3: End ======================================================================
#=================Step 1: Calculate MRT as seen by Panel ============================================
#with people
T_wall1 = Panel_MRT(T_wallF, T_wallN, T_wallS, T_wallE, T_wallW,
E_window, total_Panel_toHumans_F) #MRT in room
P1_T_wall = (T_wall1 * T_ss_err) + 273.15 #MRT in room
#without people
T_wall1_no_H = Panel_MRT(T_wallF, T_wallN, T_wallS, T_wallE, T_wallW,
E_window, 0) #MRT in room
P1_T_wall_no_H = (T_wall1_no_H * T_ss_err) + 273.15 #MRT in room
#=================Step 1: End ======================================================================
#u_val = (3*(insulation_thicknes**2))-(1.82*insulation_thicknes)+0.34 #[W/m^2-k]
Panel1_model1 = film_temp_and_Q(
orientation, panel_height, panel_width, S, A_cs, P_cs, deltaT,
10 + 273.15, P1_T_wall, T_air, RH, E_cs, wind, trans, reflect,
absorb, wave_len2, h_int_err, h_ext_err, B_err, u_err, p_err,
a_err, k_err_ext, k_err_int, n_err)
#Panel1_model1_Qdot_throughins = (u_val*A_cs*(T_wallR-15))/1000
Panel1_model1_Q = (Panel1_model1[0] + Panel1_model1[4]) / 1000
t1_in = 10 - (Panel1_model1_Q / (2 * mass_flow_rate * Cp))
Panel1_model2 = film_temp_and_Q(
orientation, panel_height, panel_width, S, A_cs, P_cs, deltaT,
5 + 273.15, P1_T_wall, T_air, RH, E_cs, wind, trans, reflect,
absorb, wave_len2, h_int_err, h_ext_err, B_err, u_err, p_err,
a_err, k_err_ext, k_err_int, n_err)
#Panel1_model2_Qdot_throughins = (u_val*A_cs*(T_wallR-10))/1000
Panel1_model2_Q = (Panel1_model2[0] + Panel1_model2[4]) / 1000
t2_in = 5 - (Panel1_model2_Q / (2 * mass_flow_rate * Cp))
P1_T_inlet_t_cs_equ = linear_regression(t1_in, 10, t2_in, 5)
#=================Step 2:Calculate Heat Transfer between Panel and Empty Room ============================================
P1_T_cs1 = (P1_T_inlet_t_cs_equ[0] * T_inlet1) + P1_T_inlet_t_cs_equ[1]
P1_T_cs = P1_T_cs1 + 273.15
FTaQ_results = film_temp_and_Q(
orientation, panel_height, panel_width, S, A_cs, P_cs, deltaT,
P1_T_cs, P1_T_wall_no_H, T_air, RH, E_cs, wind, trans, reflect,
absorb, wave_len2, h_int_err, h_ext_err, B_err, u_err, p_err,
a_err, k_err_ext, k_err_int, n_err)
P1_Q_Panel_empty_room1 = FTaQ_results[0]
P1_Q_film_room = FTaQ_results[4]
P1_Q_room_gain = P1_Q_Panel_empty_room1 * (1 - total_Panel_toHumans_F)
#=================Step 2: End ======================================================================
#=================Step 4:Calculate Heat Transfer between Panel and Human ==================================
P1_Q_PanelHuman_results = film_temp_and_Q(
orientation, panel_height, panel_width, S, A_cs, P_cs, deltaT,
P1_T_cs, P1_T_wall, T_air, RH, E_cs, wind, trans, reflect, absorb,
wave_len2, h_int_err, h_ext_err, B_err, u_err, p_err, a_err,
k_err_ext, k_err_int, n_err)
P1_Q_PanelOut = P1_Q_PanelHuman_results[2]
P1_T_film1 = FTaQ_results[1] - 273.15
#=================Step 4: End ======================================================================
#=================Step 5 & 6: Calculate MRT as seen by human and pmv============================================
H_MRT = list(range(5))
adapt_result_low = list(range(5))
adapt_result_up = list(range(5))
#adapt_result_temp = list(range(5))
panel_MRT_temp = list(range(5))
Q_human_panel = list(range(5))
for x in H_MRT:
H_MRT_results = MRT_Human(P1_Panel_toHumans_F[x], P1_Q_PanelOut,
T_wallN, T_wallS, T_wallE, T_wallW,
T_wallF, T_wallR, A_cs, E_window)
H_MRT[x] = H_MRT_results[0]
panel_MRT_temp[x] = H_MRT_results[2]
adapt_resulttemp = adaptive_ashrae(T_air1, H_MRT[x], Trm, wind)
adapt_result_low[x] = adapt_resulttemp['tmp_cmf_90_low']
adapt_result_up[x] = adapt_resulttemp['tmp_cmf_90_up']
#adapt_result_temp[x] = adapt_resulttemp['tmp_cmf']
Q_human_panel[x] = H_MRT_results[3] * P1_Panel_toHumans_F[x]
ADAPT_MIN = max(adapt_result_low)
ADAPT_MAX = min(adapt_result_up)
#ADAPT_temp_goal = sum(adapt_result_temp)/len(adapt_result_temp)
max_HUMAN_MRT = max(H_MRT)
AVE_HUMAN_MRT = sum(H_MRT) / len(H_MRT)
Panel_MRT_Temp = sum(panel_MRT_temp) / len(panel_MRT_temp)
AVE_Q_human_panel = sum(Q_human_panel) / len(Q_human_panel)
operative_temp = t_o(T_air1, max_HUMAN_MRT, wind)
#=================Step 5 & 6: End ======================================================================
#=================Step 7:Update cold surface temperature============================================
P1_T_outlet1 = P1_T_cs1 + (P1_T_cs1 - T_inlet1)
T_outlet1_ave = P1_T_outlet1
T_film1_low = P1_T_film1
water_heat_gain_rate = 1000 * mass_flow_rate * Cp * (P1_T_outlet1 -
T_inlet1)
rad_control = 1
heatpump_divert_control = 0.5
panelflow_cont_var = (P1_T_outlet1 - (T_inlet1 + 0.5)) * 100
# =============================================================================
# if T_dewpoint1 <15:
# T_dewpoint1 = 15
# =============================================================================
T_cs_results = CS_temp(orientation, panel_height, panel_width, S, A_cs,
P_cs, deltaT, T_dewpoint1 + 273.15, P1_T_wall,
T_air, RH, E_cs, wind, trans, reflect, absorb,
wave_len2, h_int_err, h_ext_err, B_err, u_err,
p_err, a_err, k_err_ext, k_err_int, n_err)
Panel_inlet_temp_set = (T_cs_results[1] + 2) - 273.15
#=================Step 7:End============================================
# =============================================================================
# ##Leave for Debugging
# rad_control = 0
# operative_temp = 20
# AVE_HUMAN_MRT = 30
# P1_Q_room_gain = 0
# panelflow_cont_var = 0
# P1_Q_film_room = 0
# ADAPT_MIN = 0
# P1_T_outlet1 = T_inlet1
# P2_T_outlet1 = T_inlet1
# T_outlet1_ave = T_inlet1
# P1_T_cs1 = T_inlet1
# T_film1_low = 0
# ave_human_toPanels_F = 0
# controlled_variable = 0
# heatpump_divert_control = 0.5
# Panel_MRT_Temp = 2532
# Panel_inlet_temp_set = 25
# ADAPT_MAX = 1
# PMV_MIN = 1
# PMV_MAX = 1
# =============================================================================
if Occ_Schedual_input == 0:
ADAPT_MIN = 0
ADAPT_MAX = 0
AVE_HUMAN_MRT = 0
rad_control = 0
P1_Q_room_gain = 0
P1_Q_film_room = 0
P1_T_outlet1 = T_inlet1
T_outlet1_ave = T_inlet1
P1_T_cs1 = T_inlet1
T_film1_low = 0
ave_human_toPanels_F = 0
panelflow_cont_var = (P1_T_outlet1 - (T_inlet1 + 0.5)) * 100
heatpump_divert_control = 1
Panel_inlet_temp_set = 20
Panel_MRT_Temp = 0
operative_temp = 0
water_heat_gain_rate = 0
AVE_Q_human_panel = 0
##########
# =============================================================================
# P1_Q_room_gain = 0
# P1_Q_film_room = 0
# =============================================================================
##########
ignore = 0
TRNSYS.setOutputValue(1, P1_Q_room_gain)
TRNSYS.setOutputValue(2, P1_T_outlet1)
TRNSYS.setOutputValue(3, rad_control)
TRNSYS.setOutputValue(4, wind)
TRNSYS.setOutputValue(5, T_inlet1)
TRNSYS.setOutputValue(6, P1_T_cs1)
TRNSYS.setOutputValue(7, operative_temp)
TRNSYS.setOutputValue(8, T_film1_low)
TRNSYS.setOutputValue(9, T_dewpoint1)
TRNSYS.setOutputValue(10, P1_Q_film_room)
TRNSYS.setOutputValue(11, Trm)
TRNSYS.setOutputValue(12, ave_human_toPanels_F)
TRNSYS.setOutputValue(13, panelflow_cont_var)
TRNSYS.setOutputValue(14, AVE_Q_human_panel)
TRNSYS.setOutputValue(15, ignore)
TRNSYS.setOutputValue(16, ignore)
TRNSYS.setOutputValue(17, ADAPT_MIN)
TRNSYS.setOutputValue(18, ADAPT_MAX)
TRNSYS.setOutputValue(19, average_wall_temp)
TRNSYS.setOutputValue(20, water_heat_gain_rate)
TRNSYS.setOutputValue(21, heatpump_divert_control)
TRNSYS.setOutputValue(22, Panel_MRT_Temp)
TRNSYS.setOutputValue(23, Panel_inlet_temp_set)
TRNSYS.setOutputValue(24, AVE_HUMAN_MRT)
# =============================================================================
# Not official
# P1_Panel_toHumans_F = [0.00088, 0.00088, 0.00088, 0.00088, 0.0028]
# total_Panel_toHumans_F = sum(P1_Panel_toHumans_F)*Occ_Schedual_input
# ave_panel_toHumans = (total_Panel_toHumans_F/len(P1_Panel_toHumans_F))*Occ_Schedual_input
# ave_human_toPanels_F = ((ave_panel_toHumans*A_cs)/1.551)
#
#
#
#
#
# if DeHumid_Sched == 1:
# if RH1 >= 60:
# AC_flow = 700
# if 58 < RH1 < 60:
# AC_flow = 400
# if RH1 <= 58:
# AC_flow = 300
#
#
#
# elif DeHumid_Sched == 0:
# AC_flow = 50
#
#
#
# TRNSYS.setOutputValue(1,abs(0))
# TRNSYS.setOutputValue(2,0)
# TRNSYS.setOutputValue(3,0)
# TRNSYS.setOutputValue(4,0)
# TRNSYS.setOutputValue(5,0)
# TRNSYS.setOutputValue(6,0)
# TRNSYS.setOutputValue(7,0)
# TRNSYS.setOutputValue(8,0)
# TRNSYS.setOutputValue(9,0)
# TRNSYS.setOutputValue(10,abs(0))
# TRNSYS.setOutputValue(11,0)
# TRNSYS.setOutputValue(12,ave_human_toPanels_F)
# TRNSYS.setOutputValue(13,0)
# TRNSYS.setOutputValue(14,0)
# TRNSYS.setOutputValue(15,0)
# TRNSYS.setOutputValue(16,abs(0))
# TRNSYS.setOutputValue(17,0)
# TRNSYS.setOutputValue(18,0)
# TRNSYS.setOutputValue(19,0)
# TRNSYS.setOutputValue(20, AC_flow)
# TRNSYS.setOutputValue(21, 0)
# TRNSYS.setOutputValue(22, 0)
# TRNSYS.setOutputValue(23, 0)
# TRNSYS.setOutputValue(24, 0)
# =============================================================================
return
