#Infiltration, Ventilation and Distribution losses Calculation:
input_data_inf_vent = pd.read_csv("input_inf_vent.csv", sep=";", index_col=0)
Output_Inf_Vent = iv.inf_vent_load_calc(input_data_inf_vent)
input_data_distribution = pd.read_csv("Input_distribution.csv",
sep=";",
index_col=0)
Losses = iv.Q_distri_Losses(input_data_distribution, Qfen_heating_load,
Qfen_cooling_load, QtotOpaque_winter,
QtotOpaque_summer, Output_Inf_Vent.iloc[7][0],
Output_Inf_Vent.iloc[6][0],
Output_Inf_Vent.iloc[8][0])
#Latent results
QtotLatent = lat.Qtot_latent(input_data_inf_vent, inputs_list)
#Final Results
results_DF = pd.read_csv("results_empty.csv", sep=";", index_col=0)
results_DF["Heating"] = [
QtotOpaque_winter, Qfen_heating_load, 0, 0, Output_Inf_Vent.iloc[7][0],
Losses.iloc[0][0], 0, 0
]
results_DF["Cooling"] = [
QtotOpaque_summer, Qfen_cooling_load, 0, Output_Inf_Vent.iloc[8][0],
Output_Inf_Vent.iloc[6][0], Losses.iloc[1][0], 0, QtotLatent
]
for column in results_DF.columns.tolist():
sensible_loads = pd.Series(results_DF[column][0:6])
results_DF[column]["Q_sensible_tot"] = sensible_loads.sum()
#results_DF.to_csv("results_wholeRFL.csv",sep =";")
) #taking values that are needed for calculation heating and cooling loads for inf+vent and internal gains
Output_Inf_Vent = iv.inf_vent_load_calc(
input_data_inf_vent
) #calculation of total inf+vent air flow in summer and winter,and calculation of heating and cooling loads for inf +vent and internal gains (just cooling)
input_data_distribution = pd.read_csv(
"input_distribution.csv", sep=";", index_col=0
) #list of type of cooling and heating system that is used, rated leakage,number of stories (needed for calculation coefficient for distributive losses)
Losses = iv.Q_distri_Losses(
input_data_distribution, Qfen_heating_load, Qfen_cooling_load,
QtotOpaque_winter, QtotOpaque_summer, Output_Inf_Vent.iloc[7][0],
Output_Inf_Vent.iloc[6][0],
Output_Inf_Vent.iloc[8][0]) #calculation of distributive losses
#Latent results
QtotLatent = lat.Qtot_latent(input_data_inf_vent,
inputs_list) #summing latent loads (for cooling)
#Final Results
results_DF = pd.read_csv("results_empty.csv", sep=";",
index_col=0) #put all the results in a data frame
results_DF["Heating"] = [
QtotOpaque_winter, Qfen_heating_load, 0, 0, Output_Inf_Vent.iloc[7][0],
Losses.iloc[0][0], 0, 0
]
results_DF["Cooling"] = [
QtotOpaque_summer, Qfen_cooling_load, 0, Output_Inf_Vent.iloc[8][0],
Output_Inf_Vent.iloc[6][0], Losses.iloc[1][0], 0, QtotLatent
]
for column in results_DF.columns.tolist():
sensible_loads = pd.Series(results_DF[column][0:6]) #6 not included
results_DF[column]["Q_sensible_tot"] = sensible_loads.sum(
def solverr(wallwinter, wallsummer, Windows):
Folder_whereThoseTablesAre = r"C:\Users\Famiglia\Documents\Manuel\Polimi\MAGISTRALE\PRIMO ANNO\Primo semestre\Bezhad\EETBS-Assignments-Polimi-2018-2019-\Assignment7_A\ExampleAssignments_fromPreviousYear\example1_assignment8"
os.chdir(Folder_whereThoseTablesAre)
# Opaque surfaces Calculation:
numericalDataDF = pd.read_csv("input_numerical_data.csv",
sep=";",
index_col=0) #numerical
dataDF = pd.read_csv("input_data.csv", sep=";", index_col=0) #string
materials_DataFrame = pd.read_csv("resistences_materials.csv",
sep=";",
index_col=1) #materials and resistances
inputWalls_DataFrame_winter = pd.read_csv(
wallwinter, sep=";", index_col=0) # reading the wallwinter data
inputWalls_DataFrame_summer = pd.read_csv(
wallsummer, sep=";", index_col=0) # reading the wallsummer data
inputDoor_DataFrame_winter = pd.read_csv(
"input_data_door_winter.csv", sep=";",
index_col=0) # reading the doorwinter data
inputDoor_DataFrame_summer = pd.read_csv(
"input_data_door_summer.csv", sep=";",
index_col=0) # reading the doorsummer data
#calling functions to calculate wall and door loads
U_wall_winter = funcOp.Utot_wall_Calculator(inputWalls_DataFrame_winter,
materials_DataFrame)
U_wall_summer = funcOp.Utot_wall_Calculator(inputWalls_DataFrame_summer,
materials_DataFrame)
U_door_winter = funcOp.Utot_door_Calculator(inputDoor_DataFrame_winter,
materials_DataFrame)
U_door_summer = funcOp.Utot_door_Calculator(inputDoor_DataFrame_summer,
materials_DataFrame)
#printing the Heating and Cooling loads of the opaque elements of the building
print "\nThis is the value of Uwall_winter: " + str(
U_wall_winter) + " W/(m^2 * K)"
print "This is the value of Udoor_winter: " + str(
U_door_winter) + " W/(m^2 * K)"
print "This is the value of Uceiling: " + str(
numericalDataDF["value"]["U_ceiling"]) + " W/(m^2 * K)"
QtotOpaque_winter = funcOp.QtotOpaque_winter_calculator(
numericalDataDF["value"]["height_windows"],
numericalDataDF["value"]["width_windowsS"],
numericalDataDF["value"]["width_windowsE"],
numericalDataDF["value"]["width_windowsW"], U_wall_winter,
numericalDataDF["value"]["U_ceiling"], U_door_winter,
inputs_list["deltaTheating"])
print "\tThis is the opaque heating load: " + str(
QtotOpaque_winter) + " W\n"
print "\nThis is the value of Uwall_summer: " + str(
U_wall_summer) + " W/(m^2 * K)"
print "This is the value of Udoor_summer: " + str(
U_door_summer) + " W/(m^2 * K)"
print "This is the value of Uceiling: " + str(
numericalDataDF["value"]["U_ceiling"]) + " W/(m^2 * K)"
QtotOpaque_summer = funcOp.QtotOpaque_summer_calculator(
numericalDataDF["value"]["height_windows"],
numericalDataDF["value"]["width_windowsS"],
numericalDataDF["value"]["width_windowsE"],
numericalDataDF["value"]["width_windowsW"], U_wall_summer,
numericalDataDF["value"]["U_ceiling"], U_door_summer,
dataDF["characteristic"]["colour_roof"],
dataDF["characteristic"]["material_roof"],
inputs_list["deltaTcooling"], inputs_list["DRcooling"],
dataDF["characteristic"]["walls_surface_type"],
dataDF["characteristic"]["ceiling_surface_type"],
dataDF["characteristic"]["doors_surface_type"])
print "\tThis is the opaque cooling load: " + str(
QtotOpaque_summer) + " W\n"
#Fenestration surfaces Calculation:
windows_DF = pd.read_csv(Windows, sep=";",
index_col=0) #reading fenestration data file
windows_DF["Area"] = windows_DF["Height"] * windows_DF[
"Width"] #calculating areas of all the windows
Qfen_heating_load = func.Qfen_heating_calculator(
windows_DF,
inputs_list) #calling function to calculate fenestration heating load
print 'The total amount of the heating load for the windows is ' + str(
Qfen_heating_load) + ' W.'
Qfen_cooling_load = func.Qfen_cooling_calculator(
windows_DF,
inputs_list) #calling function to calculate fenestration cooling load
print 'The total amount of the cooling load for the windows is ' + str(
Qfen_cooling_load) + ' W.'
#Infiltration, Ventilation and Distribution losses Calculation:
input_data_inf_vent = pd.read_csv(
"input_inf_vent.csv", sep=";",
index_col=0) #importing infiltration and ventilation data
Output_Inf_Vent = iv.inf_vent_load_calc(input_data_inf_vent)
input_data_distribution = pd.read_csv(
"input_distribution.csv", sep=";",
index_col=0) #input data for read the distribution table
Losses = iv.Q_distri_Losses(input_data_distribution, Qfen_heating_load,
Qfen_cooling_load, QtotOpaque_winter,
QtotOpaque_summer, Output_Inf_Vent.iloc[7][0],
Output_Inf_Vent.iloc[6][0],
Output_Inf_Vent.iloc[8][0])
#Latent results
QtotLatent = lat.Qtot_latent(input_data_inf_vent, inputs_list)
#Final Results
results_DF = pd.read_csv("results_empty.csv", sep=";",
index_col=0) #put all the results in a data frame
results_DF["Heating"] = [
QtotOpaque_winter, Qfen_heating_load, 0, 0, Output_Inf_Vent.iloc[7][0],
Losses.iloc[0][0], 0, 0
]
#calculated values of the loads are arranged in dataframes
results_DF["Cooling"] = [
QtotOpaque_summer, Qfen_cooling_load, 0, Output_Inf_Vent.iloc[8][0],
Output_Inf_Vent.iloc[6][0], Losses.iloc[1][0], 0, QtotLatent
]
for column in results_DF.columns.tolist():
sensible_loads = pd.Series(results_DF[column][0:6]) #6 not included
results_DF[column]["Q_sensible_tot"] = sensible_loads.sum(
) #sum of all sensible and put it in the table
#results_DF.to_csv("results_wholeRFL.csv",sep =";")
print "\nThis is the value of sensible internal gain: " + str(
Output_Inf_Vent["Results"]["Internal Gain, sensible [W]"]) + " W."
print "This is the value of sensible infiltration-ventilation Cooling load: " + str(
Output_Inf_Vent["Results"]
["Sensible Infiltration/Ventilation Cooling Load [W]"]) + " W."
print "This is the value of sensible infiltration-ventilation Heating load: " + str(
Output_Inf_Vent["Results"]
["Sensible Infiltration/Ventilation Heating Load [W]"]) + " W.\n"
print "This is the value of Heating distribution losses: " + str(
Losses["Results"]["Heating distribution losses"]) + " W."
print "This is the value of Cooling distribution losses: " + str(
Losses["Results"]["Cooling distribution losses"]) + " W.\n"
print "\t\t\t So the total Sensible Heating Load is :" + str(
results_DF["Heating"]["Q_sensible_tot"]) + " W."
print "\t\t\t So the total Sensible Cooling Load is :" + str(
results_DF["Cooling"]["Q_sensible_tot"]) + " W.\n"
print "Here is given a table with all the results:\n"
print results_DF
return results_DF
Qfen_heating_load = func.Qfen_heating_calculator(windows_DF,inputs_list) #Q window heating
print 'The total amount of the heating load for the windows is '+str(Qfen_heating_load)+' W.'
Qfen_cooling_load = func.Qfen_cooling_calculator(windows_DF,inputs_list) #Q window cooling
print 'The total amount of the cooling load for the windows is '+str(Qfen_cooling_load)+' W.'
#Infiltration, Ventilation and Distribution losses Calculation:
input_data_inf_vent = pd.read_csv("input_inf_vent.csv",sep = ";",index_col=0)
Output_Inf_Vent = iv.inf_vent_load_calc(input_data_inf_vent)
input_data_distribution = pd.read_csv("input_distribution.csv",sep = ";",index_col=0) #input data to read from the distribution table
Losses = iv.Q_distri_Losses(input_data_distribution,Qfen_heating_load,Qfen_cooling_load,QtotOpaque_winter,QtotOpaque_summer,Output_Inf_Vent.iloc[7][0],Output_Inf_Vent.iloc[6][0],Output_Inf_Vent.iloc[8][0])
#Latent results
QtotLatent = lat.Qtot_latent (input_data_inf_vent, inputs_list)
#Final Results
results_DF = pd.read_csv("results_empty.csv",sep=";",index_col=0) #put all the results in a data frame
results_DF["Heating"] = [QtotOpaque_winter,Qfen_heating_load,0,0,Output_Inf_Vent.iloc[7][0],Losses.iloc[0][0],0,0]
results_DF["Cooling"] = [QtotOpaque_summer,Qfen_cooling_load,0,Output_Inf_Vent.iloc[8][0],Output_Inf_Vent.iloc[6][0],Losses.iloc[1][0],0,QtotLatent]
for column in results_DF.columns.tolist():
sensible_loads = pd.Series(results_DF[column][0:6])
results_DF[column]["Q_sensible_tot"] = sensible_loads.sum() #total sensible heat
print "Here is given a table with all the results:\n"
print results_DF
#WORST CASE (--)
#WALL: wood_bevel, wood_fiber, com_brick, concrete_heavyweight (70%), wood_stud_2.4 (30%), gypsum
#WINDOWS: 1a, Aluminium frame, RollerDarkOpaque, Fcl 0,3