def analyze_thermal_properties_per_sim(self):
"""
Appends thermal properties for all runs and plot histograms
"""
# initialize temporal matrices
U_res = [[np.array([]) for col in range(4)] for row in range(10)]
C_res = [[np.array([]) for col in range(4)] for row in range(10)]
TAU_res = [[np.array([]) for col in range(4)] for row in range(10)]
SH_res = [[np.array([]) for col in range(4)] for row in range(10)]
HW_res = [[np.array([]) for col in range(4)] for row in range(10)]
HEAT_res = [[np.array([]) for col in range(4)] for row in range(10)]
U_nres = [np.array([]) for col in range(5)]
C_nres = [np.array([]) for col in range(5)]
TAU_nres = [np.array([]) for col in range(5)]
SH_nres = [np.array([]) for col in range(5)]
HW_nres = [np.array([]) for col in range(5)]
HEAT_nres = [np.array([]) for col in range(5)]
# append simulation values
for run in range(self.N):
# RESIDENTIAL
for year_class in range(10):
for btype in range(4):
U_res[year_class][btype] = np.append(U_res[year_class][btype], self.u_res[run][year_class][btype])
C_res[year_class][btype] = np.append(C_res[year_class][btype], self.c_res[run][year_class][btype])
TAU_res[year_class][btype] = np.append(TAU_res[year_class][btype], self.tau_res[run][year_class][btype])
SH_res[year_class][btype] = np.append(SH_res[year_class][btype], self.sh_per_area_res[run][year_class][btype])
HW_res[year_class][btype] = np.append(HW_res[year_class][btype], self.hw_per_area_res[run][year_class][btype])
HEAT_res[year_class][btype] = np.append(HEAT_res[year_class][btype], self.heat_per_area_res[run][year_class][btype])
# NON-RESIDENTIAL
for year_class in range(5):
U_nres[year_class] = np.append(U_nres[year_class], self.u_nres[run][year_class])
C_nres[year_class] = np.append(C_nres[year_class], self.c_nres[run][year_class])
TAU_nres[year_class] = np.append(TAU_nres[year_class], self.tau_nres[run][year_class])
SH_nres[year_class] = np.append(SH_nres[year_class], self.sh_per_area_nres[run][year_class])
HW_nres[year_class] = np.append(HW_nres[year_class], self.hw_per_area_nres[run][year_class])
HEAT_nres[year_class] = np.append(HEAT_nres[year_class], self.heat_per_area_nres[run][year_class])
# plot histograms
### RESIDENTIAL
###### U-value [MW/K]
figname = '{}/histU_res.png'.format(self.result_dir)
UrbanHeatPro.plot_histogram_table(3, U_res, 'U-value [MW/K]', figname,
factor = 1e6, figsize = (30, 25))
###### U-value [MW/K]
figname = '{}/histC_res.png'.format(self.result_dir)
UrbanHeatPro.plot_histogram_table(3, C_res, 'Thermal mass [GJ/K]', figname,
factor = 1e9, figsize = (30, 25))
###### Tau [h]
figname = '{}/histTau_res.png'.format(self.result_dir)
UrbanHeatPro.plot_histogram_table(3, TAU_res, 'Tau [h]', figname,
factor = 3600, figsize = (30, 25))
###### Space heating demand per unit area [kWh/m2]
figname = '{}/histSHD_perUnitArea_res.png'.format(self.result_dir)
UrbanHeatPro.plot_histogram_table(3, SH_res, 'Energy [kWh/m2]', figname,
factor = 1e3, figsize = (30, 25))
###### Hot water demand per unit area [kWh/m2]
figname = '{}/histHWD_perUnitArea_res.png'.format(self.result_dir)
UrbanHeatPro.plot_histogram_table(3, HW_res, 'Energy [kWh/m2]', figname,
factor = 1e3, figsize = (30, 25))
###### Total heat demand per unit area [kWh/m2]
figname = '{}/histHeat_perUnitArea_res.png'.format(self.result_dir)
UrbanHeatPro.plot_histogram_table(3, HEAT_res, 'Energy [kWh/m2]', figname,
factor = 1e3, figsize = (30, 25))
### NON-RESIDENTIAL
###### U-value [MW/K]
figname = '{}/histU_nres.png'.format(self.result_dir)
UrbanHeatPro.plot_histogram_table(0, U_nres, 'U-value [MW/K]', figname,
factor = 1e6, figsize = (30, 25))
###### U-value [MW/K]
figname = '{}/histC_nres.png'.format(self.result_dir)
UrbanHeatPro.plot_histogram_table(0, C_nres, 'Thermal mass [GJ/K]', figname,
factor = 1e9, figsize = (30, 25))
###### Tau [h]
figname = '{}/histTau_nres.png'.format(self.result_dir)
UrbanHeatPro.plot_histogram_table(0, TAU_nres, 'Tau [h]', figname,
factor = 3600, figsize = (30, 25))
###### Space heating demand per unit area [kWh/m2]
figname = '{}/histSHD_perUnitArea_nres.png'.format(self.result_dir)
UrbanHeatPro.plot_histogram_table(0, SH_nres, 'Energy [kWh/m2]', figname,
factor = 1e3, figsize = (30, 25))
###### Hot water demand per unit area [kWh/m2]
figname = '{}/histHWD_perUnitArea_nres.png'.format(self.result_dir)
UrbanHeatPro.plot_histogram_table(0, HW_nres, 'Energy [kWh/m2]', figname,
factor = 1e3, figsize = (30, 25))
###### Total heat demand per unit area [kWh/m2]
figname = '{}/histHeat_perUnitArea_nres.png'.format(self.result_dir)
UrbanHeatPro.plot_histogram_table(0, HEAT_nres, 'Energy [kWh/m2]', figname,
factor = 1e3, figsize = (30, 25))
def analyze_thermal_properties_per_run(self, run, result_dir_run):
"""
Extracts data from object City and plots a histogram showing the values of the
thermal properties for all buildings in the city. Buildings are classified according
to year construction class and building type. Analyzed thermal parameters are:
U-value [W/K]
Thermal mass [J/K]
Tau [s]
Space heat demand per unit area [Wh/m2]
Hot water demand per unit area [Wh/m2]
Total heat demand per unit area [Wh/m2]
"""
# extract data from City object
for year_class in range(10):
for btype in range(4):
# extract all buildings B with specific year_class, btype and use
c0 = self.my_city.energy_per_building[:, 1] == 3 # RESIDENTIAL
c1 = self.my_city.energy_per_building[:, 2] == year_class
c2 = self.my_city.energy_per_building[:, 3] == btype
B = self.my_city.energy_per_building[c0 * c1 * c2]
# U-value [W/K]
self.u_res[run][year_class][btype] = np.append(self.u_res[run][year_class][btype], \
B[:, 13])
# Thermal mass [J/K]
self.c_res[run][year_class][btype] = np.append(self.c_res[run][year_class][btype], \
B[:, 14])
# Tau [s]
self.tau_res[run][year_class][btype] = np.append(self.tau_res[run][year_class][btype], \
B[:, 15])
# Space heat demand per unit area [Wh/m2]
self.sh_per_area_res[run][year_class][btype] = np.append(self.sh_per_area_res[run][year_class][btype], \
(B[:, 16] / B[:, 5]))
# Hot water demand per unit area [Wh/m2]
self.hw_per_area_res[run][year_class][btype] = np.append(self.hw_per_area_res[run][year_class][btype], \
(B[:, 17] / B[:, 5]))
# Total heat demand per unit area [Wh/m2]
self.heat_per_area_res[run][year_class][btype] = np.append(self.heat_per_area_res[run][year_class][btype], \
(B[:, 18] / B[:, 5]))
for year_class in range(5):
# extract all buildings B with specific year_class, btype and use
C0 = self.my_city.energy_per_building[:, 1] != 3 # NON-RESIDENTIAL
c1 = self.my_city.energy_per_building[:, 2] == year_class
B = self.my_city.energy_per_building[c0 * c1]
# U-value [W/K]
self.u_nres[run][year_class] = np.append(self.u_nres[run][year_class], B[:, 13])
# Thermal mass [J/K]
self.c_nres[run][year_class] = np.append(self.c_nres[run][year_class], B[:, 14])
# Tau [s]
self.tau_nres[run][year_class] = np.append(self.tau_nres[run][year_class], B[:, 15])
# Space heat demand per unit area [Wh/m2]
self.sh_per_area_nres[run][year_class] = np.append(self.sh_per_area_nres[run][year_class], \
(B[:, 16] / B[:, 5]))
# Hot water demand per unit area [Wh/m2]
self.hw_per_area_nres[run][year_class] = np.append(self.hw_per_area_nres[run][year_class], \
(B[:, 17] / B[:, 5]))
# Total heat demand per unit area [Wh/m2]
self.heat_per_area_nres[run][year_class] = np.append(self.heat_per_area_nres[run][year_class], \
(B[:, 18] / B[:, 5]))
# plot histograms per run
### RESIDENTIAL
###### U-value [MW/K]
figname = '{}/histU_res_{}.png'.format(result_dir_run, run)
UrbanHeatPro.plot_histogram_table(3, self.u_res[run], 'U-value [MW/K]', figname,
factor = 1e6, figsize = (30, 25))
###### U-value [MW/K]
figname = '{}/histC_res_{}.png'.format(result_dir_run, run)
UrbanHeatPro.plot_histogram_table(3, self.c_res[run], 'Thermal mass [GJ/K]', figname,
factor = 1e9, figsize = (30, 25))
###### Tau [h]
figname = '{}/histTau_res_{}.png'.format(result_dir_run, run)
UrbanHeatPro.plot_histogram_table(3, self.tau_res[run], 'Tau [h]', figname,
factor = 3600, figsize = (30, 25))
###### Space heating demand per unit area [kWh/m2]
figname = '{}/histSHD_perUnitArea_res_{}.png'.format(result_dir_run, run)
UrbanHeatPro.plot_histogram_table(3, self.sh_per_area_res[run], 'Energy [kWh/m2]', figname,
factor = 1e3, figsize = (30, 25))
###### Hot water demand per unit area [kWh/m2]
figname = '{}/histHWD_perUnitArea_res_{}.png'.format(result_dir_run, run)
UrbanHeatPro.plot_histogram_table(3, self.hw_per_area_res[run], 'Energy [kWh/m2]', figname,
factor = 1e3, figsize = (30, 25))
###### Total heat demand per unit area [kWh/m2]
figname = '{}/histHeat_perUnitArea_res_{}.png'.format(result_dir_run, run)
UrbanHeatPro.plot_histogram_table(3, self.heat_per_area_res[run], 'Energy [kWh/m2]', figname,
factor = 1e3, figsize = (30, 25))
### NON-RESIDENTIAL
###### U-value [MW/K]
figname = '{}/histU_nres_{}.png'.format(result_dir_run, run)
UrbanHeatPro.plot_histogram_table(0, self.u_nres[run], 'U-value [MW/K]', figname,
factor = 1e6, figsize = (30, 25))
###### U-value [MW/K]
figname = '{}/histC_nres_{}.png'.format(result_dir_run, run)
UrbanHeatPro.plot_histogram_table(0, self.c_nres[run], 'Thermal mass [GJ/K]', figname,
factor = 1e9, figsize = (30, 25))
###### Tau [h]
figname = '{}/histTau_nres_{}.png'.format(result_dir_run, run)
UrbanHeatPro.plot_histogram_table(0, self.tau_nres[run], 'Tau [h]', figname,
factor = 3600, figsize = (30, 25))
###### Space heating demand per unit area [kWh/m2]
figname = '{}/histSHD_perUnitArea_nres_{}.png'.format(result_dir_run, run)
UrbanHeatPro.plot_histogram_table(0, self.sh_per_area_nres[run], 'Energy [kWh/m2]', figname,
factor = 1e3, figsize = (30, 25))
###### Hot water demand per unit area [kWh/m2]
figname = '{}/histHWD_perUnitArea_nres_{}.png'.format(result_dir_run, run)
UrbanHeatPro.plot_histogram_table(0, self.hw_per_area_nres[run], 'Energy [kWh/m2]', figname,
factor = 1e3, figsize = (30, 25))
###### Total heat demand per unit area [kWh/m2]
figname = '{}/histHeat_perUnitArea_nres_{}.png'.format(result_dir_run, run)
UrbanHeatPro.plot_histogram_table(0, self.heat_per_area_nres[run], 'Energy [kWh/m2]', figname,
factor = 1e3, figsize = (30, 25))
