def test_eload_w_heat(self):
# Total number of occupants in apartment
nb_occ = 5
timestep = 60
# Generate occupancy object
occ_obj = occ.Occupancy(number_occupants=nb_occ)
# Get radiation
(q_direct, q_diffuse) = loadrad.get_rad_from_try_path()
# Convert 3600 s timestep to given timestep
q_direct = cr.change_resolution(q_direct, old_res=3600,
new_res=timestep)
q_diffuse = cr.change_resolution(q_diffuse, old_res=3600,
new_res=timestep)
# Generate stochastic electric power object
el_load_obj = eload.ElectricLoad(occ_profile=occ_obj.occupancy,
total_nb_occ=nb_occ,
q_direct=q_direct,
q_diffuse=q_diffuse,
is_sfh=False,
prev_heat_dev=True,
randomize_appliances=False,
light_config=2,
season_light_mod=True,
timestep=timestep)
def example_stoch_el_load(do_plot=False):
# Total number of occupants in apartment
nb_occ = 3
# Generate occupancy object
occ_obj = occ.Occupancy(number_occupants=nb_occ)
# Get radiation
(q_direct, q_diffuse) = loadrad.get_rad_from_try_path()
# Generate stochastic electric power object
el_load_obj = eload.ElectricLoad(occ_profile=occ_obj.occupancy,
total_nb_occ=nb_occ,
q_direct=q_direct, q_diffuse=q_diffuse)
occ_profile = cr.change_resolution(values=occ_obj.occupancy,
old_res=600,
new_res=60)
if do_plot:
fig = plt.figure()
fig.add_subplot(211)
plt.plot(occ_profile[0:1440], label='occupancy')
plt.xlabel('Timestep in minutes')
plt.ylabel('Number of active occupants')
fig.add_subplot(212)
plt.plot(el_load_obj.loadcurve[0:1440], label='El. load')
plt.xlabel('Timestep in minutes')
plt.ylabel('Electric power in W')
plt.tight_layout()
plt.show()
plt.close()
def __init__(self,
environment,
number_occupants,
initial_day=1,
nb_days=365,
do_profile=True):
"""
Constructor of occupancy object.
If occupancy profile is generated with do_profile == True, profile
is saved with timestep of 600 seconds on Occupancy.occupancy.
To return profile in other timestep, use method
get_occ_profile_in_curr_timestep.
Parameters
----------
environment : object
Environment of pyCity
number_occupants : int
Maximum number of occupants within apartment (range from 1 to 5)
initial_day : int, optional
Initial day. 1-5 correspond to Monday-Friday, 6-7 to Saturday and
Sunday
nb_days : int, optional
Number of days, which should be used to generate profile
(default: 365)
do_profile : bool, optional
Defines, if user profile should be generated (default: True).
If set to False, only number of occupants is saved and no
profile is generated.
"""
assert number_occupants > 0, ('At least 1 person has to be defined ' +
'as occupant')
assert number_occupants <= 5, ('Max. allowed number of occupants ' +
'per apartment is 5')
assert nb_days > 0, 'Number of days must be larger than zero.'
assert initial_day in [1, 2, 3, 4, 5, 6, 7]
self._kind = 'occupancy'
self.environment = environment
self.number_occupants = number_occupants
self.initial_day = initial_day
self.nb_days = nb_days
self.occupancy = None # Occupancy profile
if do_profile:
occupancy = occ.Occupancy(number_occupants=number_occupants,
initial_day=initial_day,
nb_days=nb_days,
do_profile=do_profile)
occupancy.gen_occ_profile(nb_days=nb_days)
# Save occupancy profile
self.occupancy = copy.copy(occupancy.occupancy)
def exampe_occupancy(do_plot=False):
# Instanciate occupancy object
occupancy_object = occ.Occupancy(number_occupants=3)
# Pointer to occupancy profile
occupancy_profile = occupancy_object.occupancy
print('Maximum number of active occupants:')
print(np.max(occupancy_profile))
if do_plot:
plt.figure()
plt.plot(occupancy_profile[:200])
plt.ylabel('Number of active occupants')
plt.show()
def exampe_occupancy(do_plot=False):
# Instanciate occupancy object
occupancy_object = occ.Occupancy(number_occupants=3)
# Pointer to occupancy profile
occupancy_profile = occupancy_object.occupancy
print('Maximum number of active occupants:')
print(np.max(occupancy_profile))
timestep = 600
# Generate time array for plotting
timesteps = int(24 * 3600 / timestep) # Number of timesteps per day
time_array = np.arange(0, timesteps * timestep, timestep) / 3600
if do_plot:
plt.figure()
plt.plot(time_array[:144], occupancy_profile[:144])
plt.xlabel('Time in hours')
plt.ylabel('Number of active occupants')
plt.show()
def main(): # pragma: no cover
# Total number of occupants in apartment
nb_occ = 3
timestep = 60
import richardsonpy.classes.occupancy as occ
# Generate occupancy object
occ_obj = occ.Occupancy(number_occupants=nb_occ)
# Get radiation
(q_direct, q_diffuse) = loadrad.get_rad_from_try_path()
# Convert 3600 s timestep to given timestep
q_direct = cr.change_resolution(q_direct, old_res=3600, new_res=timestep)
q_diffuse = cr.change_resolution(q_diffuse, old_res=3600, new_res=timestep)
# Generate stochastic electric power object
el_load_obj = ElectricLoad(occ_profile=occ_obj.occupancy,
total_nb_occ=nb_occ,
q_direct=q_direct,
q_diffuse=q_diffuse)
occ_profile = cr.change_resolution(values=occ_obj.occupancy,
old_res=600,
new_res=timestep)
import matplotlib.pyplot as plt
fig = plt.figure()
fig.add_subplot(211)
plt.title('Occupancy and electric load for 24 hours')
plt.plot(occ_profile[0:1440], label='occupancy')
plt.xlabel('Timestep in minutes')
plt.ylabel('Number of active occupants')
fig.add_subplot(212)
plt.plot(el_load_obj.loadcurve[0:1440], label='El. load')
plt.xlabel('Timestep in minutes')
plt.ylabel('Electric power in W')
plt.tight_layout()
plt.show()
plt.close()
fig = plt.figure()
fig.add_subplot(211)
plt.title('Annual occupancy and electric load profiles')
plt.plot(occ_profile, label='occupancy')
plt.xlabel('Timestep in minutes')
plt.ylabel('Number of active occupants')
fig.add_subplot(212)
plt.plot(el_load_obj.loadcurve, label='El. load')
plt.xlabel('Timestep in minutes')
plt.ylabel('Electric power in W')
plt.tight_layout()
plt.show()
plt.close()
sum_el_energy = sum(el_load_obj.loadcurve) * 60 / 3600000
print('Electric energy in kWh: ', sum_el_energy)
def example_stoch_el_load(do_plot=False):
# Total number of occupants in apartment
nb_occ = 3
timestep = 60 # in seconds
# Generate occupancy object (necessary as input for electric load gen.)
occ_obj = occ.Occupancy(number_occupants=nb_occ)
# Get radiation (necessary for lighting usage calculation)
(q_direct, q_diffuse) = loadrad.get_rad_from_try_path()
# Convert 3600 s timestep to given timestep
q_direct = cr.change_resolution(q_direct, old_res=3600, new_res=timestep)
q_diffuse = cr.change_resolution(q_diffuse, old_res=3600, new_res=timestep)
# Generate stochastic electric power object
el_load_obj = eload.ElectricLoad(occ_profile=occ_obj.occupancy,
total_nb_occ=nb_occ,
q_direct=q_direct,
q_diffuse=q_diffuse,
timestep=timestep)
# Copy occupancy object, before changing its resolution
# (occ_obj.occupancy is the pointer to the occupancy profile array)
occ_profile_copy = copy.copy(occ_obj.occupancy)
# Change resolution of occupancy object (to match
# resolution of el. load profile; necessary for plotting)
occ_profile_copy = cr.change_resolution(values=occ_profile_copy,
old_res=600,
new_res=timestep)
# Calculate el. energy in kWh
energy_el_kwh = sum(el_load_obj.loadcurve) * timestep / (3600 * 1000)
print('Electric energy demand in kWh: ')
print(energy_el_kwh)
if do_plot:
# Generate time array for plotting
timesteps = int(24 * 3600 / timestep) # Number of timesteps per day
time_array = np.arange(0, timesteps * timestep, timestep) / 3600
fig = plt.figure()
fig.add_subplot(211)
plt.plot(time_array, occ_profile_copy[0:timesteps])
plt.xlabel('Timestep in hours')
plt.ylabel('Number of active occupants')
plt.xlim([0, 24])
fig.add_subplot(212)
plt.plot(time_array, el_load_obj.loadcurve[0:timesteps])
plt.xlabel('Timestep in hours')
plt.ylabel('Electric power in W')
plt.xlim([0, 24])
plt.tight_layout()
plt.show()
plt.close()
if save_app_light:
self.light_load = light_load
self.app_load = app_load
self.loadcurve = loadcurve
if __name__ == '__main__':
# Total number of occupants in apartment
nb_occ = 3
import richardsonpy.classes.occupancy as occ
# Generate occupancy object
occ_obj = occ.Occupancy(number_occupants=nb_occ)
# Get radiation
(q_direct, q_diffuse) = loadrad.get_rad_from_try_path()
# Generate stochastic electric power object
el_load_obj = ElectricLoad(occ_profile=occ_obj.occupancy,
total_nb_occ=nb_occ,
q_direct=q_direct, q_diffuse=q_diffuse)
occ_profile = cr.change_resolution(values=occ_obj.occupancy,
old_res=600,
new_res=60)
import matplotlib.pyplot as plt