def get_clusters(employment, **kwargs):
'''
Find the clusters for weekdays, saturday and sunday for a household member
of the given eployment type based on the Crosstables given at
# http://homepages.vub.ac.be/~daerts/Occupancy.html
'''
#first go the the correct location
cdir = os.getcwd()
PATH = '../Data/Aerts_Occupancy/Crosstables/'
os.chdir(PATH)
#create an empty dictionary
keys = ['wkdy', 'sat', 'son']
cluDict = dict()
##########################################################################
# we find the cluster for each of the daytypes for the given employment
# in 'Crosstable_employment.txt'
for key in keys:
order = ['U12', 'FTE', 'PTE', 'Unemployed', 'Retired', 'School']
emp_i = order.index(employment)
data = np.loadtxt('Crosstable_Employment_' + key + '.txt',
float).T[emp_i]
rnd = np.random.random()
cluster = stats.get_probability(rnd, data[1:], p_type='prob')
cluDict.update({key: cluster})
##########################################################################
# and return the final cluster id's
os.chdir(cdir)
return cluDict
def get_clusters(employment, **kwargs):
'''
Find the clusters for weekdays, saturday and sunday for a household member
of the given eployment type based on the Crosstables given at
# http://homepages.vub.ac.be/~daerts/Occupancy.html
'''
#first go the the correct location
cdir = os.getcwd()
PATH = '../Data/Aerts_Occupancy/Crosstables/'
os.chdir(PATH)
#create an empty dictionary
keys = ['wkdy', 'sat', 'son']
cluDict = dict()
##########################################################################
# we find the cluster for each of the daytypes for the given employment
# in 'Crosstable_employment.txt'
for key in keys:
order = ['U12','FTE','PTE','Unemployed','Retired','School']
emp_i = order.index(employment)
data = np.loadtxt('Crosstable_Employment_'+key+'.txt', float).T[emp_i]
rnd = np.random.random()
cluster = stats.get_probability(rnd, data[1:], p_type='prob')
cluDict.update({key:cluster})
##########################################################################
# and return the final cluster id's
os.chdir(cdir)
return cluDict
def __shsetting__(self):
'''
Simulation of the space heating setting points.
- Including weekend days,
- starting from a regular monday at 4:00 AM.
'''
#######################################################################
# we define setting types based on their setpoint temperatures when
# when being active (1), sleeping (2) or absent (3).
types = dict()
types.update({'2' : {1:18.5, 2:15.0, 3:18.5}})
types.update({'3' : {1:20.0, 2:15.0, 3:19.5}})
types.update({'4' : {1:20.0, 2:11.0, 3:19.5}})
types.update({'5' : {1:20.0, 2:14.5, 3:15.0}})
types.update({'6' : {1:21.0, 2:20.5, 3:21.0}})
types.update({'7' : {1:21.5, 2:15.5, 3:21.5}})
# and the probabilities these types occur based on Duth research,
# i.e. Leidelmeijer and van Grieken (2005).
types.update({'prob' : [0.16, 0.35, 0.08, 0.11, 0.05, 0.20]})
# and given a type, denote which rooms are heated
given = dict()
given.update({'2' : ['dayzone','bathroom']})
given.update({'3' : [['dayzone'],['dayzone','bathroom'],['dayzone','nightzone']]})
given.update({'4' : [['dayzone'],['dayzone','nightzone']]})
given.update({'5' : ['dayzone']})
given.update({'6' : ['dayzone','bathroom','nightzone']})
given.update({'7' : ['dayzone','bathroom']})
#######################################################################
# select a type from the given tipes and probabilities
rnd = np.random.random()
shtype = str(1 + stats.get_probability(rnd, types['prob'], 'prob'))
if len(np.shape(given[shtype])) != 1:
nr = np.random.randint(np.shape(given[shtype])[0])
shrooms = given[shtype][nr]
else:
shrooms = given[shtype]
#######################################################################
# create a profile for he heated rooms
shnon = 12*np.ones(len(self.occ_m[0])+1)
shset = np.hstack((self.occ_m[0],self.occ_m[0][-1]))
for key in types[shtype].keys():
for i in range(len(shset)):
if int(shset[i]) == key:
shset[i] = types[shtype][key]
#######################################################################
# and couple to the heated rooms
sh_settings = dict()
for room in ['dayzone', 'nightzone', 'bathroom']:
if room in shrooms:
sh_settings.update({room:shset})
else:
sh_settings.update({room:shnon})
# and store
self.sh_settings = sh_settings
print ' - Average comfort setting is %s Celsius' % str(round(np.average(sh_settings['dayzone']),2))
return None
def __shsetting__(self):
'''
Simulation of the space heating setting points.
- Including weekend days,
- starting from a regular monday at 4:00 AM.
'''
#######################################################################
# we define setting types based on their setpoint temperatures
# when being active (1), sleeping (2) or absent (3).
types = dict()
types.update({'2': {1: 18.5, 2: 15.0, 3: 18.5}})
types.update({'3': {1: 20.0, 2: 15.0, 3: 19.5}})
types.update({'4': {1: 20.0, 2: 11.0, 3: 19.5}})
types.update({'5': {1: 20.0, 2: 14.5, 3: 15.0}})
types.update({'6': {1: 21.0, 2: 20.5, 3: 21.0}})
types.update({'7': {1: 21.5, 2: 15.5, 3: 21.5}})
# and the probabilities these types occur based on Dutch research,
# i.e. Leidelmeijer and van Grieken (2005).
types.update({'prob': [0.16, 0.35, 0.08, 0.11, 0.05, 0.20]})
# and given a type, denote which rooms are heated
given = dict()
given.update({'2': [['dayzone', 'bathroom']]})
given.update({
'3': [['dayzone'], ['dayzone', 'bathroom'],
['dayzone', 'nightzone']]
})
given.update({'4': [['dayzone'], ['dayzone', 'nightzone']]})
given.update({'5': [['dayzone']]})
given.update({'6': [['dayzone', 'bathroom', 'nightzone']]})
given.update({'7': [['dayzone', 'bathroom']]})
#######################################################################
# select a type from the given types and probabilities
rnd = np.random.random()
shtype = str(1 + stats.get_probability(rnd, types['prob'], 'prob'))
if len(given[shtype]) != 1:
nr = np.random.randint(np.shape(given[shtype])[0])
shrooms = given[shtype][nr]
else:
shrooms = given[shtype][0]
#######################################################################
# create a profile for the heated rooms
shnon = 12 * np.ones(len(self.occ_m[0]) + 1)
shset = np.hstack((self.occ_m[0], self.occ_m[0][-1]))
for key in types[shtype].keys():
for i in range(len(shset)):
if int(shset[i]) == key:
shset[i] = types[shtype][key]
#######################################################################
# and couple to the heated rooms
sh_settings = dict()
for room in ['dayzone', 'nightzone', 'bathroom']:
if room in shrooms:
sh_settings.update({room: shset})
else:
sh_settings.update({room: shnon})
# and store
self.sh_settings = sh_settings
print(' - Average comfort setting is %s Celsius' %
str(round(np.average(sh_settings['dayzone']), 2)))
return None
def __shsetting__(self):
'''
Simulation of the space heating setting points.
'''
#######################################################################
# we define setting types based on their setpoint temperatures
# when being active (1), sleeping (2) or absent (3).
types = dict()
types.update({'2': {1: 18.5, 2: 15.0, 3: 18.5}})
types.update({'3': {1: 20.0, 2: 15.0, 3: 19.5}})
types.update({'4': {1: 20.0, 2: 11.0, 3: 19.5}})
types.update({'5': {1: 20.0, 2: 14.5, 3: 15.0}})
types.update({'6': {1: 21.0, 2: 20.5, 3: 21.0}})
types.update({'7': {1: 21.5, 2: 15.5, 3: 21.5}})
# and the probabilities these types occur based on Dutch research,
# i.e. Leidelmeijer and van Grieken (2005).
types.update({'prob': [0.16, 0.35, 0.08, 0.11, 0.05, 0.20]})
# and given the type, denote which rooms are heated (more than one possibility)
shr = dict()
shr.update({'2': [['dayzone', 'bathroom']]})
shr.update({
'3': [['dayzone'], ['dayzone', 'bathroom'],
['dayzone', 'nightzone']]
})
shr.update({'4': [['dayzone'], ['dayzone', 'nightzone']]})
shr.update({'5': [['dayzone']]})
shr.update({'6': [['dayzone', 'bathroom', 'nightzone']]})
shr.update({'7': [['dayzone', 'bathroom']]})
#######################################################################
# select a type based on random number and probabilities associated to types
rnd = np.random.random()
shtype = str(1 + stats.get_probability(rnd, types['prob'], 'prob'))
#define which rooms will be heated
if len(shr[shtype]
) != 1: # if there are more possibilities, choose one randomly
nr = np.random.randint(np.shape(shr[shtype])[0])
shrooms = shr[shtype][nr]
else:
shrooms = shr[shtype][0]
#######################################################################
# create a profile for the heated rooms
shnon = 12 * np.ones(len(self.occ_m[0])) #non-heated rooms : 12 degC
shset = 12 * np.ones(
len(self.occ_m[0]
)) #initiate space heating settings also as non-heated
occu = self.occ_m[0] # get merged occupancy
for key in types[shtype].keys(): # for each occupancy state
shset[occu == key] = types[shtype][
key] # use appropriate temperature setting given in "types"
#######################################################################
# and couple to the heated rooms
sh_settings = dict()
for room in ['dayzone', 'nightzone', 'bathroom']:
if room in shrooms:
sh_settings.update({room: shset})
else:
sh_settings.update({room: shnon})
# and store
self.sh_settings = sh_settings
print(' - Average comfort setting is %s Celsius' %
str(round(np.average(sh_settings['dayzone']), 2)))
self.variables.update({
'sh_day':
'Space heating set-point temperature for day-zone in degrees Celsius.',
'sh_bath':
'Space heating set-point temperature for bathroom in degrees Celsius.',
'sh_night':
'Space heating set-point temperature for night-zone in degrees Celsius.'
})
return None