def inside_temperature(self, cfg):
if cfg['Server']:
c = get_client(agent='172.17.0.1:28589', entity="thanos.ent")
else:
c = get_client()
archiver = DataClient(c)
uuids = [cfg['UUIDS']['thermostat_temperature']]
temp_now = self.current_time
start = '"' + temp_now.strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
end = '"' + (temp_now - datetime.timedelta(minutes=10)
).strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
dfs = make_dataframe(
archiver.window_uuids(uuids, end, start, '1min', timeout=120))
for uid, df in dfs.items():
if uid == uuids[0]:
if 'mean' in df.columns:
df = df[['mean']]
df.columns = ['tin']
dfs[uid] = df.resample('1min').mean()
uid, df = dfs.items()[0]
df['tin'] = df['tin'].replace(to_replace=0, method='pad')
return df['tin']
def _fetch_dataframe():
"""
Returns a `pandas.DataFrame` with columns
tin: the current temperature
a: 0=noop, 1=cooling, 2=heating
a1: is cooling?
a2: is heating?
next_temp: the temperature at the next time step
"""
with _silence():
# set $BW2_AGENT and $BW2_DEFAULT_ENTITY
archiver = DataClient(get_client())
now = datetime.now(pytz.timezone('America/Los_Angeles'))
start = (now + timedelta(minutes=15)).strftime(DATE_FMT)
end = (now - timedelta(days=7)).strftime(DATE_FMT)
dfs = make_dataframe(archiver.window_uuids(UUIDS, end, start, INTERVAL))
for uid, df in dfs.items():
if uid == SE_TEMP:
if 'mean' in df.columns:
df = df[['mean']]
df.columns = ['tin']
elif uid == SE_STATE:
if 'max' in df.columns:
df = df[['max']]
df.columns = ['a']
dfs[uid] = df.resample(INTERVAL).mean()
df = pd.concat([dframe for uid, dframe in dfs.items()], axis=1)
df['a1'] = df.apply(lambda row: int(row['a'] > 0 and row['a'] <= 1),
axis=1)
df['a2'] = df.apply(lambda row: int(row['a'] > 1), axis=1)
# the following are the features used by the baseline model
df['tin'] = df['tin'].replace(to_replace=0, method='pad')
df['tin_a1'] = df.tin * df.a1
df['tin_a2'] = df.tin * df.a2
df['next_temp'] = df['tin'].shift(-1)
# the following are necessary because rulinalg complains about ill-conditioning
# note that numpy does not have this problem
df.tin_a1 += np.random.randn(len(df.tin)) * 1e-8
df.tin_a2 += np.random.randn(len(df.tin)) * 1e-8
df = df.dropna()
return df
def __init__(self,
cfg,
now=datetime.datetime.utcnow().replace(
tzinfo=pytz.timezone("UTC")).astimezone(
tz=pytz.timezone("America/Los_Angeles")),
heat=4000,
cool=4000,
vent=500):
# query the server to learn the energy rates of the next 8 hours
self.heat = heat
self.cool = cool
self.vent = vent
self.mode = cfg['energy_rates']
self.now = now
if cfg['Server']:
c = get_client(agent='172.17.0.1:28589', entity="thanos.ent")
else:
c = get_client()
archiver = DataClient(c)
uuids = [cfg['UUIDS']['energy_cost']]
start = '"' + (now).strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
end = '"' + (
now - timedelta(hours=8)).strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
dfs = make_dataframe(
archiver.window_uuids(uuids, end, start, '15min', timeout=120))
for uid, df in dfs.items():
if uid == uuids[0]:
if 'mean' in df.columns:
df = df[['mean']]
df.columns = ['cost']
dfs[uid] = df.resample('15min').mean()
uid, self.df = dfs.items()[0]
def hvac_control():
try:
# query the server to lean the current setpoints and the state of the thermostat
if cfg['Server']:
c = get_client(agent='172.17.0.1:28589', entity="thanos.ent")
else:
c = get_client()
archiver = DataClient(c)
uuids = [
cfg['UUIDS']['thermostat_high'], cfg['UUIDS']['thermostat_low'],
cfg['UUIDS']['thermostat_mode']
]
temp_now = datetime.datetime.utcnow().replace(
tzinfo=pytz.timezone("UTC")).astimezone(
tz=pytz.timezone("America/Los_Angeles"))
start = '"' + temp_now.strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
end = '"' + (temp_now - datetime.timedelta(minutes=10)
).strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
dfs = make_dataframe(
archiver.window_uuids(uuids, end, start, '1min', timeout=120))
for uid, df in dfs.items():
if uid == uuids[0]:
if 'mean' in df.columns:
df = df[['mean']]
df.columns = ['t_high']
elif uid == uuids[1]:
if 'mean' in df.columns:
df = df[['mean']]
df.columns = ['t_low']
elif uid == uuids[2]:
if 'mean' in df.columns:
df = df[['mean']]
df.columns = ['mode']
dfs[uid] = df.resample('1min').mean()
df = pd.concat([dframe for uid, dframe in dfs.items()], axis=1)
except:
e = sys.exc_info()[0]
print e
return False
# document the "before" state
try:
f = open(filename, 'a')
f.write("Did read: " + str(df['t_low'][-1]) + ", " +
str(df['t_high'][-1]) + ", " + str(df['mode'][-1]) + "\n")
f.close()
except:
print "Could not document changes."
# choose the apropriate setpoints according to weekday and time
weekno = temp_now.weekday()
if weekno < 5:
now = datetime.datetime.utcnow().replace(
tzinfo=pytz.timezone("UTC")).astimezone(
tz=pytz.timezone("America/Los_Angeles"))
now_time = now.time()
if now_time >= datetime.time(18, 0) or now_time < datetime.time(7, 0):
heating_setpoint = 62.
cooling_setpoint = 85.
else:
heating_setpoint = 70.
cooling_setpoint = 76.
else:
heating_setpoint = 62.
cooling_setpoint = 85.
try:
adv = Advise(cfg)
action, temp = adv.advise()
temp = float(temp)
except:
e = sys.exc_info()[0]
print e
return False
# action "0" is Do Nothing, action "1" is Cooling, action "2" is Heating
if action == "0":
p = {
"override": True,
"heating_setpoint": math.floor(temp - 0.1) - 1,
"cooling_setpoint": math.ceil(temp + 0.1) + 1,
"mode": 3
}
print "Doing nothing"
print p
# document changes
try:
f = open(filename, 'a')
f.write("Did write: " + str(math.floor(temp - 0.1) - 1) + ", " +
str(math.ceil(temp + 0.1) + 1) + ", " + str(3) + "\n")
f.close()
except:
print "Could not document changes."
elif action == "1":
p = {
"override": True,
"heating_setpoint": heating_setpoint,
"cooling_setpoint": math.floor(temp - 0.1),
"mode": 3
}
print "Heating"
print p
# document changes
try:
f = open(filename, 'a')
f.write("Did write: " + str(heating_setpoint) + ", " +
str(math.floor(temp - 0.1)) + ", " + str(3) + "\n")
f.close()
except:
print "Could not document changes."
elif action == "2":
p = {
"override": True,
"heating_setpoint": math.ceil(temp + 0.1),
"cooling_setpoint": cooling_setpoint,
"mode": 3
}
print "Cooling"
print p
# document changes
try:
f = open(filename, 'a')
f.write("Did write: " + str(math.ceil(temp + 0.1)) + ", " +
str(cooling_setpoint) + ", " + str(3) + "\n")
f.close()
except:
print "Could not document changes."
else:
print "Problem with action."
return False
# try to commit the changes to the thermostat, if it doesnt work 10 times in a row ignore and try again later
for z in normal_zones:
for i in range(10):
try:
zones[z].write(p)
break
except:
if i == 9:
e = sys.exc_info()[0]
print e
return False
continue
return True
c = get_client()
archiver = DataClient(c)
demand = "4d6e251a-48e1-3bc0-907d-7d5440c34bb9"
uuids = [demand]
now = datetime.datetime.utcnow().replace(
tzinfo=pytz.timezone("UTC")).astimezone(
tz=pytz.timezone("America/Los_Angeles"))
start = '"' + now.strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
end = '"2017-9-21 00:00:00 PST"'
dfs = make_dataframe(
archiver.window_uuids(uuids, end, start, '1min', timeout=120))
for uid, df in dfs.items():
if uid == uuids[0]:
if 'mean' in df.columns:
df = df[['mean']]
df.columns = ['House Consumption']
dfs[uid] = df.resample('1min').mean()
uid, df = dfs.items()[0]
df = df / (1000 * 60)
print df.resample('60T').sum().describe()
per_zone_occ_list = []
for zone_name in zones:
query_list = []
for i in uuids:
if i[0] == zone_name:
query_list.append(i[1])
start = '"' + temp_now.strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
end = '"' + (temp_now -
timedelta(days=60)).strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
dfs = make_dataframe(
archiver.window_uuids(query_list, end, start, '15min', timeout=120))
for uid, df in dfs.items():
if 'mean' in df.columns:
df = df[['mean']]
df.columns = ['occ']
dfs[uid] = df.resample('15min').mean()
df = dfs.values()[0]
if len(dfs) > 1:
for newdf in dfs.values()[1:]:
df['occ'] += newdf['occ']
df['occ'] = 1 * (df['occ'] > 0)
df.index = df.index.tz_localize(pytz.timezone("America/Los_Angeles"))
per_zone_occ_list.append(df)
def __init__(self,
cfg,
now=datetime.datetime.utcnow().replace(
tzinfo=pytz.timezone("UTC")).astimezone(
tz=pytz.timezone("America/Los_Angeles"))):
# query the server to get all the available occupancy sensors
zone_name = cfg['zone']
if cfg['Server']:
c = get_client(agent='172.17.0.1:28589', entity="thanos.ent")
else:
c = get_client()
archiver = DataClient(c)
hod = HodClient("ciee/hod", c)
occ_query = """SELECT ?sensor ?uuid ?zone WHERE {
?sensor rdf:type brick:Occupancy_Sensor .
?sensor bf:isLocatedIn/bf:isPartOf ?zone .
?sensor bf:uuid ?uuid .
?zone rdf:type brick:HVAC_Zone
};
"""
results = hod.do_query(occ_query)
uuids = [[x['?zone'], x['?uuid']] for x in results['Rows']]
temp_now = now
# select the sensors that are contained in the zone we are optimizing for
query_list = []
for i in uuids:
if i[0] == zone_name:
query_list.append(i[1])
start = '"' + (temp_now).strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
end = '"' + (temp_now - timedelta(days=30)
).strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
dfs = make_dataframe(
archiver.window_uuids(query_list, end, start, '15min',
timeout=120))
for uid, df in dfs.items():
if 'mean' in df.columns:
df = df[['mean']]
df.columns = ['occ']
dfs[uid] = df.resample('15min').mean()
df = dfs.values()[0]
if len(dfs) > 1:
for newdf in dfs.values()[1:]:
df['occ'] += newdf['occ']
df['occ'] = 1 * (df['occ'] > 0)
df.index = df.index.tz_localize(pytz.timezone("America/Los_Angeles"))
observation_length_addition = 4 * 60
k = 5
prediction_time = 4 * 60
resample_time = 15
#now = df.index[-prediction_time/resample_time]
now = df.index[-1]
observation_length = mins_in_day(now) + observation_length_addition
similar_moments = find_similar_days(df, now, observation_length, k)
self.predictions = predict(df, now, similar_moments, prediction_time,
resample_time)
def preprocess_thermal(self, cfg):
# if state is 1 we are doing heating
def f1(row):
if row['a'] > 0 and row['a'] <= 1:
val = 1
else:
val = 0
return val
# if state is 2 we are doing cooling
def f2(row):
if row['a'] > 1 and row['a'] <= 2:
val = 1
else:
val = 0
return val
final_df = pd.DataFrame()
flag = True
heating_count = 0
cooling_count = 0
do_nothing_count = 0
max_action_count = 50
month_count = 0
while flag:
# query the server for the thermostat state and temperature
if cfg['Server']:
c = get_client(agent='172.17.0.1:28589', entity="thanos.ent")
else:
c = get_client()
archiver = DataClient(c)
uuids = [
cfg['UUIDS']['thermostat_temperature'],
cfg['UUIDS']['thermostat_state']
]
temp_now = self.current_time
start = '"' + (temp_now + timedelta(minutes=15) -
timedelta(days=(month_count) * 30)
).strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
end = '"' + (temp_now - timedelta(days=(month_count + 1) * 30)
).strftime('%Y-%m-%d %H:%M:%S') + ' PST"'
dfs = make_dataframe(
archiver.window_uuids(uuids, end, start, '15min', timeout=120))
for uid, df in dfs.items():
if uid == uuids[0]:
if 'mean' in df.columns:
df = df[['mean']]
df.columns = ['tin']
elif uid == uuids[1]:
if 'max' in df.columns:
df = df[['max']]
df.columns = ['a']
dfs[uid] = df.resample('15min').mean()
df = pd.concat([dframe for uid, dframe in dfs.items()], axis=1)
df['a1'] = df.apply(f1, axis=1)
df['a2'] = df.apply(f2, axis=1)
df['tin'] = df['tin'].replace(to_replace=0, method='pad')
df['temp_next'] = df['tin'].shift(-1)
df = df.dropna()
for idx in reversed(df.index):
if df.at[idx, 'a1'] == 1 and heating_count <= max_action_count:
if df.at[idx, 'tin'] <= df.at[idx, 'temp_next']:
final_df = final_df.append(df[df.index == idx])
heating_count += 1
elif df.at[idx,
'a2'] == 1 and cooling_count < max_action_count:
if df.at[idx, 'tin'] >= df.at[idx, 'temp_next']:
final_df = final_df.append(df[df.index == idx])
cooling_count += 1
elif df.at[idx, 'a1'] == 0 and df.at[
idx,
'a2'] == 0 and do_nothing_count < max_action_count:
final_df = final_df.append(df[df.index == idx])
do_nothing_count += 1
if heating_count>=max_action_count and cooling_count>=max_action_count\
and do_nothing_count>=max_action_count:
flag = False
break
month_count += 1
return shuffle(final_df)