In [1]:
import numpy as np
import pandas as pd
from os.path import join
from pylab import rcParams
import matplotlib.pyplot as plt
%matplotlib inline
rcParams['figure.figsize'] = (16, 8)
import nilmtk
from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore
from nilmtk.disaggregate import CombinatorialOptimisation
from nilmtk.utils import print_dict
from nilmtk.metrics import f1_score
import warnings
warnings.filterwarnings("ignore")
The full data set can be downloaded from the remote WikiEnergy database. The credentials are omitted here for security reasons.
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# download_wikienergy(database_username, database_password, hdf_filename)
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data_dir = '/home/nipun/Downloads/'
we = DataSet(join(data_dir, 'wikienergy.h5'))
print('loaded ' + str(len(we.buildings)) + ' buildings')
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print_dict(we.metadata)
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building_number = 11
print_dict(we.buildings[building_number].metadata)
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elec = we.buildings[building_number].elec
elec.appliances
Out[6]:
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for m in elec.power_series_all_columns():
print(m, m.chunk)
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fridges = nilmtk.global_meter_group.select_using_appliances(type='fridge')
The energy consumed by each appliance can be expressed as a proportion of the household's total energy. Here we find the range of proportions for each fridge.
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# Select a subset of fridges, otherwise the computation takes a long time
fridges_restricted = MeterGroup(fridges.meters[:5])
proportion_per_fridge = fridges_restricted.proportion_of_upstream_total_per_meter()
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proportion_per_fridge.plot(kind='bar');
plt.title('fridge energy as proportion of total building energy');
plt.ylabel('proportion');
plt.xlabel('Fridge (<appliance instance>, <building instance>, <dataset name>)');
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# How much energy does the largest-consuming fridge consume in kWh?
fridges.select(building=61).total_energy()
Out[11]:
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fridges.select(building=61).plot();
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fridges_restricted = MeterGroup(fridges.meters[:20])
daily_energy = pd.DataFrame([meter.average_energy_per_period(offset_alias='D')
for meter in fridges_restricted.meters])
daily_energy.plot(kind='hist');
plt.title('Histogram of daily fridge energy');
plt.xlabel('energy (kWh)');
plt.ylabel('occurences');
plt.legend().set_visible(False)
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we.store.window = TimeFrame(start='2014-04-01 00:00:00-05:00', end='2014-04-02 00:00:00-05:00')
elec.plot();
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fraction = elec.submeters().fraction_per_meter().dropna()
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elec.clear_cache()
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elec.submeters().fraction_per_meter()
Out[17]:
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elec.submeters().fraction_per_meter()
Out[18]:
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# Create convenient labels
labels = elec.get_appliance_labels(fraction.index)
plt.figure(figsize=(8,8))
fraction.plot(kind='pie', labels=labels);
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elec.select_using_appliances(category='heating')
Out[20]:
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# Find all appliances with a particular type of motor
elec.select_using_appliances(category='single-phase induction motor')
Out[21]:
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# Train
co = CombinatorialOptimisation()
co.train(elec)
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for model in co.model:
print_dict(model)
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# Disaggregate
disag_filename = join(data_dir, 'wikienergy-disag.h5')
output = HDFDataStore(disag_filename, 'w')
co.disaggregate(elec.mains(), output)
output.close()
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disag = DataSet(disag_filename)
disag_elec = disag.buildings[building_number].elec
disag_elec.plot()
disag.store.close()
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disag = DataSet(disag_filename)
disag_elec = disag.buildings[building_number].elec
f1 = f1_score(disag_elec, elec)
f1.index = disag_elec.get_appliance_labels(f1.index)
f1.plot(kind='bar')
plt.xlabel('appliance');
plt.ylabel('f-score');
disag.store.close()
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