

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'] = (13, 6)
plt.style.use('ggplot')
import nilmtk
from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore
from nilmtk.disaggregate import CombinatorialOptimisation, fhmm_exact
from nilmtk.utils import print_dict
from nilmtk.metrics import f1_score

import warnings
warnings.filterwarnings("ignore")

Comparing FHMM and CO

Downloading data

The full data set can be downloaded from the remote WikiEnergy database. The credentials are omitted here for security reasons.



In [2]:

    
# download_wikienergy(database_username, database_password, hdf_filename)

Loading data



In [3]:

    
data_dir = '/Users/nipunbatra/Downloads/'
we = DataSet(join(data_dir, 'wikienergy.h5'))
print('loaded ' + str(len(we.buildings)) + ' buildings')









    



loaded 239 buildings



In [4]:

    
building_number = 11
print_dict(we.buildings[building_number].metadata)









    




instance: 11
dataset: WikiEnergy
original_name: 434

Examine sub-metered appliances



In [5]:

    
elec = we.buildings[building_number].elec
elec.appliances









    Out[5]:





[Appliance(type='fridge', instance=1),
 Appliance(type='dish washer', instance=1),
 Appliance(type='electric water heating appliance', instance=1),
 Appliance(type='spin dryer', instance=1),
 Appliance(type='electric furnace', instance=1),
 Appliance(type='sockets', instance=1),
 Appliance(type='sockets', instance=2),
 Appliance(type='air conditioner', instance=1),
 Appliance(type='sockets', instance=3),
 Appliance(type='sockets', instance=4)]

Wiring hierarchy of meters



In [6]:

    
elec.draw_wiring_graph();

Plot sub-metered data for a single day



In [7]:

    
we.set_window(start='2014-04-01 00:00:00', end='2014-04-02 00:00:00')
elec.plot();

Good sections



In [8]:

    
elec.mains().good_sections()









    Out[8]:





[TimeFrame(start='2014-04-01 00:00:00-05:00', end='2014-04-02 00:00:00-05:00', empty=False)]

Training and disaggregation

CO



In [9]:

    
# Train
co = CombinatorialOptimisation()
co.train(elec)









    



Training model for submeter 'ElecMeter(instance=2, building=11, dataset='WikiEnergy', appliances=[Appliance(type='air conditioner', instance=1)])'
Training model for submeter 'ElecMeter(instance=3, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=1)])'
Training model for submeter 'ElecMeter(instance=4, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=2)])'
Training model for submeter 'ElecMeter(instance=5, building=11, dataset='WikiEnergy', appliances=[Appliance(type='dish washer', instance=1)])'
Training model for submeter 'ElecMeter(instance=6, building=11, dataset='WikiEnergy', appliances=[Appliance(type='spin dryer', instance=1)])'
Training model for submeter 'ElecMeter(instance=7, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric furnace', instance=1)])'
Training model for submeter 'ElecMeter(instance=8, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=3)])'
Training model for submeter 'ElecMeter(instance=9, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=4)])'
Training model for submeter 'ElecMeter(instance=10, building=11, dataset='WikiEnergy', appliances=[Appliance(type='fridge', instance=1)])'
Training model for submeter 'ElecMeter(instance=11, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric water heating appliance', instance=1)])'
Done training!



In [10]:

    
# Disaggregate
disag_filename = join(data_dir, 'wikienergy-disag-co.h5')
output = HDFDataStore(disag_filename, 'w')
co.disaggregate(elec.mains(), output)
output.close()









    



vampire_power = 321.0 watts
Estimating power demand for 'ElecMeter(instance=2, building=11, dataset='WikiEnergy', appliances=[Appliance(type='air conditioner', instance=1)])'
Estimating power demand for 'ElecMeter(instance=3, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=1)])'
Estimating power demand for 'ElecMeter(instance=4, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=2)])'
Estimating power demand for 'ElecMeter(instance=5, building=11, dataset='WikiEnergy', appliances=[Appliance(type='dish washer', instance=1)])'
Estimating power demand for 'ElecMeter(instance=6, building=11, dataset='WikiEnergy', appliances=[Appliance(type='spin dryer', instance=1)])'
Estimating power demand for 'ElecMeter(instance=7, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric furnace', instance=1)])'
Estimating power demand for 'ElecMeter(instance=8, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=3)])'
Estimating power demand for 'ElecMeter(instance=9, building=11, dataset='WikiEnergy', appliances=[Appliance(type='sockets', instance=4)])'
Estimating power demand for 'ElecMeter(instance=10, building=11, dataset='WikiEnergy', appliances=[Appliance(type='fridge', instance=1)])'
Estimating power demand for 'ElecMeter(instance=11, building=11, dataset='WikiEnergy', appliances=[Appliance(type='electric water heating appliance', instance=1)])'

FHMM



In [11]:

    
%%capture
# Train
fhmm = fhmm_exact.FHMM()
fhmm.train(elec)
# Disaggregate
disag_filename = join(data_dir, 'wikienergy-disag-fhmm.h5')
output = HDFDataStore(disag_filename, 'w')
fhmm.disaggregate(elec.mains(), output)
output.close()

Examine disaggregated data

CO



In [12]:

    
disag_filename = join(data_dir, 'wikienergy-disag-co.h5')
disag = DataSet(disag_filename)
disag_elec = disag.buildings[building_number].elec
disag_elec.plot()
disag.store.close()
plt.title("CO");

Calculate accuracy of disaggregation



In [13]:

    
disag = DataSet(disag_filename)
disag_elec = disag.buildings[building_number].elec

f1 = f1_score(disag_elec, elec)
f1.index = disag_elec.get_labels(f1.index)
f1.plot(kind='barh')
plt.ylabel('appliance');
plt.xlabel('f-score');
plt.title("CO");

disag.store.close()



In [14]:

    
disag_filename = join(data_dir, 'wikienergy-disag-fhmm.h5')
disag = DataSet(disag_filename)
disag_elec = disag.buildings[building_number].elec
disag_elec.plot()
plt.title("FHMM");
disag.store.close()



In [15]:

    
disag = DataSet(disag_filename)
disag_elec = disag.buildings[building_number].elec

f1 = f1_score(disag_elec, elec)
f1.index = disag_elec.get_labels(f1.index)
f1.plot(kind='barh')
plt.ylabel('appliance');
plt.xlabel('f-score');
plt.title("FHMM");

disag.store.close()



In [17]:

    
# CSS styling
from IPython.core.display import display, HTML
display(HTML(open('static/styles.css', 'r').read()));