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Hart Algorithm Example





In [2]:



    


%matplotlib inline



    











In [3]:



    


from __future__ import print_function
import numpy as np
import pandas as pd
from pylab import rcParams
import matplotlib.pyplot as plt

rcParams['figure.figsize'] = (13, 6)



    











In [4]:



    


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 Data from HDF file





In [7]:



    


data = DataSet('../datasets/REDD/low_freq.h5')
print('Loaded', len(data.buildings), 'buildings')



    


















    






Loaded 6 buildings










    






C:\Users\diego\anaconda3\envs\doutorado\lib\site-packages\nilm_metadata\file_management.py:16: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
  objs = yaml.load(fh)

Loading data for Building 1





In [8]:



    


elec = data.buildings[1].elec



    











In [9]:



    


elec.get_timeframe()



    


















    
Out[9]:








TimeFrame(start='2011-04-18 09:22:09-04:00', end='2011-05-24 15:57:02-04:00', empty=False)

Set a window





In [10]:



    


data.set_window(start='2011-04-20',end='2011-04-24')
elec_1 = data.buildings[1].elec

Mains and Submeters Data





In [11]:



    


mains=elec_1.mains()
mains



    


















    
Out[11]:








MeterGroup(meters=
  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])
  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])
)

















In [12]:



    


mains.available_ac_types('power')



    


















    
Out[12]:








['apparent']

















In [13]:



    


submeters=elec_1.submeters()
submeters



    


















    
Out[13]:








MeterGroup(meters=
  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])
  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])
  ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])
  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])
  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])
  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])
  ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])
  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])
  ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])
  ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])
  ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])
  ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])
  ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])
  ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])
  MeterGroup(meters=
    ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])
    ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])
  )
  MeterGroup(meters=
    ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])
    ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])
  )
)

















In [14]:



    


submeters.available_ac_types('power')



    


















    
Out[14]:








['active']

Running HART_85





In [15]:



    


from nilmtk.disaggregate.hart_85 import Hart85
h = Hart85()

Please set columns accordingly

For REDD, since mains is 'Apparent Power'

For IAWE, you may set columns to columns= [('power','active')] or to columns= [('power','active'),('power','reactive')]

Train the model





In [16]:



    


h.train(mains,columns=[('power','apparent')])



    


















    






Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Finding Edges, please wait ...
Edge detection complete.
Creating transition frame ...
Transition frame created.
Creating states frame ...
States frame created.
Finished.

Save Model using export_model





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#h.export_model('model.pickle')

Import model after saving





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#h.import_model('model.pickle')



    











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output = HDFDataStore('output.h5', 'w')
df=h.disaggregate(mains, output)



    


















    






C:\Users\diego\Desktop\Doutorado\nilmtk\nilmtk\metergroup.py:935: UserWarning: As a quick implementation we only get Good Sections from the first meter in the meter group.  We should really return the intersection of the good sections for all meters.  This will be fixed...
  warnings.warn("As a quick implementation we only get Good Sections from"










    






Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Finding Edges, please wait ...
Edge detection complete.
Creating transition frame ...
Transition frame created.
Creating states frame ...
States frame created.
Finished.
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Finding Edges, please wait ...
Edge detection complete.
Creating transition frame ...
Transition frame created.
Creating states frame ...
States frame created.
Finished.
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Finding Edges, please wait ...
Edge detection complete.
Creating transition frame ...
Transition frame created.
Creating states frame ...
States frame created.
Finished.
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Finding Edges, please wait ...
Edge detection complete.
Creating transition frame ...
Transition frame created.
Creating states frame ...
States frame created.
Finished.
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Finding Edges, please wait ...
Edge detection complete.
Creating transition frame ...
Transition frame created.
Creating states frame ...
States frame created.
Finished.
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Finding Edges, please wait ...
Edge detection complete.
Creating transition frame ...
Transition frame created.
Creating states frame ...
States frame created.
Finished.
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Next Chunk..
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Next Chunk..
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Next Chunk..
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Next Chunk..
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Next Chunk..
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Next Chunk..
Appending mains data to datastore
Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Done

















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# Uncomment it for closing the Output HDFDataStore
# output.close()

Returned Disaggregated Dataframe





In [18]:



    


df.tail()



    


















    
Out[18]:











  
    

      
      0
      1
      2
    

  
  
    

      2011-04-23 23:55:00-04:00
      209
      0
      0
    

    

      2011-04-23 23:56:00-04:00
      209
      0
      0
    

    

      2011-04-23 23:57:00-04:00
      209
      0
      0
    

    

      2011-04-23 23:58:00-04:00
      209
      0
      0
    

    

      2011-04-23 23:59:00-04:00
      0
      0
      0

Since Hart is unsupervised, Find best matched appliances to disaggregated output.





In [19]:



    


h.best_matched_appliance(submeters,df)



    


















    






Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     
Done loading data all meters for this chunk.
Best Matched Pair is (0, ElecMeterID(instance=5, building=1, dataset='REDD'))
Best Matched Pair is (1, ElecMeterID(instance=6, building=1, dataset='REDD'))
Best Matched Pair is (2, MeterGroupID(meters=(ElecMeterID(instance=10, building=1, dataset='REDD'), ElecMeterID(instance=20, building=1, dataset='REDD'))))

So it shows column 0's appliance best matches with Fridge, 1-> dish washer, 2-> washer dryer





In [20]:



    


elec



    


















    
Out[20]:








MeterGroup(meters=
  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])
  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])
  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])
  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])
  ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])
  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])
  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])
  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])
  ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])
  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])
  ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])
  ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])
  ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])
  ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])
  ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])
  ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])
  MeterGroup(meters=
    ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])
    ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])
  )
  MeterGroup(meters=
    ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])
    ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])
  )
)

Comparing for Fridge





In [27]:



    


# First we need to take intersection of indices of dataframes of fridge and predicted (Inner Join)
df_fridge = next(elec_1['fridge', 1].load())
merged_df = pd.merge(pd.DataFrame(df[0]), df_fridge, left_index=True, right_index=True)



    


















    






C:\Users\diego\anaconda3\envs\doutorado\lib\site-packages\pandas\core\reshape\merge.py:544: UserWarning: merging between different levels can give an unintended result (1 levels on the left, 2 on the right)
  warnings.warn(msg, UserWarning)

















In [28]:



    


merged_df.head()



    


















    
Out[28]:











  
    

      
      0
      (power, active)
    

  
  
    

      2011-04-22 22:49:00-04:00
      0
      6.0
    

    

      2011-04-22 22:54:00-04:00
      0
      7.0
    

    

      2011-04-22 22:55:00-04:00
      0
      7.0
    

    

      2011-04-22 23:00:00-04:00
      0
      7.0
    

    

      2011-04-22 23:01:00-04:00
      0
      6.0
    

  




















In [29]:



    


merged_df[0].plot(c='r')
merged_df['power', 'active'].plot()
plt.legend(["Predicted", "Ground truth"]);
plt.ylabel("Power (W)")
plt.xlabel("Time");

Comparing for Washer Dryer





In [30]:



    


df_dish_washer = next(elec_1['washer dryer', 1].load())
merged_df = pd.merge(pd.DataFrame(df[2]), df_fridge, left_index=True, right_index=True)



    


















    






Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     
Done loading data all meters for this chunk.










    






C:\Users\diego\anaconda3\envs\doutorado\lib\site-packages\pandas\core\reshape\merge.py:544: UserWarning: merging between different levels can give an unintended result (1 levels on the left, 2 on the right)
  warnings.warn(msg, UserWarning)

















In [31]:



    


merged_df.head()



    


















    
Out[31]:











  
    

      
      2
      (power, active)
    

  
  
    

      2011-04-22 22:49:00-04:00
      0
      6.0
    

    

      2011-04-22 22:54:00-04:00
      0
      7.0
    

    

      2011-04-22 22:55:00-04:00
      0
      7.0
    

    

      2011-04-22 23:00:00-04:00
      0
      7.0
    

    

      2011-04-22 23:01:00-04:00
      0
      6.0
    

  




















In [32]:



    


ax1 = merged_df[2].plot(c='r')
ax2 = merged_df['power', 'active'].plot(c='grey')
ax1.legend(["Predicted", "Ground truth"])
plt.ylabel("Power (W)")
plt.xlabel("Time");



    


















    
























In [ ]:

Content source: diegocavalca/Studies

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