In [22]:

    

%matplotlib inline
import eemeter


    

In [2]:

    

>>> eemeter.samples()


    

    
Out[2]:





['il-electricity-cdd-hdd-billing_bimonthly',
 'il-electricity-cdd-hdd-billing_monthly',
 'il-electricity-cdd-hdd-daily',
 'il-electricity-cdd-hdd-hourly',
 'il-electricity-cdd-only-billing_bimonthly',
 'il-electricity-cdd-only-billing_monthly',
 'il-electricity-cdd-only-daily',
 'il-electricity-cdd-only-hourly',
 'il-gas-hdd-only-billing_bimonthly',
 'il-gas-hdd-only-billing_monthly',
 'il-gas-hdd-only-daily',
 'il-gas-hdd-only-hourly',
 'il-gas-intercept-only-billing_bimonthly',
 'il-gas-intercept-only-billing_monthly',
 'il-gas-intercept-only-daily',
 'il-gas-intercept-only-hourly']

In [3]:

    

>>> meter_data, temperature_data, metadata = \
...     eemeter.load_sample('il-electricity-cdd-hdd-daily')
>>> print(meter_data.head())


    

    
Out[3]:







  

    

      

      
value
    
    

      
start
      

    
  
  

    

      
2015-11-22 00:00:00+00:00
      
32.34
    
    

      
2015-11-23 00:00:00+00:00
      
23.80
    
    

      
2015-11-24 00:00:00+00:00
      
26.26
    
    

      
2015-11-25 00:00:00+00:00
      
21.32
    
    

      
2015-11-26 00:00:00+00:00
      
6.70
    
  

In [5]:

    

>>> data = eemeter.merge_temperature_data(meter_data, temperature_data)
>>> print(data.head())


    

    
Out[5]:







  

    

      

      
meter_value
      
temperature_mean
    
  
  

    

      
2015-11-22 00:00:00+00:00
      
32.34
      
26.740000
    
    

      
2015-11-23 00:00:00+00:00
      
23.80
      
38.831667
    
    

      
2015-11-24 00:00:00+00:00
      
26.26
      
41.304583
    
    

      
2015-11-25 00:00:00+00:00
      
21.32
      
49.198333
    
    

      
2015-11-26 00:00:00+00:00
      
6.70
      
57.856667
    
  

In [6]:

    

>>> data = eemeter.merge_temperature_data(
...     meter_data, temperature_data, temperature_mean=False,
...     data_quality=True)
>>> print(data.head())


    

    
Out[6]:







  

    

      

      
meter_value
      
temperature_not_null
      
temperature_null
    
    

      
start
      

      

      

    
  
  

    

      
2015-11-22 00:00:00+00:00
      
32.34
      
18
      
0.0
    
    

      
2015-11-23 00:00:00+00:00
      
23.80
      
24
      
0.0
    
    

      
2015-11-24 00:00:00+00:00
      
26.26
      
24
      
0.0
    
    

      
2015-11-25 00:00:00+00:00
      
21.32
      
24
      
0.0
    
    

      
2015-11-26 00:00:00+00:00
      
6.70
      
24
      
0.0
    
  

In [9]:

    

>>> data = eemeter.merge_temperature_data(
...     meter_data, temperature_data, temperature_mean=False,
...     heating_balance_points=[60, 61], cooling_balance_points=[70])
>>> print(data.head())


    

    




                           meter_value  cdd_70     hdd_60     hdd_61  \
2015-11-22 00:00:00+00:00        32.34     0.0  33.260000  34.260000   
2015-11-23 00:00:00+00:00        23.80     0.0  21.168333  22.168333   
2015-11-24 00:00:00+00:00        26.26     0.0  18.695417  19.695417   
2015-11-25 00:00:00+00:00        21.32     0.0  10.801667  11.801667   
2015-11-26 00:00:00+00:00         6.70     0.0   2.143333   3.143333   

                           n_days_dropped  n_days_kept  
2015-11-22 00:00:00+00:00             0.0          1.0  
2015-11-23 00:00:00+00:00             0.0          1.0  
2015-11-24 00:00:00+00:00             0.0          1.0  
2015-11-25 00:00:00+00:00             0.0          1.0  
2015-11-26 00:00:00+00:00             0.0          1.0  

In [17]:

    

>>> import datetime
>>> import pytz
>>> datetime.datetime(2016, 12, 26, 0, 0, tzinfo=pytz.UTC)
>>> baseline_data, warnings = eemeter.get_baseline_data(
...     data, end=baseline_end_date, max_days=365)
>>> print(baseline_data.head())


    

    




                           meter_value  cdd_70     hdd_60     hdd_61  \
2015-12-27 00:00:00+00:00        25.55     0.0  18.093333  19.093333   
2015-12-28 00:00:00+00:00        26.46     0.0  22.478333  23.478333   
2015-12-29 00:00:00+00:00        30.38     0.0  25.003333  26.003333   
2015-12-30 00:00:00+00:00        49.82     0.0  29.161667  30.161667   
2015-12-31 00:00:00+00:00        34.47     0.0  29.572917  30.572917   

                           n_days_dropped  n_days_kept  
2015-12-27 00:00:00+00:00             0.0          1.0  
2015-12-28 00:00:00+00:00             0.0          1.0  
2015-12-29 00:00:00+00:00             0.0          1.0  
2015-12-30 00:00:00+00:00             0.0          1.0  
2015-12-31 00:00:00+00:00             0.0          1.0  

In [20]:

    

>>> import json
>>> model_results = eemeter.caltrack_method(data)
>>> print(json.dumps(model_results.json(), indent=2))


    

    




{
  "status": "SUCCESS",
  "method_name": "caltrack_method",
  "model": {
    "model_type": "cdd_hdd",
    "formula": "meter_value ~ cdd_70 + hdd_60",
    "status": "QUALIFIED",
    "model_params": {
      "intercept": 11.751623378135884,
      "beta_cdd": 3.070146725078682,
      "beta_hdd": 0.8447599217224312,
      "cooling_balance_point": 70,
      "heating_balance_point": 60
    },
    "r_squared": 0.7294645737524558,
    "warnings": []
  },
  "r_squared": 0.7294645737524558,
  "warnings": [],
  "metadata": {},
  "settings": {
    "fit_cdd": true,
    "minimum_non_zero_cdd": 10,
    "minimum_non_zero_hdd": 10,
    "minimum_total_cdd": 20,
    "minimum_total_hdd": 20,
    "beta_cdd_maximum_p_value": 1,
    "beta_hdd_maximum_p_value": 1
  }
}

In [21]:

    

>>> model_results.r_squared


    

    
Out[21]:





0.7294645737524558

In [24]:

    

model_results.plot()


    

    
Out[24]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f190457c128>






    

In [18]:

    

! eemeter caltrack --sample=il-electricity-cdd-only-billing_monthly --output-file=/tmp/out.json


    

    




Loading sample: il-electricity-cdd-only-billing_monthly
Output written: /tmp/out.json

In [25]:

    

>>> eemeter.plot_energy_signature(meter_data, temperature_data)


    

    
Out[25]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f190467a2b0>






    

In [26]:

    

>>> eemeter.plot_time_series(meter_data, temperature_data)


    

    
Out[26]:





(<matplotlib.axes._subplots.AxesSubplot at 0x7f1904432390>,
 <matplotlib.axes._subplots.AxesSubplot at 0x7f1904615160>)






    

In [27]:

    

>>> ax = eemeter.plot_energy_signature(meter_data, temperature_data)
>>> model_results.plot(ax=ax, with_candidates=True)


    

    
Out[27]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f1901e3a9b0>






    

In [33]:

    

>>> model_results.model.plot()


    

    
Out[33]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f1900454f60>






    

In [ ]: