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Getting Started ***************
Import the energiscore library and other dependencies. 




In [19]:



    


import energistream as es # Imports the Energiscore Library
import pandas as pd # Imports the Pandas Library for Dataframe handling
import datetime as dt # Imports the datetime Library for timeseries handling
import numpy as np # Imports the Numpy Library for series handling
import scipy as sp # Imports scipy package

import seaborn as sns # Imports seaborn for graph generation
import matplotlib # Imports matplotlib for math functionality



    











In [20]:



    


%load_ext autoreload 
%autoreload 2 
# Running autoreload will reload any updated modules before running a cell. 
%pylab inline 
%matplotlib inline 
#pylab inline and matplotlib inline will cause charts to be displayed within ipython notebook.



    


















    






The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload
Populating the interactive namespace from numpy and matplotlib
Client Instantiation and Authentication ***************************************
``EnergiStreamClient`` takes a valid username and password and establishes a unique session with temporary credentials. 




In [141]:



    


stream = es.EnergiStreamClient('USER', 'PASS', include_sensors=True)
External Data *************
``get_weather`` returns a dataframe of hourly temperature and relative humidity data for the instantiated client's associated physical location. Note : This data is collected from third party sources. Weather ID is an energistream unique key and not associated to any third party ID schema. 




In [22]:



    


stream.get_weather(weather_id = 102, start = '12/29/2014', end = '1/29/2015').head()



    


















    
Out[22]:










  
    

      
      c
      f
      h
    

  
  
    

      2014-12-29 00:00:00+00:00
       15.6
       60
       42
    

    

      2014-12-29 01:00:00+00:00
       15.0
       59
       48
    

    

      2014-12-29 02:00:00+00:00
       13.3
       56
       55
    

    

      2014-12-29 03:00:00+00:00
       13.3
       56
       53
    

    

      2014-12-29 04:00:00+00:00
       12.2
       54
       57
Energistream Data and Metadata ******************************
``get_energy`` accepts a sensor ID and returns a dataframe relating active and reactive energy, current and voltage RMS, and total Energy. 




In [23]:



    


stream.get_energy(3505, start = '12/29/2014', end = '1/29/2015', tz = 'local').head()



    


















    
Out[23]:










  
    

      
      activeEnergy
      currentRMS
      powerFactor
      reactiveEnergy
      sensorId
      totalEnergy
      voltageRMS
    

  
  
    

      2014-12-29 00:01:00-08:00
       43566.626
       27.44
       0.93
       13801.665
       3505
       45700.513
       490.52
    

    

      2014-12-29 00:02:00-08:00
       43566.850
       27.74
       0.94
       13801.740
       3505
       45700.749
       490.00
    

    

      2014-12-29 00:03:00-08:00
       43567.077
       30.02
       0.93
       13801.816
       3505
       45700.989
       489.29
    

    

      2014-12-29 00:04:00-08:00
       43567.310
       28.41
       0.93
       13801.901
       3505
       45701.236
       491.54
    

    

      2014-12-29 00:05:00-08:00
       43567.541
       27.81
       0.94
       13801.986
       3505
       45701.482
       491.18
``search_group_tree`` accepts a keyword and searches the instantiated client for matching sensor groups returning group names, sensor group ID, and assigned sensors. 




In [24]:



    


stream.search_group_tree('Engineering',case=False).head()



    


















    
Out[24]:










  
    

      
      name
      description
      load_type
      sensorGroups
      sensors
      groupMultiplier
      time_zone
      weatherStationId
      base_group_level
    

    

      sensorGroupId
      
      
      
      
      
      
      
      
      
    

  
  
    

      2177
             Rockwell Engineering Center - Sub Groups
             Rockwell Engineering Center - Sub Groups
       building
           Int64Index([2179, 2178, 2180], dtype='int64')
                                                    None
                         [1, 1, 1]
       America/Los_Angeles
       102
       1
    

    

      2258
       Engineering and Computing Trailer - Sub Groups
       Engineering and Computing Trailer - Sub Groups
       building
                                                    None
       [{u'timeZoneId': 67, u'sourceTypeId': 1, u'des...
                              None
       America/Los_Angeles
       102
       1
    

    

      157 
         Engineering Laboratory Facility - Sub Groups
                                Feed to ELF Sub Loads
       building
       Int64Index([196, 193, 192, 195, 194], dtype='i...
                                                    None
                   [1, 1, 1, 1, 1]
       America/Los_Angeles
       102
       1
    

    

      255 
                       Engineering Tower - Sub Groups
                                Feed to ET Sub Groups
       building
       Int64Index([258, 256, 257, 262, 263, 260, 261,...
                                                    None
       [1, 1, 1, 1, 1, 1, 1, 1, 1]
       America/Los_Angeles
       102
       1
    

    

      265 
                Engineering Lecture Hall - Sub Groups
                                 Feed ELH Sub Groups 
       building
       Int64Index([274, 266, 271, 267, 270, 273, 272]...
                                                    None
             [1, 1, 1, 3, 3, 3, 3]
       America/Los_Angeles
       102
       1
Sensors are grouped in a heirarchy by level. 




In [25]:



    


stream.groups[stream.groups.base_group_level == 0].head()



    


















    
Out[25]:










  
    

      
      name
      description
      load_type
      sensorGroups
      sensors
      groupMultiplier
      time_zone
      weatherStationId
      base_group_level
    

    

      sensorGroupId
      
      
      
      
      
      
      
      
      
    

  
  
    

      216 
              UCI - MelRok Electric Meter Sub Groups
               Feed to Individual Loads at UCI Buildings
       campus
       Int64Index([2203, 2193, 2198, 2188, 2177, 2181...
       None
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
       America/Los_Angeles
       102
       0
    

    

      734 
                   UCI - Electric Distribution Loads
       Electrical Meters that Distribute Electricity ...
       campus
       Int64Index([732, 733, 1836, 1837, 1838, 1839, ...
       None
                            [1, 1, 1, 1, 1, 1, -1, 1, 1]
       America/Los_Angeles
       102
       0
    

    

      769 
       UCI - Building Heating & Chilled Water Meters
                            14 KEP-ES749 for 6 Buildings
       campus
       Int64Index([774, 775, 772, 771, 776, 777, 1824...
       None
                             [1, 1, 1, 1, 1, 1, 1, 1, 1]
       America/Los_Angeles
       102
       0
    

    

      1843
                           UCI - Total Electric Load
                      Total Electric Load for UCI Campus
       campus
       Int64Index([735, 1842, 1484, 1483, 1486, 1485,...
       None
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
       America/Los_Angeles
       102
       0
``sensors`` returns a dataframe describing the individual sensors associated with the instantiated energistream client ID. This includes the sensor ID, the associated group ID, time zone, and a multiplier based on the method of measurement i.e. one, two, or three channel. 




In [26]:



    


stream.sensors.head()



    


















    
Out[26]:










  
    

      
      description
      iconId
      multiplier
      name
      properties
      sensorFunctionTypeId
      sensorGroupId
      sourceTypeId
      time_zone
    

    

      sensorId
      
      
      
      
      
      
      
      
      
    

  
  
    

      8812
       D40010000628-04
       21
       1
       ECT B1
       [{u'name': u'Phase', u'value': u'A'}]
       1
       2258
       1
       America/Los_Angeles
    

    

      8813
       D40010000628-05
       21
       1
       ECT B2
       [{u'name': u'Phase', u'value': u'C'}]
       1
       2258
       1
       America/Los_Angeles
    

    

      8814
       D40010000628-06
       21
       1
       ECT B3
       [{u'name': u'Phase', u'value': u'B'}]
       1
       2258
       1
       America/Los_Angeles
    

    

      8815
       D40010000628-07
       21
       1
       ECT B4
       [{u'name': u'Phase', u'value': u'A'}]
       1
       2258
       1
       America/Los_Angeles
    

    

      8816
       D40010000628-08
       21
       1
       ECT B5
       [{u'name': u'Phase', u'value': u'C'}]
       1
       2258
       1
       America/Los_Angeles
``get_boards`` returns a dataframe describing the boards associated with the instantiated energistream client ID. This includes the boards serial number, version, display name, and model. 




In [29]:



    


stream.get_boards.head()



    


















    
Out[29]:










  
    

      
      currentConfigVersion
      displayName
      firmwareVersion
      manufacturer
      model
      sensors
      timeZoneId
    

    

      serialNumber
      
      
      
      
      
      
      
    

  
  
    

      C20006000002
       26
       MSTB First Floor 2
       1
       MelRok Manufacturer
       EN-12
       [{u'timeZoneId': 67, u'sourceTypeId': 1, u'nam...
       67
    

    

      C20006000003
       21
       MSTB First Floor 3
       1
       MelRok Manufacturer
       EN-12
       [{u'timeZoneId': 67, u'sourceTypeId': 1, u'nam...
       67
    

    

      C20006000004
       25
        MSTB Second Floor
       1
       MelRok Manufacturer
       EN-12
       [{u'timeZoneId': 67, u'sourceTypeId': 1, u'nam...
       67
    

    

      A30006000036
       20
             Aldrich Hall
       1
       MelRok Manufacturer
       EN-12
       [{u'timeZoneId': 67, u'sourceTypeId': 1, u'nam...
       67
    

    

      A30006000041
       18
       Social Science LAB
       1
       MelRok Manufacturer
       EN-12
       [{u'timeZoneId': 67, u'sourceTypeId': 1, u'nam...
       67
``get_demand`` returns the fifteen minute demand rate for the given sensor group. Start and end date may be specified as well as the timezone and desired resolution. Note : Defaults to the last thirty days and fifteen minute resolution. 




In [28]:



    


stream.get_demand(157).head()



    


















    
Out[28]:








2015-01-26 11:29:59-08:00    50.667600
2015-01-26 11:44:59-08:00    48.961200
2015-01-26 11:59:59-08:00    48.903466
2015-01-26 12:14:59-08:00    49.121333
2015-01-26 12:29:59-08:00    48.301666
Name: kW, dtype: float64
Presentation and Analysis Examples **********************************
seaborn is used to plot aesthetically pleasing charts and graphs. The following commands set up the presentation by defining the seaborn aesthetics, chart sizing, and figure scaling to be used. 




In [34]:



    


sns.set(font='Bitstream Vera Sans')
sns.set_context('poster')
#sns.set_context("poster", font_scale=1.7)
sns.plotting_context()



    


















    
Out[34]:








{'axes.labelsize': 17.6,
 'axes.titlesize': 19.200000000000003,
 'figure.figsize': array([ 12.8,   8.8]),
 'grid.linewidth': 1.6,
 'legend.fontsize': 16.0,
 'lines.linewidth': 2.8000000000000003,
 'lines.markeredgewidth': 0.0,
 'lines.markersize': 11.200000000000001,
 'patch.linewidth': 0.48,
 'xtick.labelsize': 16.0,
 'xtick.major.pad': 11.200000000000001,
 'xtick.major.width': 1.6,
 'xtick.minor.width': 0.8,
 'ytick.labelsize': 16.0,
 'ytick.major.pad': 11.200000000000001,
 'ytick.major.width': 1.6,
 'ytick.minor.width': 0.8}
A large dataset at a sufficiently fine resolution may contain very large short duration demand spikes which are not indicative of the actual demand. These spikes may be filtered out, carefully, by masking demand values that exceed a defined limit. 




In [144]:



    


demand = stream.get_demand(216)
multi = 1.5
mask = (demand < 0) | (demand > demand.mean() + multi * demand.std())
Seen below is the demand charted over a thirty day time period. This can provide insight into demand over time such as due to changes in the weather or building usage. Note the regularly spaced peaks during the work-day and comparatively smaller spikes which occur on off-days. There is a three day period of reduced demand coinciding with February 16th, a Federal Holiday. 




In [226]:



    


demand.mask(mask).plot()

plt.title('30 Day Demand for UCI - Melrok Loads')
plt.ylabel('Demand [kW]')



    


















    
Out[226]:








<matplotlib.text.Text at 0x1130b25d0>
Taking the mean and median of the days creates a single chart representing an average demand day. This is useful for examining a typical demand profile such as to obverve the time of peak loads and the duration of regular demand spikes. In this case the peak occurs in the middle of the afternoon around 3 PM. Comparing the mean and median offers quick insight into the daily variation in demand. 




In [282]:



    


mean = demand.mask(mask).groupby([demand.index.hour,demand.index.minute]).mean().plot(color = 'b', label = 'mean')
median = demand.mask(mask).groupby([demand.index.hour,demand.index.minute]).median().plot(color = 'g', label = 'median')
legend()

plt.title('Demand Profile for UCI - Melrok Loads')
plt.ylabel('Demand [kW]')

plt.xticks( (16 * arange(7)),['12AM','4AM','8AM','12PM','4PM','8PM','12AM'])



    


















    
Out[282]:








([<matplotlib.axis.XTick at 0x118450150>,
  <matplotlib.axis.XTick at 0x118444590>,
  <matplotlib.axis.XTick at 0x118653950>,
  <matplotlib.axis.XTick at 0x118658150>,
  <matplotlib.axis.XTick at 0x1186588d0>,
  <matplotlib.axis.XTick at 0x118659090>,
  <matplotlib.axis.XTick at 0x11865d750>],
 <a list of 7 Text xticklabel objects>)
A histogram is another useful way to examine the distribution of the demand, plotting demand magnitude by the total number of events in the data set. 




In [240]:



    


demand.mask(mask).hist(bins=80)

plt.title('Demand Histogram - Melrok Loads')
plt.ylabel('Number of Events')
plt.xlabel('Demand [kW]')



    


















    
Out[240]:








<matplotlib.text.Text at 0x11466c290>










    
























In [ ]:

Content source: Melrok/energistream-py

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