Set Up



In [74]:

    
# Will allow us to embed images in the notebook
%matplotlib inline

Import Energy Data

Energy data can be obtained from your DNSP as a NEM12 file.



In [75]:

    
# Import NEM12 data
from nemreader import output_as_data_frames
file_path = '../examples/example.csv'
dfs = output_as_data_frames(file_path)
nmi, df = dfs[0] # Get data for first NMI in file
nmi = '*Redacted*'

Timing Considerations

Energy billing data is reported by the meter at the end of the interval period.

The energy reading on 2020-01-01T00:00 is actually for the last interval of the previous day/month/year. That is 2019-12-31T23:30 to 2020-01-01T00:00 for a 30min interval reading.

So for analysis we want to report on interval start to ensure values are grouped properly.



In [76]:

    
df.set_index('period_start', inplace=True)
df.tail()









    Out[76]:







  
    
      
      period_end
      B1
      E1
      E2
      quality_method
      event
    
    
      period_start
      
      
      
      
      
      
    
  
  
    
      2020-05-04 21:30:00
      2020-05-04 22:00:00
      0.0
      0.10
      0.0
      A
      
    
    
      2020-05-04 22:00:00
      2020-05-04 22:30:00
      0.0
      0.18
      0.0
      A
      
    
    
      2020-05-04 22:30:00
      2020-05-04 23:00:00
      0.0
      0.15
      0.0
      A
      
    
    
      2020-05-04 23:00:00
      2020-05-04 23:30:00
      0.0
      0.13
      0.0
      A
      
    
    
      2020-05-04 23:30:00
      2020-05-05 00:00:00
      0.0
      0.13
      0.0
      A

Plot NMI Data



In [77]:

    
#Show quick daily summary
import pandas as pd
import calplot
calplot.calplot(pd.Series(df.E1), daylabels='MTWTFSS')









    Out[77]:





(<Figure size 864x244.8 with 3 Axes>,
 array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f82ca242cd0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f82c99864c0>],
       dtype=object))



In [78]:

    
# Technically we should probably use a bar chart since they are totals
month_totals = df.groupby(df.index.strftime('%Y-%m')).sum()
month_totals.plot(title=nmi+" Monthly kWh", kind='bar', y=['E1'])









    Out[78]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f82c95b9880>



In [79]:

    
# But a line chart resampled by month shows better date axis labels
month_totals = df.resample('M').sum()
month_totals.plot(title=nmi+" Monthly kWh", y=['E1'])









    Out[79]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f82c950e940>



In [80]:

    
day_totals = df.resample('W').sum()
day_totals.plot(title=nmi+" Weekly kWh", y=['E1'])









    Out[80]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f82ca21df10>

Daily Profiles



In [81]:

    
hourly_avg = df.groupby(df.index.strftime('%H:%M')).mean()
hourly_avg.plot(title=nmi+" Avg Daily Profile", y=['E1'])









    Out[81]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f82ca240a60>



In [82]:

    
# Filter by summer months only 
summer_df = df.loc[df.index.month.isin([11, 12, 1, 2])]
summer_avg = summer_df.groupby(summer_df.index.strftime('%H:%M')).mean()
summer_avg.plot(title=nmi+" Avg Daily Profile (Hot Months)", y=['E1'])









    Out[82]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f82ca5b1820>



In [83]:

    
# Filter by winter months only 
winter_df = df.loc[df.index.month.isin([5, 6, 7, 8, 9])]
winter_avg = winter_df.groupby(winter_df.index.strftime('%H:%M')).mean()
winter_avg.plot(title=nmi+" Avg Daily Profile (Cold Months)", y=['E1'])









    Out[83]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f82ce7661c0>

	period_end	B1	E1	E2	quality_method
period_start
2020-05-04 21:30:00	2020-05-04 22:00:00	0.0	0.10	0.0	A
2020-05-04 22:00:00	2020-05-04 22:30:00	0.0	0.18	0.0	A
2020-05-04 22:30:00	2020-05-04 23:00:00	0.0	0.15	0.0	A
2020-05-04 23:00:00	2020-05-04 23:30:00	0.0	0.13	0.0	A
2020-05-04 23:30:00	2020-05-05 00:00:00	0.0	0.13	0.0	A