In [1]:
%matplotlib inline
import pandas as pd
data = pd.read_csv('aggregated_PV_data.csv', parse_dates=True, index_col=0)
data.head()
Out[1]:
111 112 113 114 121 122 123 124 131 132 ... 203 204 221 222 223 224 231 232 233 234
DT
2016-01-01 00:00:00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 0.000 0.000 ... 0.0 0.0 0.000 0.000 0.0 0.0 0.000 0.000 0.000 0.0
2016-01-01 00:15:00 0.003 0.005 0.004 0.004 0.004 0.003 0.008 0.0 0.008 0.008 ... 0.0 0.0 0.003 0.004 0.0 0.0 0.004 0.008 0.006 0.0
2016-01-01 00:30:00 0.006 0.010 0.007 0.008 0.009 0.005 0.016 0.0 0.016 0.015 ... 0.0 0.0 0.006 0.007 0.0 0.0 0.007 0.016 0.009 0.0
2016-01-01 00:45:00 0.009 0.015 0.007 0.012 0.014 0.007 0.022 0.0 0.024 0.023 ... 0.0 0.0 0.010 0.011 0.0 0.0 0.010 0.024 0.012 0.0
2016-01-01 01:00:00 0.011 0.020 0.007 0.016 0.019 0.007 0.029 0.0 0.032 0.030 ... 0.0 0.0 0.013 0.015 0.0 0.0 0.013 0.032 0.015 0.0

5 rows × 48 columns

In [2]:
data.plot(legend = False, figsize=(16, 8))
Out[2]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca1faefd90>
Out[2]:
In [3]:
%matplotlib inline
data['111'].plot(legend = False, figsize=(16, 8))
data['112'].plot(legend = False, figsize=(16, 8))
data['113'].plot(legend = False, figsize=(16, 8))
Out[3]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca1f1ff3d0>
Out[3]:
In [4]:
%matplotlib inline
data['121'].plot(legend = False, figsize=(16, 8))
data['122'].plot(legend = False, figsize=(16, 8))
data['123'].plot(legend = False, figsize=(16, 8))
data['124'].plot(legend = False, figsize=(16, 8))
Out[4]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca1f34e8d0>
Out[4]:
In [5]:
%matplotlib inline
data['131'].plot(legend = False, figsize=(16, 8))
data['132'].plot(legend = False, figsize=(16, 8))
data['133'].plot(legend = False, figsize=(16, 8))
data['134'].plot(legend = False, figsize=(16, 8))
Out[5]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca1f013850>
Out[5]:
In [6]:
%matplotlib inline
data['141'].plot(legend = False, figsize=(16, 8))
data['142'].plot(legend = False, figsize=(16, 8))
data['143'].plot(legend = False, figsize=(16, 8))
data['144'].plot(legend = False, figsize=(16, 8))
Out[6]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca19de1f50>
Out[6]:
In [7]:
%matplotlib inline
data['151'].plot(legend = False, figsize=(16, 8))
data['152'].plot(legend = False, figsize=(16, 8))
data['153'].plot(legend = False, figsize=(16, 8))
data['154'].plot(legend = False, figsize=(16, 8))
Out[7]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca1a118050>
Out[7]:
In [8]:
%matplotlib inline
data['161'].plot(legend = False, figsize=(16, 8))
data['162'].plot(legend = False, figsize=(16, 8))
data['163'].plot(legend = False, figsize=(16, 8))
data['164'].plot(legend = False, figsize=(16, 8))
Out[8]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca194e0890>
Out[8]:
In [9]:
%matplotlib inline
data['171'].plot(legend = False, figsize=(16, 8))
data['172'].plot(legend = False, figsize=(16, 8))
data['173'].plot(legend = False, figsize=(16, 8))
data['174'].plot(legend = False, figsize=(16, 8))
Out[9]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca18cac050>
Out[9]:
In [10]:
%matplotlib inline
data['181'].plot(legend = False, figsize=(16, 8))
data['182'].plot(legend = False, figsize=(16, 8))
data['183'].plot(legend = False, figsize=(16, 8))
data['184'].plot(legend = False, figsize=(16, 8))
Out[10]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca18815750>
Out[10]:
In [11]:
%matplotlib inline
data['191'].plot(legend = False, figsize=(16, 8))
data['192'].plot(legend = False, figsize=(16, 8))
data['193'].plot(legend = False, figsize=(16, 8))
data['194'].plot(legend = False, figsize=(16, 8))
Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca18260410>
Out[11]:
In [12]:
%matplotlib inline
data['201'].plot(legend = False, figsize=(16, 8))
data['202'].plot(legend = False, figsize=(16, 8))
data['203'].plot(legend = False, figsize=(16, 8))
data['204'].plot(legend = False, figsize=(16, 8))
Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca1819ef50>
Out[12]:
In [13]:
%matplotlib inline
data['221'].plot(legend = False, figsize=(16, 8))
data['222'].plot(legend = False, figsize=(16, 8))
data['223'].plot(legend = False, figsize=(16, 8))
data['224'].plot(legend = False, figsize=(16, 8))
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca135a30d0>
Out[13]:

repetitive candidates for futher inquery as street lights:

outliers for further inquery:

141 had 350, 7 times avg. 144 flat 0 energy 151-154 had issues, 2 0 energy usage, 2 had 350+ 202 had 250, 5 times avg. 223 at 0 energy use

Average energy use seems to fall in the shape of a double bell curve with two maximum usage groups centered around 70 and 35

In [14]:
%matplotlib inline
data['181'].diff()
Out[14]:
DT
2016-01-01 00:00:00      NaN
2016-01-01 00:15:00    0.003
2016-01-01 00:30:00    0.004
2016-01-01 00:45:00    0.005
2016-01-01 01:00:00    0.004
2016-01-01 01:15:00    0.005
2016-01-01 01:30:00    0.005
2016-01-01 01:45:00    0.004
2016-01-01 02:00:00    0.005
2016-01-01 02:15:00    0.005
2016-01-01 02:30:00    0.004
2016-01-01 02:30:59    0.001
2016-01-01 02:45:00    0.004
2016-01-01 03:00:00    0.005
2016-01-01 03:15:00    0.004
2016-01-01 03:30:00    0.005
2016-01-01 03:45:00    0.005
2016-01-01 04:00:00    0.001
2016-01-01 04:15:00    0.000
2016-01-01 04:30:00    0.000
2016-01-01 04:45:00    0.000
2016-01-01 05:00:00    0.000
2016-01-01 05:15:00    0.000
2016-01-01 05:30:00    0.000
2016-01-01 05:45:00    0.000
2016-01-01 06:00:00    0.001
2016-01-01 06:15:00    0.000
2016-01-01 06:30:00    0.000
2016-01-01 06:45:00    0.000
2016-01-01 07:00:00    0.000
                       ...  
2016-11-30 17:00:00    0.000
2016-11-30 17:15:00    0.000
2016-11-30 17:30:00    0.002
2016-11-30 17:45:00    0.001
2016-11-30 18:00:00    0.000
2016-11-30 18:15:00    0.000
2016-11-30 18:30:00    0.000
2016-11-30 18:45:00    0.000
2016-11-30 19:00:00    0.000
2016-11-30 19:15:00    0.000
2016-11-30 19:30:00    0.000
2016-11-30 19:45:00    0.000
2016-11-30 20:00:00    0.000
2016-11-30 20:15:00    0.000
2016-11-30 20:30:00    0.000
2016-11-30 20:45:00    0.000
2016-11-30 21:00:00    0.000
2016-11-30 21:15:00    0.000
2016-11-30 21:30:00    0.000
2016-11-30 21:45:00    0.000
2016-11-30 22:00:00    0.000
2016-11-30 22:00:59      NaN
2016-11-30 22:15:00      NaN
2016-11-30 22:30:00    0.000
2016-11-30 22:45:00    0.000
2016-11-30 23:00:00    0.000
2016-11-30 23:15:00    0.000
2016-11-30 23:30:00    0.000
2016-11-30 23:45:00    0.000
2016-12-01 00:00:00    0.000
Name: 181, dtype: float64

NEW GRAPH WITHOUT OUTLIERS BELOW

In [15]:
import matplotlib
matplotlib.use('nbagg')
import matplotlib.pyplot as plt

/projects/sage/sage/local/lib/python2.7/site-packages/matplotlib/__init__.py:1350: UserWarning:  This call to matplotlib.use() has no effect
because the backend has already been chosen;
matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.

  warnings.warn(_use_error_msg)

^ We were trying to import Python script in order to find the means of a couple of data points, but it did not work. ^

In [16]:
%matplotlib.plt
data['111'].plot(legend = False, figsize=(16, 8))
data['112'].plot(legend = False, figsize=(16, 8))
data['113'].plot(legend = False, figsize=(16, 8))
data['114'].plot(legend = False, figsize=(16, 8))
data['121'].plot(legend = False, figsize=(16, 8))
data['122'].plot(legend = False, figsize=(16, 8))
data['123'].plot(legend = False, figsize=(16, 8))
data['124'].plot(legend = False, figsize=(16, 8))
data['131'].plot(legend = False, figsize=(16, 8))
data['132'].plot(legend = False, figsize=(16, 8))
data['133'].plot(legend = False, figsize=(16, 8))
data['134'].plot(legend = False, figsize=(16, 8))

data['142'].plot(legend = False, figsize=(16, 8))
data['143'].plot(legend = False, figsize=(16, 8))


data['171'].plot(legend = False, figsize=(16, 8))
data['172'].plot(legend = False, figsize=(16, 8))
data['173'].plot(legend = False, figsize=(16, 8))
data['174'].plot(legend = False, figsize=(16, 8))
data['181'].plot(legend = False, figsize=(16, 8))
data['182'].plot(legend = False, figsize=(16, 8))
data['183'].plot(legend = False, figsize=(16, 8))
data['184'].plot(legend = False, figsize=(16, 8))
data['191'].plot(legend = False, figsize=(16, 8))
data['192'].plot(legend = False, figsize=(16, 8))
data['193'].plot(legend = False, figsize=(16, 8))
data['194'].plot(legend = False, figsize=(16, 8))
data['201'].plot(legend = False, figsize=(16, 8))

data['203'].plot(legend = False, figsize=(16, 8))
data['204'].plot(legend = False, figsize=(16, 8))
data['221'].plot(legend = False, figsize=(16, 8))
data['222'].plot(legend = False, figsize=(16, 8))

data['224'].plot(legend = False, figsize=(16, 8))
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca1291f990>
Out[16]:

Total amount of time that data was offline: 2,730 data points,Total time of recorded date: 32,616 data points, Percent of total time that data was offline: 8.37%,The system is at least 91.63% reliable as far as we know

In [17]:
%matplotlib inline
data['113'].diff(1)['2016-03-01':'2016-03-05'].plot(legend = False, figsize=(16, 8))
Out[17]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca0bca5690>
Out[17]:

finding total appliances/lighting: 5 applications at one time

In [18]:
%matplotlib inline
data['113'].diff(1)['2016-03-05':'2016-03-10'].plot(legend = False, figsize=(16, 8))
Out[18]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca1012f650>
Out[18]:

4 applications at one time

In [19]:
%matplotlib inline
data['113'].diff(1)['2016-03-10':'2016-03-15'].plot(legend = False, figsize=(16, 8))
Out[19]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca10307350>
Out[19]:

5 appliances at one time

In [20]:
%matplotlib inline
data['113'].diff(1)['2016-03-20':'2016-03-25'].plot(legend = False, figsize=(16, 8))
Out[20]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca100e9e90>
Out[20]:

4 appliances at one time

In [21]:
.001*1000
Out[21]:
1.0

They use three 1 watt appliances; most probably a mixture of 1 watt clock, led lightbulbs and phone charger. aside from that they use a single 3 watt appliance. This could likely be two things; either a clock or a double A battery charger which is exactly 3 watts to run.

In [0]:
In [22]:
%matplotlib inline
data['111'].plot(legend = False, figsize=(16, 8))
Out[22]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca0beb4f50>
Out[22]:
In [23]:
%matplotlib inline
data['153'].diff(1)['2016-08-10':'2016-08-15'].plot(legend = False, figsize=(16, 8))
Out[23]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca0bf0fb90>
Out[23]:

maximum of 6 appliances, three to four 1 watt appliances, a 2 watt appliance and rarely a 6 watt appliance

In [24]:
%matplotlib inline
data['113'].diff(1)['2016-08-10':'2016-08-12'].plot(legend = False, figsize=(16, 8))
Out[24]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca0b832f10>
Out[24]:
In [25]:
%matplotlib inline
data['141'].diff(1)['2016-06-10':'2016-06-15'].plot(legend = False, figsize=(16, 8))
Out[25]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca0b7a69d0>
Out[25]:
In [26]:
%matplotlib inline
data['141'].diff(1)['2016-05-10':'2016-05-12'].plot(legend = False, figsize=(16, 8))
Out[26]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca0ba1c610>
Out[26]:

This is one of the most energy using families-at times consuming 30 watts+, this is one of the families that could have a television. From the energy usage data it seems that less than 20% of households own a tv.

In [0]:

We have been looking at the data in lots of different ways, cumulative kwh yearly usage as well as looking at daily houshold power usage and comparing high and low usage household appliances. We found that the average monthly usage without the upper and lower outliers comes to around 3.0 kwh. We also found the minumim offline time of the overall grid and found that the grid is online at least 91.63% of the time. This is counting all missing data points as offline so the real online time of the grid is definitely above 91%, although is still does not compare to the US reliance of above 99% for our grid systems. The reliance could be closer to 99% but the data was not recorded. When looking at individual households we found that most houses use two to four 1 watt led light bulbs along with phone chargers, clocks, and radios. A smaller portion of the households, around 15% use higher amounts of power at once which leads us to believe that they own televisions which run on 25-30 watts. Most households power spikes occur in the evenings, with noticable light bulbs being turned on for 4-6 hour periods.

In [27]:
%matplotlib.plt
data['111']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['112']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['113']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['114']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['121']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['122']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['123']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['124']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['131']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['132']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['133']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['134']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))

data['142']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['143']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))


data['171']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['172']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['173']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['174']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['181']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['182']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['183']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['184']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['191']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['192']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['193']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['194']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['201']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))

data['203']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['204']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['221']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['222']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))

data['224']['2016-09-17':'2016-09-19'].plot(legend = False, figsize=(16, 8))
Out[27]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca0b991090>
Out[27]:

5.5 earthquake very close to Lake Sentani, 18th of september 2016

In [28]:
from IPython.display import Image
Image("eq1140301_2.png")
Out[28]:

This 5.5 magnitude earthquake took place on the eighteenth of September, 18, 2016 07:17 UTC very near the city of Jayapura.

In [29]:
from IPython.display import Image
Image("eq5_9.png")
Out[29]:
In [0]:

There was also this 5.9 magnitude earthquake september 17th 2016 also near Jayapura

In [30]:
%matplotlib.plt
data['111']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['112']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['113']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['114']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['121']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['122']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['123']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['124']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['131']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['132']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['133']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['134']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))

data['142']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['143']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))


data['171']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['172']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['173']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['174']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['181']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['182']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['183']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['184']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['191']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['192']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['193']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['194']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['201']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))

data['203']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['204']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['221']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
data['222']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))

data['224']['2016-05-18':'2016-05-19'].plot(legend = False, figsize=(16, 8))
Out[30]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca0b992ad0>
Out[30]:

small minute long outages, possibly eq relateed but very very minimal

In [31]:
%matplotlib.plt
data['111']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['112']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['113']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['114']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['121']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['122']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['123']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['124']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['131']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['132']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['133']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['134']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))

data['142']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['143']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))


data['171']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['172']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['173']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['174']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['181']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['182']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['183']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['184']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['191']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['192']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['193']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['194']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['201']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))

data['203']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['204']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['221']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
data['222']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))

data['224']['2016-09-20':'2016-09-22'].plot(legend = False, figsize=(16, 8))
Out[31]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca0b632190>
Out[31]:
In [0]:
In [0]:
In [32]:
%matplotlib.plt
data['111']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['112']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['113']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['114']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['121']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['122']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['123']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['124']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['131']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['132']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['133']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['134']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))

data['142']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['143']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))


data['171']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['172']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['173']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['174']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['181']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['182']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['183']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['184']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['191']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['192']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['193']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['194']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['201']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))

data['203']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['204']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['221']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
data['222']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))

data['224']['2016-09-10':'2016-09-19'].plot(legend = False, figsize=(16, 8))
Out[32]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca0b22f090>
Out[32]:
In [33]:
%matplotlib.plt
data['111'].plot(legend = False, figsize=(16, 8))
Out[33]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fca0b9d7cd0>
Out[33]:

The largest major outage runs from september 12th until september 14th, but one third of house holds seem to keep power, which makes us think it may be a collection malfunction.

In [34]:
Major earthquake we had to find actual timestamp as different weather data was in greenich mean time vs. our local time. none the less the grid was resiliant during the 18th and 19th during the powerful earthquake.

  File "<ipython-input-34-fe39fd2f8005>", line 1
    Major earthquake we had to find actual timestamp as different weather data was in greenich mean time vs. our local time. none the less the grid was resiliant during the 18th and 19th during the powerful earthquake.
                   ^
SyntaxError: invalid syntax
In [0]:
%matplotlib.plt
data['111']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['112']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['113']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['114']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['121']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['122']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['123']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['124']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['131']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['132']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['133']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['134']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))

data['142']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['143']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))

data['171']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['172']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['173']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['174']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['181']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['182']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['183']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['184']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['191']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['192']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['193']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['194']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['201']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))

data['203']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['204']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['221']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))
data['222']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))

data['224']['2016-10-18':'2016-10-23'].plot(legend = False, figsize=(16, 8))

We found that there was 0 sunlight for octobor 18th 2016 which corrosponds to the outages on the 19

large outage chuncks two thirds into octobor; looking for weather related causes

In [0]:
data['154'].plot(legend = False, figsize=(16, 8))
In [0]:
http://earthquaketrack.com/id-36-jayapura/recent
    
    average of over 40 a year
In [0]:
%matplotlib.plt
data['111']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['112']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['113']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['114']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['121']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['122']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['123']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['124']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['131']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['132']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['133']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['134']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))

data['142']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['143']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))

data['171']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['172']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['173']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['174']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['181']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['182']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['183']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['184']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['191']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['192']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['193']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['194']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['201']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))

data['203']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['204']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['221']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
data['222']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))

data['224']['2016-12-04':'2016-12-07'].plot(legend = False, figsize=(16, 8))
In [0]:
In [0]:
In [0]:
%matplot.plt
data['123']['2016-12-04':'2016-12-10'].plot(legend = False, figsize=(16, 8))
data['124']['2016-12-04':'2016-12-10'].plot(legend = False, figsize=(16, 8))
data['131']['2016-12-04':'2016-12-10'].plot(legend = False, figsize=(16, 8))
data['132']['2016-12-04':'2016-12-10'].plot(legend = False, figsize=(16, 8))
data['133']['2016-12-04':'2016-12-10'].plot(legend = False, figsize=(16, 8))
data['134']['2016-12-04':'2016-12-10'].plot(legend = False, figsize=(16, 8))


#NO DECEMBER DATA
In [0]:
%matplotlib.plt
data['111']['2016-12-02':'2016-12-22'].plot(legend = False, figsize=(16, 8))
data['112']['2016-12-02':'2016-12-22'].plot(legend = False, figsize=(16, 8))
data['113']['2016-12-02':'2016-12-22'].plot(legend = False, figsize=(16, 8))

Avergae of 45 eq's per yr in Lake Sentani area over 4.5 magnitude'

True average of mean for overall daily avg power usage per household below

In [0]:
44.25+75.54+42.55+14.83+19.41+7.34+75.96+8.29+33+61.27+72.261+314.59+104.03+51.76+99.24+201.67+9.17+16.97+28.93+72.78+26.92+29.74+10.3+10.8+54.1+62.51+45.36+56.57+18.4+26.35+49.87+55.93+3.83+241.49+25.7+27.31+20.79+35.39+27.72+20.79+35.39+27.72+41.71+51.54+20.07+27.65
In [0]:
2407.790999999993/43
In [0]:
55.99513953488/337

0.166157 kwh avg per day per household. = 166 watts a day. (not including smallest outliers under 1kwh)

Sources Cited:

"The Role of Microgrids in Helping to Advance the Nation's Energy System." The Role of Microgrids in Helping to Advance the Nation's Energy System | Department of Energy. US Department of Energy, n.d. Web. 21 Apr. 2017. https://energy.gov/oe/services/technology-development/smart-grid/role-microgrids-helping-advance-nation-s-energy-system

"An Earthquake of Magnitude 5.9 Occurred in Indonesia." Earth Chronicles News. N.p., n.d. Web. 21 Apr. 2017. http://earth-chronicles.com/natural-catastrophe/an-earthquake-of-magnitude-5-9-occurred-in-indonesia-2.html

GDACS. "Mw 5.5 Earthquake, Green Alert in Indonesia." Overall Green Earthquake Alert in Indonesia on 18 Sep 2016 07:17 UTC. GDACS, 18 Sept. 2016. Web. 21 Apr. 2017. http://www.gdacs.org/report.aspx?eventtype=EQ&eventid=1094162

"Past Weather in Jakarta, Jakarta Special Capital Region, Indonesia - October 2016."Timeanddate.com. Timeanddate, n.d. Web. 21 Apr. 2017. https://www.timeanddate.com/weather/indonesia/jakarta/historic?month=10&year=2016

Acknowledgements This work funded by the CSU CALL Grants Professor Soto