Predictive models to demonstrate homeowners how the data can be used to help a home owner to make decisions about altering power consumption.

1. Visualize & Analyze
Energy Data
In-depth analysis of the power consumption data set via data
visualization and time series regression analysis.

2. Goals of the Analysis
Understanding energy consumption through visualization.
Uncover energy hogs; accurate knowledge of where energy is being consumed
is the first step in creating energy savings.
To provide business suggestions based on analysis.

3. Data Overview
Individual power consumption data gathered between December 2006 and November 2010
Measurement of electric power consumption in one household with a one-minute sampling
rate in three connected sub-meters.
Over 2 million instances with about 26K (1.25%) missing data with only date and timestamp.
Submeter 1 Submeter 2 Submeter 3
Main Meter

4. Visualize Data
• to gain a deeper understanding of the data

8. Household Habit
This household typically does most cooking on Sunday while
on Monday and Tuesday kitchen is hardly in use.
Laundry is not done on Wednesdays and Thursdays.
SundayWednesday
Thursday

10. Forecasting a Time-Series
• To identify possible patterns to pursue via regression analysis

11. Residuals: Min 1Q Median 3Q Max
-34.737 -7.354 0.158 9.677 34.737
Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 84.5463 11.2986 7.483 1.32e-07 ***
Residual standard error: 18.28 on 23 degrees of freedom
Multiple R-squared: 0.4152, Adjusted R-squared: 0.1101
F-statistic: 1.361 on 12 and 23 DF, p-value: 0.2531

12. Residuals: Min 1Q Median 3Q Max
-22.008 -13.229 -7.461 12.349 59.487
Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 77.9653 6.5224 11.954 1e-13
Residual standard error: 19.16 on 34 degrees of freedom
Multiple R-squared: 0.0497, Adjusted R-squared: 0.02175
F-statistic: 1.778 on 1 and 34 DF, p-value: 0.1912

13. Residuals: Min 1Q Median 3Q Max
-34.737 -7.354 0.158 9.677 34.737
Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 76.278112 10.172071 7.499 2.47e-10 ***
Residual standard error: 17.03 on 64 degrees of freedom
Multiple R-squared: 0.5226, Adjusted R-squared: 0.1272
F-statistic: 1.322 on 53 and 64 DF, p-value: 0.1426

14. Residuals: Min 1Q Median 3Q Max
-26.225 -14.174 1.231 11.261 58.743
Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 73.61821 3.19666 23.030 2e-16
Residual standard error: 17.25 on 116 degrees of freedom
Multiple R-squared: 0.1122, Adjusted R-squared: 0.1046
F-statistic: 14.66 on 1 and 116 DF, p-value: 0.0002091

15. Residuals: Min 1Q Median 3Q Max
-43.84 -11.61 0.00 11.90 43.84
Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.778104 12.277529 2.751 0.00622 **
Residual standard error: 21.11 on 376 degrees of freedom
Multiple R-squared: 0.4481, Adjusted R-squared: -0.08756
F-statistic: 0.8365 on 365 and 376 DF, p-value: 0.9567

16. Residuals: Min 1Q Median 3Q Max
-30.858 -19.435 1.682 12.968 96.124
Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.67807 1.45383 14.22 < 2e-16
Residual standard error: 19.78 on 740 degrees of freedom
Multiple R-squared: 0.0461, Adjusted R-squared: 0.04481
F-statistic: 35.76 on 1 and 740 DF, p-value: 3.466e09

17. Glimpse of Power Usage
Collected once a Week
at 4 hours interval during
2007-2009

18. Decompose Seasonal Time-Series
Holt-Winters Forecast using
To forecast by triple exponential smoothing
To identify a trend component, a seasonal component and an irregular
component. Decomposing the time series means separating the time
series into these three components: that is, estimating these three
components.
&
non-seasonal components

19. Household Global Active Power Consumption
Holt-Winters Forecast
of Household Global Active
Power Consumption

22. Smoothing parameters:
alpha: 0.1760599 beta : FALSE gamma: FALSE Coefficients: [,1] a 8.941701 Sum-of-squared-error: 7480.818

23. Here is the forecasts for 2010-2012 plotted as a blue line, the 80% prediction interval as an dark grey shaded area, and the 95% prediction interval as
a light grey shaded area. Forecasted global active power consumption for December 2010 is about 8.94 watts with a 95% prediction is about 25.38
watts)

24. L-Jung test statistics of 15.66 and p value 0.7 shows there is little evidence of non-zero autocorrelations in the in-sample
forecast errors at lags 1-20

25. Power Consumption in Kitchen
Power Consumption
by AC & Water Heater
(data collected from Submeter 3)

27. Forecasted global active power consumption for forecast for next 2 years is 1.58 watts with a 95% prediction is about 12.59 & 80% prediction of
usage is 8.78 watts)

28. Power Consumption in Laundry
Power Consumption
in Laundry
(data collected from Submeter 2)

30. Smoothing parameters:
alpha: 0.1166697 beta : FALSE gamma: FALSE Coefficients: [,1] a 0.1073942 Sum-of-squared-error: 13.2682

31. Forecasted global active power consumption for forecast for 2011 December 0.1 watts with a 95% prediction is about 1.51 watts & 80% prediction
of usage is 1.02 watts)

32. L-Jung test statistics of 33.194 and p value 0.03 shows there is little evidence of non-zero autocorrelations in the in-sample
forecast errors at lags 1-20

33. Power Consumption by AC & Water Heater
Power Consumption
by AC & Water Heater
(data collected from Submeter 3)

35. Smoothing parameters:
alpha: 0.3246485 beta : FALSE gamma: FALSE Coefficients: [,1] a 3.445695 Sum-of-squared-error: 2518..009

36. Forecasted global active power consumption for forecast for 2011 December 3.45 watts with a 95% prediction is about 34.25 watts & 80% prediction
of usage is 23.59 watts)

37. L-Jung test statistics of 7.1882 and p value 0.99 shows there is little evidence of non-zero autocorrelations in the in-sample
forecast errors at lags 1-20
Lag 20 exceed significance bounds at 0, only 1 of 20 will exceed 95% bound

38. Summary
of
Household Energy
Consumption

39.  Average daily energy consumption is high between 7 am to noon and 9pm to mid night; consumptions
are mostly driven by AC/Water Heater usage.
 ¾ of overall consumption is by AC & Water Heater
 This household typically does most of cooking on Sunday noon while on Monday and Tuesdays
kitchen is hardly in use. Kitchen usage is NOT seasonal.
 Laundry is usually not done on Wednesday and Thursdays
 Power usage highly dependent on seasonality; in other words, usage pattern varies based on seasons
 TSLM forecasts of all three time series indicates linear regression. While daily time series shows upward
trend with chances in increase of .95 watts in December 2011 (depicted by p value slope). However,
weekly and monthly time series shows downward trend
 Holt winters regression model looks reasonably acceptable, They shows non-zero autocorrelation in
sample forecast. Model trend is constant, neither upward nor downward, monthly usage predicted is
8.94 watts. In December 2011, 95% usage predicted is 45.8 watts and 80% chances are 33 watts.
Insights from the Household Electricity Consumption

40. • Looking at data looks like am consumption of submeter3 is due to the use of water heater.
Separate submeter or at least data related water heater would help to see clearer picture
• Using timer may help in energy saving
• Introducing dynamic setpoint for HVAC that will help to adjust depending on outdoor
temperature
• Curtail load by determining which equipment may be turned down for lower energy
consumption
• Sharing monthly usage breakup per appliance with the leaser in owners office or portral will
help in identifying and reducing energy consumption.
Recommendation for Home Owners