Critically evaluate and compare the research design, including the methodology and methods, and discuss how these supports their respective questions/hyoptheses

INTRODUCTION
The use of electricity varies among domestic buildings. It
depends on numerous factors, such as home architecture,
number of occupants, number of electric appliances, indoor
environment and outdoor environment of the vicinity, for
example: temperature, light, noise etc [1]. All these factors are
interrelated strongly. These relations can be analyzed using
regression models to understand the relationship among the
factors [2,3,4]. Moreover, electric energy demands are
changed in weekdays and weekend days due to the staying
time of home residents. So, the demand loads are fluctuated
during regular days and weekdays. Regression analysis helps
to reveal these patterns changing in load demands also [5-9].
Many researches have been done to identify these demand
loads patterns. Candanedo et.al.[1], Wang et.al.[4], Arghira
et.al.[5], Cetin et.al.[7], and Kavousian et.al.[12], they all
worked for predicting appliances energy use into a low energy
house. They have used wireless sensor data for environmental
assessments inside the house and outside the house, and smart
electric meter’s data to analyze energy demand loads. All the
works above tried to design a load demand model for
predicting future electric use into a home using big data driven
analysis. On the other hand, many studies have been
undertaken to analyze occupant’s behaviors into home or
office in order to rank the appliance efficiency in household
use [2,3,6] . These works tried to analyze occupant’s
behaviors in home appliance using during their staying at
home or office. They used regression analysis and
probabilistic analysis to identify the load patterns in different
behaviors of occupant’s. In addition, some works were done to
predict accurate occupancy numbers by analyzing the
appliance use behaviors [11]. However, all these works above
proved one common thing that, appliance energy use in home
or office depends on many factors, such as occupant’s
numbers, internal and external environment of home or office,
building architecture, geo-location of the building etc. Without
counting these factors it is not possible to estimate the
appliance energy use in a home or office. In this study, we
have used environmental wireless sensor data, such as
humidity, temperature, wind speed, dew points and visibility
in order to understand the internal and external environments
of the building and energy use data from smart electric meter.
II. METHODOLOGY
A. Data description
The experiment dataset was prepared by combining two
types of information. One was environmental data, like:
temperature, humidity, wind speed, dew point, and visibility.
Other was energy use information for lights and appliances.
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Temperature and humidity data were collected by using
wireless sensor network (ZigBee), which contained 4.5
months data for each 10 minutes slot, which means hourly 6
data and 144 data daily. The energy use data was also
collected in 10 minutes interval using m-bus meters. However,
wind speed, dew point and visibility data were collected from
nearest weather station. Then all data from different sources
were merged together to prepare experiment data. Finally, We
had separated the main dataset into five parts, like: mooring,
afternoon, evening, night, and midnight. Table I shows the
data set attributes with their measurement units information.
TABLE I. DATA ATTRIBUTE LIST
Attribute Name Unit
Date time year-month-day hour:minute:second
Appliances, energy use Wh
lights, energy use of lights Wh
T1, Temperature in kitchen o
C
T2, Temperature in living room o
C
T3, Temperature in laundry room o
C
T4, Temperature in office room o
C
T5, Temperature in bathroom o
C
T6, Temperature outside the building o
C
T7, Temperature in ironing room o
C
T8, Temperature in teenager room o
C
T9, Temperature in parents room o
C
T_out, Temperature outside (from weather station) o
C
RH_1, Humidity in kitchen %
RH_2, Humidity in living room %
RH_3, Humidity in laundry room %
RH_4, Humidity in office room %
RH_5, Humidity in bathroom %
RH_6, Humidity outside the building %
RH_7, Humidity in ironing room %
RH_8, Humidity in teenager room %
RH_9, Humidity in parents room %
RH_out, Humidity outside (from weather station) %
Pressure (from weather station) mm Hg
Wind speed (from weather station) m/s
B. Data vidualization
Figure 1 and Figure 2 show the visual representation of
appliances and lights energy use data respectively. From these
two graph, there is a clear indication of uncertainty in energy
use prediction into a low energy house. These data are only
about a specific home. The spikes of the line graphs show that
the demand of power fluctuates in time varying. It is because
of many correlated things; like: presence of household people,
room temperature, outside temperature, light condition into
and outside the rooms, weather conditions, Humidity etc.
Light energy use relatively lower than the appliances, because
lights remains turned off most of time in day time. But,
appliances need more energy use because some household
appliances, like: Refrigerator, Air condition, Electric Fan,
Electric cooker, Room heater, Other electronic stuffs, etc
remain in use very frequently in whole day.
Appliances data
0
50
100
150
200
250
300
350
400
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97
days
energy use (wh)
Appliances
Fig. 1. Daily average energy use data by home appliances
lights data
0
5
10
15
20
25
30
35
40
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97
days
energy use (wh)
lights
Fig. 2. Daily average energy use data by lights
C. Data preparation
Though the raw data set was collected from online
repository [1], it needed to do some major data pre-process
before preparing our experiment data. Such as, the main data
set contained energy usage time series data for each 10
minutes interval. But we have calculated some derived
attributes, like; daily average, daily minimum, daily
maximum, weekday use, weekend use, morning time use,
afternoon time use, evening time use, night and midnight use.
All these attributes are prepared in order to identify the
different patterns of electric energy use in different times in a
day. These analyses help to identify correlations among the
predictor attributes and the target attribute (label). Moreover,
we have done 5 different types of analysis, such as morning
time use, afternoon time use, evening time use, night and
midnight use. So, dataset are prepared accordingly for these
five types of experiments. Table II shows the PCA analysis
results for choosing correlated feature attributes as predictors.
Here Positive (+) means strong correlation and Negative (-)
means relatively less correlated.

Fig. 3. Model flow chart
Figure 3 shows the flow chart of our experiment process.
For feature selection we applied PCA and F-test analysis in
order to identify necessary input attributes from the dataset.
Min-max normalization techniques are applied such that the
ranges of values of different attributes remain into a certain
range, like: 0 to 1. It helps to create equal opportunities of
dominance to all predictor variables into predictive model.
Otherwise, attributes with larger values will always dominate
in weight gaining during model training, which creates hurdle
to ensure significance of lower valued attributes into trained
model. As a result, model faces over-fitting and under-fitting
problems in prediction. We have separated our dataset into
two parts, 80% of total data into training data and rest of the
20% into test data. SVR and BP-ANN were applied as
machine learning algorithms. Cross validations were applied
to identify fine tuned parameters combinations of those
algorithms. Finally, best model was selected based on their
predictive performance.
III. EXPERIMENT RESULTS
A. Support vector machine model parameters for appliances
Support Vector Regression (SVR) works very good to
identify patterns of dataset with numeric feature values. It has
the ability to perform computationally effective analysis to
reveal knowledge from input dataset. As our experiments are
related to time series data analysis for pattern recognition and
future value prediction, we choose SVR to apply with
different types of kernel tricks, like; radial, polynomial, and
sigmoid. Different kernel tricks help to identify and learn
those hidden patterns and train the model with these
knowledge such that model can produce good predictive
results with minimum error rate. Table III show the cross
validated information of SVR analysis with three different
kernel techniques for appliance energy use prediction. All the
parameters are cross validated and only the best combinations
are tabulated with their error performances. From the Table III
we see radial bias function performs better than the others,
though the differences are very small.
TABLE III. CROSS VALIDATED RESULT SUMMARY OF SVM MODEL WITH
DIFFERENT KERNEL FOR APPLIANCES
kernel Kernel parameters
MSE

RMSE gamma cost Epsilon degree Coef0
radial 0.01 2 0.0001 ---- ---- 1.32% 11.49%
polynomial 0.01 2 0.0001 2 0 1.56% 12.51%
sigmoid 0.01 2 0.0001 ---- 0 1.54% 12.42%
Table IV shows the fine tuned SVR parameters with three
different kernels for lights energy use prediction. Here
polynomial and sigmoid kernels very slightly outperformed
radial bias function.
TABLE IV. CROSS VALIDATED RESULT SUMMARY OF SVM MODEL WITH
DIFFERENT KERNEL FOR LIGHTS
kernel Kernel parameters
MSE RMSE
gamma cost Epsilon degree Coef0
radial 0.01 2 0.0001 ---- ---- 0.39% 6.25%
polynomial 0.01 2 0.0001 2 0 0.37% 6.07%
sigmoid 0.01 2 0.0001 ---- 0 0.37% 6.11%
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TABLE V. ACTUAL VS PREDICTED VALUES OF APPLIANCES USING SVR
Attributes Date Actual
Value
Predicted
value
Difference
Appliances,
T1,RH_1,
T2,T3,
RH_3,T4,
RH_4,T5,
RH_5,T6,
T7,T8,T9,
T_out,
Windspeed
4/20/2016 0.168 0.108 0.060
morning
4/20/2016 0.284 0.069 0.215
afternoon
4/20/2016 0.034 0.051 -0.017
evening
4/20/2016 0.038 0.006 0.033
night
4/21/2016 0.052 0.163 -0.111
midnight
4/21/2016 0.135 0.225 -0.090
morning
4/21/2016 0.262 0.240 0.022
afternoon
4/21/2016 0.884 0.152 0.733
evening
4/21/2016, 0.078 0.112 -0.034
night

TABLE VI. ACTUAL VS PREDICTED VALUES OF LIGHTS USING SVR
Attributes Date Actual
Value
Predicted
value
Difference
Lights,
RH_1,
RH_2,
RH_3,
RH_4,
RH_5,
RH_6,
RH_7,
RH_8,
RH_out,
Windspeed
4/20/2016 0.063 0.007 0.057
morning
4/20/2016 0.333 0.001 0.333
afternoon
4/20/2016 0.000 -0.038 0.038
evening
4/20/2016 0.010 -0.079 0.089
night
4/21/2016 0.041 0.009 0.032
midnight
4/21/2016 0.008 -0.019 0.027
morning
4/21/2016 0.036 -0.002 0.038
afternoon
4/21/2016 0.000 -0.106 0.106
evening
4/21/2016 0.000 -0.140 0.140
night
Actual vs Predicted of Appliances using SVR
(average value)
0
0.2
0.4
0.6
0.8
1 2 3 4 5 6 7 8 9 10 11
Days
energy use (wh)
Actual Predicted
Fig. 4. Actual vs Predicted electric energy use values for home appliances
using SVR
Fig. 4 and Fig. 5, show the graphical illustrations of actual and
predicted energy use values for home appliances and lights
respectively using support vector regression model. From Fig.
4, it is clear that energy usage in home appliances always has
some values rather than “0”, and there is no gradual increase
or decrease happens all in a sudden. So, the proposed model
can identify and predict the pattern of appliances power use
almost correctly. But, in Fig. 5, there is a clear indication of
irregular and inconsistent fluctuations of energy use by lights
into the house. So, the pattern identification is little bit critical
than the appliances use. But, after the rigorous and careful
training, our proposed model able to solve the problem with
margin of error between actual and predicted values.
Actual vs Predicted values for lights using SVR
(average value)
0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4 5 6 7 8 9 10 11
Days
energy use (wh)
Actual Predicted
Fig. 5. Actual vs Predicted electric energy use values for home lights using
SVR
C. Artificial Neural Network with Back Propagation
Artificial neural network is one the best machine learning
approaches for identifying patterns and predictions. It has
many variations such as feed forward, back-propagation,
recurrent etc which make it pioneer in computational
intelligence era. Moreover, the flexibility in ANN
architectural design, such as choosing hidden layer number,
processing unit number, activation function variations,
threshold values setup etc make it fantastic in computational
analysis for identifying very critical patterns which is very
tough task for other AI techniques.
In this study, we have applied feed forward and backpropagation artificial neural networks with different network
architectures using logistic (sigmoid) and tanH activation
functions for activating processing units (neurons) for
triggering information in forward directions and sum squared
error (sse) functions as convergence of error to minimize error
generated in each iteration. Table VII and VIII show the
experiments summaries using ANN with back propagation
technique. The tabulated values of parameters were cross
validated and only the fine tuned combination of parameters
are shown. From the Table VII and VIII, it is very clear to see
that sigmoid or logistic activation function with threshold
value 0.05 and learning rate 0.05 can produce the best results
among the all. TanH function also able to predict energy use
for appliances and lights with marginal error rates. But logistic
function outperforms all. And the network architecture is 15
inputs, one hidden layer with 2 neurons and 1 output (15-2-1).
TABLE VII. BP-ANN MODEL PARAMETERS WITH ERROR RATE FOR
APPLIANCES (CROSS VALIDATED)
Hidden
Layer
Activation
function
Error
Function
Threshold
value
Learning
rate
MAE RMSE
2 logistic sse 0.01 0.01 0.29 0.36
0.02 0.02 0.24 0.30
0.02 0.03 0.25 0.30
0.03 0.04 0.18 0.20
0.04 0.05 0.16 0.19
0.05 0.05 0.14 0.18
3 tanh sse 0.01 0.01 0.48 0.50
0.02 0.02 0.48 0.50
0.02 0.03 0.48 0.50
0.03 0.04 0.48 0.50
0.04 0.05 0.48 0.50
0.05 0.05 0.48 0.50
TABLE VIII. BP-ANN MODEL PARAMETERS WITH ERROR RATE FOR
LIGHTS (CROSS VALIDATED)
Hidden
Layer
Error
Function
Activation
function
Threshold
value
Learning
rate MAE RMSE
2 logistic sse 0.01 0.01 0.08 0.13
0.02 0.02 0.07 0.12
0.02 0.03 0.07 0.12
0.03 0.04 0.06 0.11
0.04 0.05 0.06 0.11
0.05 0.05 0.06 0.11
3 tanh sse 0.01 0.01 0.07 0.13
0.02 0.02 0.07 0.13
0.02 0.03 0.07 0.13
0.03 0.04 0.07 0.12
0.04 0.05 0.07 0.12
0.05 0.05 0.08 0.12
Table IX and X show the actual vs Predicted values using
neural network model with back propagation approach. The
predictors were chosen by using PCA analysis to feed input
data into BP-ANN model. There are five types of predicted
values, like; morning, afternoon, evening, night and midnight
time prediction of energy usage by home appliances and
lights. Difference between actual vs predicted values is very
marginal in each time slot, which is the indication of model
performance stability in different situation or time.
TABLE IX. ACTUAL VS PREDICTED VALUES OF APPLIANCES USING BPANN MODEL WITH LOGISTIC FUNCTION
Attributes Date Actual
value
Predicted
value Difference
Appliances, T1,RH_1,
T2,RH_2,T3,RH_3,
T4,RH_4,T5,RH_5,T6,
RH_6,T7,RH_7,
T8,RH_8,T9,RH_9,
T_out,
Windspeed
4/20/2016
0.1677 0.1385 0.0292 (afternoon)
4/20/2016 0.2842 0.2128 0.0714 (evening)
4/20/2016 0.0337 0.0905 -0.0569 (night)
4/20/2016 0.0383 0.0407 -0.0024 (midnight)
4/21/2016 0.0520 0.1224 -0.0704 (morning)
4/21/2016 0.1347 0.1541 -0.0194 (afternoon)
4/21/2016 0.2621 0.1876 0.0745 (evening)
4/21/2016 0.0884 0.0948 0.0121 (night)
4/21/2016
0.0779 0.0658 0.0121 (midnight)
TABLE X. ACTUAL VS PREDICTED VALUES OF LIGHT USING BP-ANN
MODEL WITH LOGSTIC FUNCTION
Attributes Date Actual
value
Predicted
value
Difference
Lights, T1,RH_1,
T2,RH_2,T3,RH_3,
T4,RH_4,T5,RH_5,T6,
RH_6,T7,RH_7,
T8,RH_8,T9,RH_9,
RH_out,
Windspeed
,
4/20/2016 0.0635 0.0201 0.0434 (afternoon)
4/20/2016
0.3333 0.0550 0.2783 (evening)
4/20/2016
0.0000 0.0566 -0.0566 (night)
4/20/2016
0.0095 0.0554 -0.4586 (midnight)
4/21/2016 0.0408 0.0051 0.0357 (morning)
4/21/2016 0.0079 0.0484 -0.0405 (afternoon)
4/21/2016 0.0357 0.0544 -0.0187 (evening)
4/21/2016 0.0000 0.0560 -0.0560 (night)
4/21/2016
0.0000 0.0557 0.0557 (midnight)
Fig. 6. and Fig. 7. show the graphical representations of actual
and predicted energy use values for home appliances and
lights respectively using BP-ANN model. From Fig. 6. it is
clear that our proposed BP-ANN model able to predict
appliance energy use very effectively. The error between
actual and predicted values is so marginal, almost negligible.
But from Fig. 7. it is very easy to understand that model has
limitation to predict actual pattern of light energy usage. It
predicts almost a linear pattern of energy use for lights. This is
because input data contained too many “0” values, as a result
model could not reveal the actual pattern of energy usage of
lights. Which means, the experiment dataset needs to be
purified by applying some rectifications techniques to
strengthen the input signals rather than “0” values to overcome
the problem stated above. And, the predictive model still
needs some fine tuning in parameter selections also. However,
the error rate between actual and predicted values is still in
tolerable range.
Actual and predicted value of appliances using BPANN (average dataset)
0
0.05
0.1
0.15
0.2
0.25
0.3
4/20/2016
4/21/2016
4/22/2016
4/23/2016
4/24/2016
4/25/2016
4/26/2016
4/27/2016
4/28/2016
4/29/2016
Days
Energy use (wh)
Actual value Predicted value
Fig. 6. Actual vs Predicted electric energy use values for home appliances
using BP-ANN
Actual and predicted value of lights using BP-ANN
(average dataset)
0
0.05
0.1
0.15
0.2
0.25
0.3
4/20/2016
4/21/2016
4/22/2016
4/23/2016
4/24/2016
4/25/2016
4/26/2016
4/27/2016
4/28/2016
4/29/2016
4/30/2016
Days
Energy use (Wh)
Actual value Predicted value
Fig. 7. Actual vs Predicted electric energy use values for home lights using
BP-ANN