Critically evaluate and compare the research design and discuss how these supports their respective questions/hypotheses from the following text

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.
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.

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.

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.

night
Table V and VI show the actual vs Predicted values of
energy usage of home appliances and lights respectively using
support vector regression model. The predictors were chosen
by using PCA analysis to feed input data into SVR 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. In some cases of
light energy prediction, some predicted values are produced in
negative amount. This is because the previous inputs pattern
contained many zero values, which means no lights were used.
So the model predicts some negative energy values which
could be treat as “0”, as the energy use amounts can not be
negative ever; either zero or some positive values.

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 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.

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.

IV. CONCLUSION
The aim of this study is to propose a computational
intelligence model to predict electric energy usage in a low
energy house or apartment. To design the model two very
computationally effective machine learning approaches are
applied, named support vector machine (SVM) and Artificial
neural network (ANN). Both models are trained and tested
with a very clean and properly pre-processed dataset, which is
collected from UCI, a very popular data repository for data
mining and machine learning enthusiasts. The experiments are
design to predict electric energy use in five dimensions, such
as morning time use, afternoon time use, evening time use,
night time use, and midnight time use. All are done in order to
provide flexibility in electric energy use prediction in different
times in a day. Moreover, use of electric energy by appliances
and lights are experimented separately, by which our proposed
model will help the users to understand the separate pattern of
energy use by home appliances and lights. After a meticulous
analysis, we find our proposed BP-ANN model is very
efficient in predicting electric energy use in a low energy
house or apartment. Though the BP-ANN model outperforms
SVR model very slightly, but the SVR model also can produce
good results in electric energy predicting.