ASSESSMENT TASK $2($PROBLEM SOLVING$)$ in $2023$T$3$

Using aggregation functions for data analysis

The provided zip file contains the data file $[$ENB$\_2023.$txt $]$ and the R code $[$AggWaFit$718.$R $]$ to use with the following tasks, include these in your R working directory.

Total Marks $100,$ Weighting $20\%$

Energy Appliances Dataset

The Dataset for this assignment is modified version of a subset of data used in Candanedo et al$,2017.$

The experimental data have been used to create models of energy use of appliances in a low$-$energy house. The modified Dataset provides the energy use of Appliances $($denoted as Y$).$

The Dataset comprises $5$ features $($variables$),$ which are denoted as X$1,$ X$2,$ X$3,$ X$4$ and X$5.$

The details about these variables are given below:

X$1$: Temperature in living room area $($Celsius degrees$)$

X$2$: Humidity in living room area $($percentage$)$

X$3$: Temperature in office room $($Celsius degrees$)$

X$4$: Humidity in office room $($percentage$)$

X$5$: Pressure $($millimeter of mercury$)$

Y: Appliances energy consumption $($Wh$)$

For more information about the variables see Candanedo et al$,2017.$

Assignment tasks

T$1.$ Understand the data

$($i$)($ii$)$

$($iii$)$

Download the txt file $($ENB$\_2023.$txt$)$ from CloudDeakin and save it to your R working directory. Assign the data to a matrix, e$.$g$.$ using

the.data $<-$ as$.$matrix$($read$.$table$("$ENB$\_2023.$txt$"))$

The variable of interest is Y$.$ To investigate Y$,$ generate a subset of num$\_$row$=400($use the same setting for the following tasks as well$)$ with numerical data e$.$g$.$ using:

my$.$data $<-$ the.data$[$sample$(1$:num$\_$samples,num$\_$row$)$ c$(1$:num$\_$col$)]$

This would give you a new dataset with num$\_$row rows and num$\_$col columns. Values of num$\_$sample and num$\_$col have to be determined from the data provided.

$($iv$)$Use scatter plots and histograms to understand the relationship between each of the variables X$1$ X$2,$ X$3,$ X$4,$ X$5,$

and your variable of interest Y$,$ i$.$e$.,$ catter plots of $($X$1,$ Y$),($X$2,$ Y$),...,($X$5,$ Y$),$ and histograms of X$1$ X$2,$ X$3,$

X$4,$ X$5,$ Y$.$

T$2.$ Transform the data

Choose any FOUR variables from X$1,$ X$2,$ X$3,$ X$4,$ X$5.$

Make appropriate transformations so that the values can be aggregated in order to predict

the variable of interest Y$.$

Assign your transformed data along with your transformed variable of interest to an array

$($it should be $``$num$\_$row$$ rows and $5$ columns$).$ Save it to a txt file titled "name$-$transformed.txt$".$

write.table$($your$.$data,"name$-$transformed.txt$")$

The following tasks are based on the saved transformed data.

T$3.$ Build models and investigate the importance of each variable.

$($i$)$ Download the AggWaFit.R file to your working directory and load into the

R workspace using,

source$("$AggWaFit$718.$R$")$

$($ii$)$ Use the fitting functions to learn the parameters for

a$.$ A weighted arithmetic mean $($WAM$),$

b$.$ Weighted power means $($WPM$)$ with p $=0\mathrm{.}5,$

c$.$ Weighted power means $($WPM$)$ with p $=2,$

d$.$ An ordered weighted averaging function $($OWA$).$

T$4.$ Use your model for prediction.

Using your best fitting model from T$3,$ i$.$e$.,$ WAM, WPM$(0\mathrm{.}5),$ WPM$(2),$ or OWA, predict Y $($Appliances$)$ for the following inputs:

X$1=19\mathrm{.}1,$ X$2=43\mathrm{.}29,$ X$3=19\mathrm{.}7,$ X$4=43\mathrm{.}4,$ X$5=743\mathrm{.}6$

You should use the same pre$-$processing as in Task $2.$

Compare your prediction with the measured Y$=60.$

T$5.$ Summarise your data analysis in up to $20$ slides for a $5-$minute presentation

The slides should include the following content:

$-$ Correlations between the variables;

$-$ What kinds of data distributions you have identified in the raw data, use the histograms you have produced;

$-$ List and explain the transformations applied for the selected four variables and the variable of interest;

$-$ Explain the importance of the variables you have selected;

$-$ The best fitting model on your selected data; include two tables:

one with the error measures and correlation coefficients, and one summarizing the weights$/$parameters

and any other useful information learned for your data;

$-$ Your prediction result and comment on wheather you think it is reasonable;

$-$ Discuss the best conditions $($in terms of your chosen variables$)$ under which a low energy use of

appliances will occur.

$-$ Comment on the implications and limitations of the fitting model you used for prediction.

The slides should contain all necessary information to prove your findings. All the bold terms above must appear in slide titles. For the $5-$minute presentation, you may provide a link to YouTube or upload a mp$4$ video. Any content beyond $5$ minutes will not be graded.

SUBMISSION:

Submit to the SIT$718$ CloudDeakin Dropbox.

Your final submission must include the following TWO files:

$1.$ The presentation slides with video, "name$-$slides" $($pdf$),$ covering all of the items in above

$($where $$name$$ is replaced with your name $-$you can use your surname or first name$)$

$($a link to YouTube or uploading a mp$4$ file$).$

$2.$ The R code file $($that you have written to produce your results$)$ named "name$-$code.R$"($where $$name$$ is replaced with your surname or first name; $.$RMD file is not allowed$).$

Your assignment will not be assessed if the code is missing, or the outputs of the code are