Real World Analytics - Assignment

Added on - 31 May 2021

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Running head: REAL WORLD ANALYTICSREAL WORLD ANALYTICSName of StudentName of University
REAL WORLD ANALYTICS1Table of ContentsPart A:..............................................................................................................................................2Description...................................................................................................................................2Task 1...........................................................................................................................................2Task 2...........................................................................................................................................9Task 3...........................................................................................................................................9Task 4.........................................................................................................................................11
REAL WORLD ANALYTICS2Part A:DescriptionGenerally two types of loadings (heating and cooling load) helps to guide thespecifications in installed buildings. Hence, to take into account the optimization of energyconsumption, key variables are regarded to design the efficient designing of buildings andconstructions.The variables of the analysis are – 1) Heating load or Cooling load (dependent variable),2) Relative compactness 3) Surface Area 4) Wall Area 5) Wall Area and 6) Roof Area. Apartfrom the heating load or cooling load, all the other variables are considered as independentvariables.The units show that Relative compactness is measured in decimals. Wall Area, Surface Area andRoof area are measured in square meters. Overall heights are measured in meters. On the otherhand, heating load is measured in kWh.m-2per annum. The ENB18data.txt file includes thosedata variables with chosen response. The analysis is executed using simulation and randomsampling technique. Out of 768 variables, only 300 are utilized for analysis.The statistical software “R” is utilized to execute the task.Task 1The data file ENB18data.txt was downloaded from CloudDeakin website as perinstruction. The dependent variable “Cooling Load” is regarded as the variable of interest toconsider as dependent variable. The predictor variables or the dependent variables are the rest ofthe numerical factors. The dependent variable is renamed as Y1 and the dependent variables arerenamed as X1, X2, X3, X4 and X5 respectively. The influence of the variables were analyzedon the basis of chosen samples and elaborated.The summary of the analysis, graphical visualizations and relationship betweendependent and independent variables are shown as follows. The histogram plots indicate thedistribution of the variables.Figure1: Histogram of per annum heating load
REAL WORLD ANALYTICS3Per annum Heating load in kWh per squared metreHeating LoadFrequenciesofheatingload1020304050010203040506070The histogram of heating load (KWh per square meter per annum) is positively skewedand curved to the left. Most of the frequencies are located in the left tail of the distribution. Onthe other hand, comparatively lesser number of frequencies are obtained in right tail of thedistribution. The right tail is much elongated than the left tail. A significant number of values liein the interval of 10 to 20 kWh per square meter followed by the interval of 30-40 kWhm-2. Themode of the distribution of heating load lies between 15 – 20 kWhm-2.Figure2: Histogram of Relative compactness
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