Analysis of Energy Efficiency Dataset for Buildings - PDF

Added on - 14 Jun 2021

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Running head: REAL WORLD ANALYTICSREAL WORLD ANALYTICSName of StudentName of University
REAL WORLD ANALYTICS1Table of ContentsPart A:Analysis of Energy Efficiency Dataset for Buildings.........................................................2Description...................................................................................................................................2Task 1...........................................................................................................................................2Task 2...........................................................................................................................................9Task 3...........................................................................................................................................9Task 4.........................................................................................................................................10Part B.............................................................................................................................................111.................................................................................................................................................11
REAL WORLD ANALYTICS2Part A:Analysis of Energy Efficiency Dataset for BuildingsDescriptionHeating load and cooling load are important determinants of the specifications in theheating and cooling equipment used in designing efficient buildings. Therefore tools for energysimulation to predict energy consumption of a building is necessary to anticipate theseparameters and hence design structures which can accommodate the demand optimally, keepingin line with the idea of energy efficient buildings.The variables Heating load (HL or denoted by Y1) and cooling load (CL or Y2) are made thetwo suggested variables of interest for this paper and the variables, relative compactness inpercentage or X1, Surface area in square meters or X2, wall areain square meters or X3, roofareain square meters or X4 and overall heightin meters or X5 are taken as potentialpredictors of the chosen response, heating load. 768 building data units were simulated through abuilding simulator and the data on the above mentioned variables were noted and hence used forthe analysis. The analysis was done in R.Task 1The text data fileENB18data.txtwas downloaded from CloudDeakin and into the Rworking directory. The data was hence loaded into the R console and assigned to a data matrix,namely, “the.data”. The matrix consisted of 7 columns to accommodate each variable and 786rows of simulated data observations.The response variable Heating Load or Y1 was chosen as the variable of interest and it’sthe influence of the variables denoted by X1, X2, X3, X4 and X5 was analyzed and each of thevariables were individually scrutinized as well. The analysis was done on the basis of a sampleof size 300 chosen by use of simple random sampling process in R.The graphical summarization of each variable and the relationship between the responsevariable and each individual independent variables are depicted and discussed hence.Thehistogram of the response variable depicts that heating load in KWh per meter square per annumfollows a right skewed distribution that is most of its values seem to be concentrated towards thelower or left tail making its right tail more elongated than its left. The mode is indicated to lie inthe interval 15KWh per meter square to 20KWh per meter square.
REAL WORLD ANALYTICS3Figure 1The relative compactness values are seen to be more or less evenly distributed aroundcenter lying between 0.6 and 1.Figure 2
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