Data 606 Project Proposal
Deepak Mongia
October 27, 2018
Energy efficiency Data Set
For this project for Data 606, we are going to work on the Energy Efficient data set which is present at this web link: http://archive.ics.uci.edu/ml/datasets/Energy+efficiency#
Data Preparation
Loading the data set into R, so that it can be used
library(readxl)
library(ggplot2)
library(gridExtra)
energy.efficiency.df1 <- read_xlsx("ENB2012_data.xlsx", sheet = 1)
head(energy.efficiency.df1)## # A tibble: 6 x 10
## X1 X2 X3 X4 X5 X6 X7 X8 Y1 Y2
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.98 514. 294 110. 7 2 0 0 15.6 21.3
## 2 0.98 514. 294 110. 7 3 0 0 15.6 21.3
## 3 0.98 514. 294 110. 7 4 0 0 15.6 21.3
## 4 0.98 514. 294 110. 7 5 0 0 15.6 21.3
## 5 0.9 564. 318. 122. 7 2 0 0 20.8 28.3
## 6 0.9 564. 318. 122. 7 3 0 0 21.5 25.4names(energy.efficiency.df1) <- c("relative.compactness", "surface.area", "wall.area", "roof.area", "overall.height", "orientation", "glazing.area", "glazing.area.distribution", "heating.load", "cooling.load")
summary(energy.efficiency.df1)## relative.compactness surface.area wall.area roof.area
## Min. :0.6200 Min. :514.5 Min. :245.0 Min. :110.2
## 1st Qu.:0.6825 1st Qu.:606.4 1st Qu.:294.0 1st Qu.:140.9
## Median :0.7500 Median :673.8 Median :318.5 Median :183.8
## Mean :0.7642 Mean :671.7 Mean :318.5 Mean :176.6
## 3rd Qu.:0.8300 3rd Qu.:741.1 3rd Qu.:343.0 3rd Qu.:220.5
## Max. :0.9800 Max. :808.5 Max. :416.5 Max. :220.5
## overall.height orientation glazing.area glazing.area.distribution
## Min. :3.50 Min. :2.00 Min. :0.0000 Min. :0.000
## 1st Qu.:3.50 1st Qu.:2.75 1st Qu.:0.1000 1st Qu.:1.750
## Median :5.25 Median :3.50 Median :0.2500 Median :3.000
## Mean :5.25 Mean :3.50 Mean :0.2344 Mean :2.812
## 3rd Qu.:7.00 3rd Qu.:4.25 3rd Qu.:0.4000 3rd Qu.:4.000
## Max. :7.00 Max. :5.00 Max. :0.4000 Max. :5.000
## heating.load cooling.load
## Min. : 6.01 Min. :10.90
## 1st Qu.:12.99 1st Qu.:15.62
## Median :18.95 Median :22.08
## Mean :22.31 Mean :24.59
## 3rd Qu.:31.67 3rd Qu.:33.13
## Max. :43.10 Max. :48.03energy.efficiency.df1$orientation <- as.factor(energy.efficiency.df1$orientation)
levels(energy.efficiency.df1$orientation)[levels(energy.efficiency.df1$orientation) == "2"] <- "north"
levels(energy.efficiency.df1$orientation)[levels(energy.efficiency.df1$orientation) == "3"] <- "east"
levels(energy.efficiency.df1$orientation)[levels(energy.efficiency.df1$orientation) == "4"] <- "south"
levels(energy.efficiency.df1$orientation)[levels(energy.efficiency.df1$orientation) == "5"] <- "west"
head(energy.efficiency.df1)## # A tibble: 6 x 10
## relative.compac~ surface.area wall.area roof.area overall.height
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.98 514. 294 110. 7
## 2 0.98 514. 294 110. 7
## 3 0.98 514. 294 110. 7
## 4 0.98 514. 294 110. 7
## 5 0.9 564. 318. 122. 7
## 6 0.9 564. 318. 122. 7
## # ... with 5 more variables: orientation <fct>, glazing.area <dbl>,
## # glazing.area.distribution <dbl>, heating.load <dbl>,
## # cooling.load <dbl>Attribute Details:
Relative Compactness - Ratio
Surface Area - sq. meters
Wall Area - sq. meters
Roof Area - sq. meters
Overall Height - meters
Orientation - 2:North, 3:East, 4:South, 5:West
Glazing area (ratio) - 0.00, 0.10, 0.25, 0.40
Glazing area distribution - 1:Uniform, 2:North, 3:East, 4:South, 5:West
Heating Load - kWh/sq. meters
Cooling Load - kWh/sq. meters
Research question
You should phrase your research question in a way that matches up with the scope of inference your dataset allows for.
Is the relative compactness of a building predictive of heating load and cooling load of a building ?
Is the area (wall area / roof area / surface area) of a building predictive of the heating or cooling load ?
Does the building orientation suggest anything about the heating or cooling efficiency of a building ?
Cases
What are the cases, and how many are there?
Cases are all these variable values for each building which is a part of the sample
There are total of 768 cases in this data set
Describe the method of data collection.
The dataset was created by Angeliki Xifara (angxifara ‘@’ gmail.com, Civil/Structural Engineer) and was processed by Athanasios Tsanas (tsanasthanasis ‘@’ gmail.com, Oxford Centre for Industrial and Applied Mathematics, University of Oxford, UK).
Type of study
What type of study is this (observational/experiment)?
This is an observational study as the data has been collected for the buildings which are there already. This is not an experiment.
Data Source
If you collected the data, state self-collected. If not, provide a citation/link.
The data is collected from: http://archive.ics.uci.edu/ml/datasets/Energy+efficiency#
Dependent Variable
What is the response variable? Is it quantitative or qualitative?
The response variables are: heating.load cooling.load
Independent Variable
You should have two independent variables, one quantitative and one qualitative.
In this case, we have the below independent variables: relative.compactness - numerical surface.area - numerical wall.area - numerical roof.area - numerical overall.height - numerical orientation - categorical glazing.area - numerical glazing.area.distribution - categorical
Relevant summary statistics
Provide summary statistics for each the variables. Also include appropriate visualizations related to your research question (e.g. scatter plot, boxplots, etc). This step requires the use of R, hence a code chunk is provided below. Insert more code chunks as needed.
Efficiencies against relative compactness
a <- ggplot(energy.efficiency.df1, aes(x=energy.efficiency.df1$relative.compactness, y=energy.efficiency.df1$heating.load)) +
geom_point()
b <- ggplot(energy.efficiency.df1, aes(x=energy.efficiency.df1$relative.compactness, y=energy.efficiency.df1$cooling.load)) +
geom_point()
grid.arrange(a, b, nrow = 1)
Efficiencies against surface area
a <- ggplot(energy.efficiency.df1, aes(x=energy.efficiency.df1$surface.area, y=energy.efficiency.df1$heating.load)) +
geom_point()
b <- ggplot(energy.efficiency.df1, aes(x=energy.efficiency.df1$surface.area, y=energy.efficiency.df1$cooling.load)) +
geom_point()
grid.arrange(a, b, nrow = 1)
Relationships between the 2 efficiency values
ggplot(energy.efficiency.df1, aes(x=energy.efficiency.df1$heating.load, y=energy.efficiency.df1$cooling.load)) +
geom_point() + stat_smooth(method = lm)