ENB {HTT}

Energy efficiency dataset

Description

The data is about energy performance of buildings, containing eight input variables: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution and two output variables: heating load (HL) and cooling load (CL) of residential buildings. The goal is to predict two real valued responses from eight input variables. It can also be used as a multi-class classification problem if the response is rounded to the nearest integer.

Usage

data("ENB")

Format

A data frame with 768 observations on the following 10 variables.

X1

Relative Compactness

X2

Surface Area

X3

Wall Area

X4

Roof Area

X5

Overall Height

X6

Orientation

X7

Glazing Area

X8

Glazing Area Distribution

Y1

Heating Load

Y2

Cooling Load

Source

UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Energy+efficiency.

References

A. Tsanas, A. Xifara: 'Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools', Energy and Buildings, Vol. 49, pp. 560-567, 2012

Examples

data(ENB)
set.seed(1)
idx = sample(1:nrow(ENB), floor(nrow(ENB)*0.8))
train = ENB[idx, ]
test = ENB[-idx, ]
htt_enb = HTT(cbind(Y1, Y2) ~ . , data = train, controls = htt_control(pt = 0.05, R = 99))
# prediction
pred = predict(htt_enb, newdata = test)
test_y = test[, 9:10]
# MAE
colMeans(abs(pred - test_y))
# MSE
colMeans(abs(pred - test_y)^2)