📌 In this section, predicting the energy efficiency of buildings with machine learning algorithms.
📌 The dataset includes features such as surface/wall/roof area, glass area, glass area distribution and orientation of 768 simulated building shapes. Based on this data, it is aimed to estimate the heating and cooling load of the building. In this age where environmental awareness is very important, it can be a super consultancy initiative to reduce our carbon footprint, as we still haven't switched to renewable energy sources.
Data Set Information:
📌 We perform energy analysis using 12 different building shapes simulated in Ecotect. The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valued responses. It can also be used as a multi-class classification problem if the response is rounded to the nearest integer.
Attribute Information:
📌 The dataset contains eight attributes (or features, denoted by X1…X8) and two responses (or outcomes, denoted by y1 and y2). The aim is to use the eight features to predict each of the two responses.
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