Energy efficiency
Donated on 11/29/2012
This study looked into assessing the heating load and cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Classification, Regression
Feature Type
Integer, Real
# Instances
768
# Features
-
Dataset Information
Additional 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.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
|---|---|---|---|---|---|---|
| no | ||||||
| no | ||||||
| no | ||||||
| no | ||||||
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0 to 8 of 8
Additional Variable 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. Specifically: 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
Papers Citing this Dataset
By Mahmut Yazici, Shadi Basurra, Mohamed Gaber. 2018
Published in Big Data and Cognitive Computing.
By Jan Zegklitz, Petr Posík. 2017
Published in ArXiv.
0 to 2 of 2
Tsanas,Athanasios and Xifara,Angeliki. (2012). Energy efficiency. UCI Machine Learning Repository. https://doi.org/10.24432/C51307.
@misc{misc_energy_efficiency_242,
author = {Tsanas,Athanasios and Xifara,Angeliki},
title = {{Energy efficiency}},
year = {2012},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C51307}
}pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset energy_efficiency = fetch_ucirepo(id=242) # data (as pandas dataframes) X = energy_efficiency.data.features y = energy_efficiency.data.targets # metadata print(energy_efficiency.metadata) # variable information print(energy_efficiency.variables)
Creators
Athanasios Tsanas
Angeliki Xifara
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.