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

8

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

Introductory Paper

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
X1FeatureContinuousRelative Compactnessno
X2FeatureContinuousSurface Areano
X3FeatureContinuousWall Areano
X4FeatureContinuousRoof Areano
X5FeatureContinuousOverall Heightno
X6FeatureIntegerOrientationno
X7FeatureContinuousGlazing Areano
X8FeatureIntegerGlazing Area Distributionno
Y1TargetContinuousHeating Loadno
Y2TargetContinuousCooling Loadno

0 to 10 of 10

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

Edge Machine Learning: Enabling Smart Internet of Things Applications

By Mahmut Yazici, Shadi Basurra, Mohamed Gaber. 2018

Published in Big Data and Cognitive Computing.

Symbolic Regression Algorithms with Built-in Linear Regression

By Jan Zegklitz, Petr Posík. 2017

Published in ArXiv.

0 to 3 of 3

Reviews

There are no reviews for this dataset yet.

Login to Write a Review
Download
3 citations
46533 views

Creators

Athanasios Tsanas

Angeliki Xifara

License

By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Read Policy