Dublin Core
Title
Quantitative estimation of cooling load capabilities of residential buildings using
machine learning
machine learning
Abstract
Based on previous research on energy efficiency of the buildings, particularly their cooling
load capabilities we will develop a collection of machine learning methods for detecting buildings
with best cooling load capabilities. This collection will study the influence of 8 input variables (relative
compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area
distribution) on one output parameter, that is cooling load of buildings. The results of this study
support the practicability of using machine-learning software to estimate building parameters as a
convenient and accurate approach, as long as the methods chosen are well suited for the type of data
in question.
load capabilities we will develop a collection of machine learning methods for detecting buildings
with best cooling load capabilities. This collection will study the influence of 8 input variables (relative
compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area
distribution) on one output parameter, that is cooling load of buildings. The results of this study
support the practicability of using machine-learning software to estimate building parameters as a
convenient and accurate approach, as long as the methods chosen are well suited for the type of data
in question.
Keywords
cooling load, energy efficiency, machine learning, neural network.
Identifier
2637-2835
DOI
10.14706/JONSAE2021315