Prediction of Cooling Load of Tropical Buildings with Machine Learning

Gebrail Bekdaş (), Yaren Aydın, Ümit Isıkdağ, Aidin Nobahar Sadeghifam, Sanghun Kim and Zong Woo Geem ()
Additional contact information
Gebrail Bekdaş: Department of Civil Engineering, Istanbul University-Cerrahpaşa, Istanbul 34320, Turkey
Yaren Aydın: Department of Civil Engineering, Istanbul University-Cerrahpaşa, Istanbul 34320, Turkey
Ümit Isıkdağ: Department of Informatics, Mimar Sinan Fine Arts University, Istanbul 34427, Turkey
Aidin Nobahar Sadeghifam: Department of Civil Engineering, Curtin University Malaysia, Miri 98009, Malaysia
Sanghun Kim: Department of Civil and Environmental Engineering, Temple University, Philadelphia, PA 19122, USA
Zong Woo Geem: Department of Smart City, Gachon University, Seongnam 13120, Republic of Korea

Sustainability, 2023, vol. 15, issue 11, 1-17

Abstract: Cooling load refers to the amount of energy to be removed from a space (or consumed) to bring that space to an acceptable temperature or to maintain the temperature of a space at an acceptable range. The study aimed to develop a series of models and determine the most accurate ones in the prediction of the cooling load of low-rise tropical buildings based on their basic architectural and structural characteristics. In this context, a series of machine learning (regression) algorithms were tested during the research to determine the most accurate/efficient prediction model. In this regard, a data set consisting of ten features indicating the basic characteristics of the building (floor area, aspect ratio, ceiling height, window material, external wall material, roof material, window wall ratio north faced, window wall ratio south faced, horizontal shading, orientation) were used to predict the cooling load of a low-rise tropical building. The dataset was generated utilizing a set of generative and algorithmic design tools. Following the dataset generation, a series of regression models were tested to find the most accurate model to predict the cooling load. The results of the tests with different algorithms revealed that the relationship between the predictor variables and cooling load could be efficiently modeled through Histogram Gradient Boosting and Stacking models.

Keywords: cooling load; building; predictive modelling; energy efficiency (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/11/9061/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/11/9061/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:11:p:9061-:d:1163328

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().