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Hussien, A, Khan, W, Hussain, A, Liatsis, P, Al-Shamma'a, A and Al-Jumeily, D (2023) Predicting energy performances of buildings' envelope wall materials via the random forest algorithm. Journal of Building Engineering, 69. ISSN 2352-7102
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Accepted_Predicting Energy Performances of Buildings.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
Abstract
Purpose: Numerous simulation software has been used to evaluate energy performance with 12% of the research focusing on long-term energy consumption prediction. This paper aims to utilize machine learning to predict the energy performance of building envelope wall materials over extended periods.
Methodology: In our work, machine learning model learns from a large set of building envelopes simulated using the Integrated Environmental Solutions Virtual Environment as follows:
Findings: Machine Learning models can also be used to predict the impact of building design and construction characteristics on energy consumption, showing that factors such as wall thickness, orientation, and thermal mass indicated lower relative standard error (<0.001); however, not all of them were statistically significant (p > 0.05). While the overall model indicates statistical significance (p = 2e-16), the multivariate linear regression model produces R2 value of 0.42, indicating a weak relationship between predictor variables and target attributes.
Originality: The utilisation of Random forest algorithm for the wall envelop energy consumption.
Research implecation: different to other techniques, our proposed approach addressed the issue related to building envelop for new constructions to assist professional from construction industry.
| Item Type: | Article |
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| Uncontrolled Keywords: | 0905 Civil Engineering; 1201 Architecture; 1202 Building |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Elsevier |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 20 Mar 2023 10:30 |
| Last Modified: | 15 Mar 2024 00:50 |
| DOI or ID number: | 10.1016/j.jobe.2023.106263 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/19131 |
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