-
CiteScore
0.43
Impact Factor
Volume 1, Issue 4, IECE Transactions on Social Statistics and Computing
Volume 1, Issue 4, 2024
Submit Manuscript Edit a Special Issue
Academic Editor
Guojie Xie
Guojie Xie
Xiamen University of Technology, China
Article QR Code
Article QR Code
Scan the QR code for reading
Popular articles
IECE Transactions on Social Statistics and Computing, Volume 1, Issue 4, 2024: 89-101

Free to Read | Research Article | 21 November 2024
Enhancing Energy Performance in Irish Dwellings: A Machine Learning Approach to Retrofit Interventions
1 School of Computing, National College of Ireland, Dublin, Ireland
2 School of Computing, Dublin City University, Ireland
* Corresponding Author: Teerath Kumar, [email protected]
Received: 16 October 2024, Accepted: 03 November 2024, Published: 21 November 2024  
Abstract
This research investigates the impact of retrofit interventions on the energy performance of domestic buildings in Ireland using predictive machine learning (ML) models. The study applies machine learning models to classify Building Energy Rating (BER) for dwellings in County Dublin Ireland. Keeping the focus on selecting features in a highly correlated dataset, the study predicts energy ratings with an accuracy of 69 percent. Light Gradient Boosting Machine Classifier is observed for best performance among twenty plus ML models applied for prediction. The study also performs retrofit experiments on dwelling features and evaluate their effectiveness towards improving the energy performance of the dwelling contributing to Energy Performance of Buildings Directives (EPBD) applicable in Ireland using statistical inferences. This research discusses the potential of data driven approaches in optimizing energy utilisation and shaping policies for sustainable building practices.

Graphical Abstract
Enhancing Energy Performance in Irish Dwellings: A Machine Learning Approach to Retrofit Interventions

Keywords
machine learning
BER
statistical
optimizing

Funding
This work was supported without any funding.

References
  1. Kumar, T., Brennan, R., & Bendechache, M. (2022, January). Stride Random Erasing Augmentation. In CS & IT Conference Proceedings (Vol. 12, No. 2).
    [Google Scholar]
  2. Kumar, T., Mileo, A., Brennan, R., & Bendechache, M. (2023). RSMDA: Random Slices Mixing Data Augmentation. Applied Sciences, 13(3), 1711.
    [Google Scholar]
  3. Kumar, T., Turab, M., Talpur, S., Brennan, R., & Bendechache, M. (2022). Forged character detection datasets: passports, driving licences and visa stickers. Int. J. Artif. Intell. Appl.(IJAIA), 13, 21-35.
    [Google Scholar]
  4. Roy, A. M., Bhaduri, J., Kumar, T., & Raj, K. (2022). A computer vision-based object localization model for endangered wildlife detection. Ecological Economics, Forthcoming.
    [Google Scholar]
  5. Ranjbarzadeh, R., Jafarzadeh Ghoushchi, S., Tataei Sarshar, N., Tirkolaee, E. B., Ali, S. S., Kumar, T., & Bendechache, M. (2023). ME-CCNN: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognition. Artificial Intelligence Review, 56(9), 10099-10136.
    [Google Scholar]
  6. Aleem, S., Kumar, T., Little, S., Bendechache, M., Brennan, R., & McGuinness, K. (2022). Random data augmentation based enhancement: a generalized enhancement approach for medical datasets. arXiv preprint arXiv:2210.00824.
    [Google Scholar]
  7. Kumar, T., Park, J., Ali, M. S., Uddin, A. F. M., & Bae, S. H. (2021). Class specific autoencoders enhance sample diversity. Journal Of Broadcast Engineering, 26(7), 844-854.
    [Google Scholar]
  8. Kumar, T., Brennan, R., Mileo, A., & Bendechache, M. (2024). Image data augmentation approaches: A comprehensive survey and future directions. IEEE Access.
    [Google Scholar]
  9. Turab, M., Kumar, T., Bendechache, M., & Saber, T. (2022). Investigating multi-feature selection and ensembling for audio classification. arXiv preprint arXiv:2206.07511.
    [Google Scholar]
  10. Raj, K., Singh, A., Mandal, A., Kumar, T., & Roy, A. M. (2023). Understanding EEG signals for subject-wise definition of armoni activities. arXiv preprint arXiv:2301.00948.
    [Google Scholar]
  11. Kumar, T., Park, J., Ali, M. S., Uddin, A. S., Ko, J. H., & Bae, S. H. (2021). Binary-classifiers-enabled filters for semi-supervised learning. IEEE Access, 9, 167663-167673.
    [Google Scholar]
  12. Kumar, T., Turab, M., Mileo, A., Bendechache, M., & Saber, T. (2023). AudRandAug: Random Image Augmentations for Audio Classification. arXiv preprint arXiv:2309.04762.
    [Google Scholar]
  13. Chandio, A., Shen, Y., Bendechache, M., Inayat, I., & Kumar, T. (2021). AUDD: audio Urdu digits dataset for automatic audio Urdu digit recognition. Applied Sciences, 11(19), 8842.
    [Google Scholar]
  14. Park, J., Kumar, T., & Bae, S. H. (2020). Search of an optimal sound augmentation policy for environmental sound classification with deep neural networks. In Proceedings Of The Korean Society Of Broadcast Engineers Conference (pp. 18-21). The Korean Institute of Broadcast and Media Engineers.
    [Google Scholar]
  15. Kumar, T., Park, J., & Bae, S. H. (2020). Intra-Class Random Erasing (ICRE) augmentation for audio classification. In Proceedings Of The Korean Society Of Broadcast Engineers Conference (pp. 244-247). The Korean Institute of Broadcast and Media Engineers.
    [Google Scholar]
  16. Borràs, I. M., Neves, D., & Gomes, R. (2023). Using urban building energy modeling data to assess energy communities’ potential. Energy and Buildings, 282, 112791.
    [Google Scholar]
  17. Nutkiewicz, A., Yang, Z., & Jain, R. K. (2017). Data-driven Urban Energy Simulation (DUE-S): Integrating machine learning into an urban building energy simulation workflow. Energy Procedia, 142, 2114-2119.
    [Google Scholar]
  18. Min, J., Yan, G., Abed, A. M., Elattar, S., Khadimallah, M. A., Jan, A., & Ali, H. E. (2022). The effect of carbon dioxide emissions on the building energy efficiency. Fuel, 326, 124842.
    [Google Scholar]
  19. Ali, U., Bano, S., Shamsi, M. H., Sood, D., Hoare, C., Zuo, W., ... & O’Donnell, J. (2024). Urban residential building stock synthetic datasets for building energy performance analysis. Data in Brief, 53, 110241.
    [Google Scholar]
  20. Ali, U., Bano, S., Shamsi, M. H., Sood, D., Hoare, C., Zuo, W., ... & O’Donnell, J. (2024). Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach. Energy and Buildings, 303, 113768.
    [Google Scholar]
  21. Li, Y., Kubicki, S., Guerriero, A., & Rezgui, Y. (2019). Review of building energy performance certification schemes towards future improvement. Renewable and Sustainable Energy Reviews, 113, 109244.
    [Google Scholar]
  22. Wenninger, S., Kaymakci, C., & Wiethe, C. (2022). Explainable long-term building energy consumption prediction using QLattice. Applied Energy, 308, 118300.
    [Google Scholar]
  23. Ferrantelli, A., Belikov, J., Petlenkov, E., Thalfeldt, M., & Kurnitski, J. (2022). Evaluating the energy readiness of national building stocks through benchmarking. IEEE Access, 10, 45430-45443.
    [Google Scholar]
  24. Miller, C. (2019). What’s in the box?! Towards explainable machine learning applied to non-residential building smart meter classification. Energy and Buildings, 199, 523-536.
    [Google Scholar]
  25. Xiao, Q., Liu, D., & Credit, K. (2024). Model Failure or Data Corruption? Exploring Inconsistencies in Building Energy Ratings with Self-Supervised Contrastive Learning. arXiv preprint arXiv:2404.12399.
    [Google Scholar]
  26. Hyland, M., Lyons, R. C., & Lyons, S. (2013). The value of domestic building energy efficiency—evidence from Ireland. Energy economics, 40, 943-952.
    [Google Scholar]
  27. McGarry, K. (2023). What Impact is Occupancy Behavior having on the Energy Performance Gap of a cohort of A rated Dwellings?.
    [Google Scholar]
  28. Khayatian, F., & Sarto, L. (2016). Application of neural networks for evaluating energy performance certificates of residential buildings. Energy and Buildings, 125, 45-54.
    [Google Scholar]
  29. Zhang, Y., Teoh, B. K., Wu, M., Chen, J., & Zhang, L. (2023). Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence. Energy, 262, 125468.
    [Google Scholar]
  30. Seo, J., Kim, S., Lee, S., Jeong, H., Kim, T., & Kim, J. (2022). Data-driven approach to predicting the energy performance of residential buildings using minimal input data. Building and Environment, 214, 108911.
    [Google Scholar]
  31. Araújo, G. R., Gomes, R., Ferrão, P., & Gomes, M. G. (2024). Optimizing building retrofit through data analytics: A study of multi-objective optimization and surrogate models derived from energy performance certificates. Energy and Built Environment, 5(6), 889-899.
    [Google Scholar]
  32. Saravanan, R., Swaminathan, A., & Balaji, S. (2023). An intelligent shell game optimization based energy consumption analytics model for smart metering data. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 23(2), 374-381.
    [Google Scholar]

Cite This Article
APA Style
Tirpathi, S., & Kumar, T. (2024). Enhancing Energy Performance in Irish Dwellings: A Machine Learning Approach to Retrofit Interventions. IECE Transactions on Social Statistics and Computing, 1(4), 89–101. https://doi.org/10.62762/TSSC.2024.898106

Article Metrics
Citations:

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 553
PDF Downloads: 161

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
Institute of Emerging and Computer Engineers (IECE) or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
IECE Transactions on Social Statistics and Computing

IECE Transactions on Social Statistics and Computing

ISSN: 2996-8488 (Online)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.