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Volume 1, Issue 4, IECE Transactions on Social Statistics and Computing
Volume 1, Issue 4, 2024
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Xiamen University of Technology, China
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IECE Transactions on Social Statistics and Computing, Volume 1, Issue 4, 2024: 92-104

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

Data Availability Statement
Data will be made available on request.

Funding
This work was supported without any funding.

Conflicts of Interest
The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate
Not applicable.

References
  1. Cho, S. H., Lee, K. T., Kim, S. H., & Kim, J. H. (2019). Image processing for sustainable remodeling: Introduction to real-time quality inspection system of external wall insulation works. Sustainability, 11(4), 1081.
    [CrossRef]   [Google Scholar]
  2. Cai, W., Wen, X., Wang, S., & Wang, L. (2019). A real-time detection method of building energy efficiency based on image processing. Journal of Visual Communication and Image Representation, 60, 295-304.
    [CrossRef]   [Google Scholar]
  3. Tang, Y., Ten, C. W., Wang, C., & Parker, G. (2015). Extraction of energy information from analog meters using image processing. IEEE transactions on smart grid, 6(4), 2032-2040.
    [CrossRef]   [Google Scholar]
  4. Sharma, A., Singh, P. K., & Kumar, Y. (2020). An integrated fire detection system using IoT and image processing technique for smart cities. Sustainable Cities and Society, 61, 102332.
    [CrossRef]   [Google Scholar]
  5. Burger, W., & Burge, M. J. (2022). Digital image processing: An algorithmic introduction. Springer Nature.
    [Google Scholar]
  6. Monga, V., Li, Y., & Eldar, Y. C. (2021). Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing. IEEE Signal Processing Magazine, 38(2), 18-44.
    [CrossRef]   [Google Scholar]
  7. Chitradevi, B., & Srimathi, P. (2014). An overview on image processing techniques. International Journal of Innovative Research in Computer and Communication Engineering, 2(11), 6466-6472.
    [Google Scholar]
  8. Nadkarni, P. M., Ohno-Machado, L., & Chapman, W. W. (2011). Natural language processing: an introduction. Journal of the American Medical Informatics Association, 18(5), 544-551.
    [Google Scholar]
  9. Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266.
    [CrossRef]   [Google Scholar]
  10. Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., ... & Roth, D. (2023). Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys, 56(2), 1-40.
    [CrossRef]   [Google Scholar]
  11. Lauriola, I., Lavelli, A., & Aiolli, F. (2022). An introduction to deep learning in natural language processing: Models, techniques, and tools. Neurocomputing, 470, 443-456.
    [CrossRef]   [Google Scholar]
  12. Purwins, H., Li, B., Virtanen, T., Schlüter, J., Chang, S. Y., & Sainath, T. (2019). Deep learning for audio signal processing. IEEE Journal of Selected Topics in Signal Processing, 13(2), 206-219.
    [CrossRef]   [Google Scholar]
  13. Scott, S. K. (2005). Auditory processing—speech, space and auditory objects. Current opinion in neurobiology, 15(2), 197-201.
    [Google Scholar]
  14. Yang, Y. Y., Hira, M., Ni, Z., Astafurov, A., Chen, C., Puhrsch, C., ... & Quenneville-Bélair, V. (2022, May). Torchaudio: Building blocks for audio and speech processing. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6982-6986). IEEE.
    [CrossRef]   [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]

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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), 92–104. https://doi.org/10.62762/TSSC.2024.898106

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