TY  - JOUR
AU  - Tripath, Samiksha
AU  - Kumar, Teerath
PY  - 2024
DA  - 2024/11/21
TI  - Enhancing Energy Performance in Irish Dwellings: A Machine Learning Approach to Retrofit Interventions
JO  - Journal of Social Systems and Policy Analysis
T2  - Journal of Social Systems and Policy Analysis
JF  - Journal of Social Systems and Policy Analysis
VL  - 1
IS  - 4
SP  - 100
EP  - 112
DO  - 10.62762/JSSPA.2024.898106
UR  - https://www.icck.org/article/abs/JSSPA.2024.898106
KW  - machine learning
KW  - BER
KW  - statistical
KW  - optimizing
AB  - 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 the Building Energy Rating (BER) of dwellings in County Dublin, Ireland. With a focus on feature selection in a highly correlated dataset, the study predicts energy ratings with an accuracy of 69%. The Light Gradient Boosting Machine classifier is observed to achieve the best performance among more than twenty ML models applied for prediction. The study also performs retrofit experiments on dwelling features and evaluates their effectiveness in improving energy performance, contributing to the Energy Performance of Buildings Directive (EPBD) applicable in Ireland, using statistical inference. This research highlights the potential of data-driven approaches for optimizing energy utilization and shaping policies for sustainable building practices.
SN  - 3068-5540
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Tripath2024Enhancing,
  author = {Samiksha Tripath and Teerath Kumar},
  title = {Enhancing Energy Performance in Irish Dwellings: A Machine Learning Approach to Retrofit Interventions},
  journal = {Journal of Social Systems and Policy Analysis},
  year = {2024},
  volume = {1},
  number = {4},
  pages = {100-112},
  doi = {10.62762/JSSPA.2024.898106},
  url = {https://www.icck.org/article/abs/JSSPA.2024.898106},
  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 the Building Energy Rating (BER) of dwellings in County Dublin, Ireland. With a focus on feature selection in a highly correlated dataset, the study predicts energy ratings with an accuracy of 69\%. The Light Gradient Boosting Machine classifier is observed to achieve the best performance among more than twenty ML models applied for prediction. The study also performs retrofit experiments on dwelling features and evaluates their effectiveness in improving energy performance, contributing to the Energy Performance of Buildings Directive (EPBD) applicable in Ireland, using statistical inference. This research highlights the potential of data-driven approaches for optimizing energy utilization and shaping policies for sustainable building practices.},
  keywords = {machine learning, BER, statistical, optimizing},
  issn = {3068-5540},
  publisher = {Institute of Central Computation and Knowledge}
}