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Volume 1, Issue 4 (Online First) - Table of Contents

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On the cover: This study employs machine learning models to classify Building Energy Ratings (BER) for residential dwellings in County Dublin, Ireland. With a focus on selecting relevant features from a highly correlated dataset, the study achieves a prediction accuracy of 69%. Among the twenty-plus machine learning models tested, the Light Gradient Boosting Machine Classifier demonstrates the best performance. Additionally, the study conducts retrofit experiments on dwelling features, evaluating their effectiveness in improving energy performance. These insights contribute to the goals of the Energy Performance of Buildings Directive (EPBD) applicable in Ireland, supported by statistical inferences.
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Free Access | Research Article | 21 November 2024
Enhancing Energy Performance in Irish Dwellings: A Machine Learning Approach to Retrofit Interventions
IECE Transactions on Social Statistics and Computing | Volume 1, Issue 4: 89-101, 2024 | DOI:10.62762/TSSC.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 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 o... More >

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