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An explainable deep learning model for energy performance classification and retrofitting recommendations
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DOI：10.1016/j.enbuild.2025.116522.png)
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Abstract
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• Achieved 99.98% test accuracy in classifying building energy efficiency. • Introduced a deep learning model with L2 regularisation and dropout layers. • Balanced EPC dataset using SMOTE for improved fairness and generalisation. • Applied SHAP for explainability and feature importance analysis. • Provided counterfactuals for personalised retrofit recommendations.
Keywords:
Building retrofit strategies
Explainable artificial intelligence
Deep learning
Synthetic minority over-sampling technique
Shapley additive explanations explainability
Energy efficiency
Sustainable development goals
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