Publicação
Deep learning classification approaches and applications for energy performance certificates (EPCs)
| Resumo: | Building energy performance classification is a cornerstone of sustainable development initiatives. This study presents an innovative approach leveraging Artificial Neural Networks to classify Energy Performance Certificates. Our Artificial Neural Networks model, integrating the Synthetic Minority Oversampling Technique for class balancing and Principal Component Analysis for dimensionality reduction, achieved a test accuracy of 93.44 %, supported by a macro and weighted F1-score of 0.93, outperforming many existing models and creating a unique sequence and combination of methods to conclude in that result. A detailed analysis of class-level performance underscores its robustness for high-rated energy classes while revealing challenges in differentiating lower-rated classes. This work bridges the gap between high-performance AI models and their interpretability, setting a benchmark for future energy performance certificate classification studies. |
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| Autores principais: | Anastasiadou, Maria |
| Outros Autores: | Santos, Vítor; Dias, Miguel Sales |
| Assunto: | Energy Performance Certificates Artificial Intelligence Deep learning Artificial Neural Networks Synthetic Minority Oversampling Technique Principal Component Analysis 7th Sustainable Development Goal Civil and Structural Engineering Building and Construction Modelling and Simulation Renewable Energy, Sustainability and the Environment Fuel Technology Energy Engineering and Power Technology Pollution Mechanical Engineering General Energy Industrial and Manufacturing Engineering Management, Monitoring, Policy and Law Electrical and Electronic Engineering SDG 7 - Affordable and Clean Energy |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Building energy performance classification is a cornerstone of sustainable development initiatives. This study presents an innovative approach leveraging Artificial Neural Networks to classify Energy Performance Certificates. Our Artificial Neural Networks model, integrating the Synthetic Minority Oversampling Technique for class balancing and Principal Component Analysis for dimensionality reduction, achieved a test accuracy of 93.44 %, supported by a macro and weighted F1-score of 0.93, outperforming many existing models and creating a unique sequence and combination of methods to conclude in that result. A detailed analysis of class-level performance underscores its robustness for high-rated energy classes while revealing challenges in differentiating lower-rated classes. This work bridges the gap between high-performance AI models and their interpretability, setting a benchmark for future energy performance certificate classification studies. |
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