Filtered dataset of Irish energy performance certificates: A data-driven approach for enhanced building stock modellingMendeley DataGithubSEAI NBER
The data presented in this article supports the research publication “A data-driven standardised generalisable methodology to validate a large energy performance Certification dataset: A case of the application in Ireland” by Raushan et al. [1]. It provides the filtered Energy Performance Certificat...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000137 |
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| Summary: | The data presented in this article supports the research publication “A data-driven standardised generalisable methodology to validate a large energy performance Certification dataset: A case of the application in Ireland” by Raushan et al. [1]. It provides the filtered Energy Performance Certificate (EPC) database for residential buildings in Ireland after applying rigorous data validation methods to remove erroneous entries, and outliers. EPCs contain valuable information about building energy efficiency and characteristics. The raw EPC database for Ireland is publicly accessible but contains over 1 million unfiltered entries with inconsistent and erroneous values that can skew analysis. This processed dataset enhances the quality and robustness of the EPC data for use in building stock modelling and research. The data is openly available in .CSV format along with the methodology used for processing the raw database, published in full Python scripts. Supporting notes and metadata explain the filtering process, experimental design, and content of 211 variables across four categories: Informational, form, envelope, and system. By publishing this standardised data-driven filtered EPC dataset, this research enables stakeholders, non-expert and expert alike, to leverage this higher quality input for characterising the Irish housing stock. |
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| ISSN: | 2352-3409 |