Dataset and Code for 'Feature Extraction-based Clustering Selection Methodology to Identify Representative Buildings for Scalable Energy Simulations'

This repository contains the anonymized dataset and reproducible code accompanying the paper: “Feature extraction-based clustering selection methodology to identify representative buildings for scalable energy simulations.” The dataset includes: epc_metadata.csv: EPC-based building attributes (energy performance value, weighted U-value, air leakage, heat recovery) dh_merged_kwh_per_m2.csv: hourly district heating consumption, normalized per m² outdoor_temp_2023.csv: hourly outdoor temperature data (2023) features_epc_only_standardized.csv: standardized feature table used for clustering data_dictionary.xlsx: description of all variables The code provides a minimal pipeline to reproduce the main results of the paper: Load and preprocess data Extract EPC-based features Run clustering (K-Medoids, Agglomerative, GMM) Compute validation metrics and agreement indices Expected outputs (e.g., validation_metrics.csv, agreement_metrics.csv) reproduce the main tables in the manuscript. Anonymization: all identifiers (school names, locations, addresses) are removed. Only numerical features are included. Licenses: Code: MIT License Data: Creative Commons Attribution 4.0 International (CC BY 4.0) Related resources:The GitHub repository with the same reproducible pipeline is available at:https://github.com/hatefh/zenodo_code_bundle_epc_clustering_paper