Akademik Veri Yönetim Sistemi
3rd International Conference on Optimization and Data Science in Industrial Engineering, ODSIE 2025, Hybrid, Istanbul, Türkiye, 20 - 22 Kasım 2025, cilt.2854 CCIS, ss.415-425, (Tam Metin Bildiri)
The rapid growth of smart metering and sensing systems generates vast amounts of electricity consumption data that providers must analyse carefully to manage resources and costs effectively. This study presents a novel clustering framework that significantly enhances consumption profile segmentation by incorporating cyclic signal characteristics which capture inherent periodic behaviour. We derive phase-based descriptors using the Hilbert Transform, including circular mean, circular variance, and chord distance, that accurately represent temporal cycles in the data. We compare two experimental scenarios: Case 1 combines these cyclic descriptors with Principal Component Analysis components for feature generation, while Case 2 relies exclusively on Principal Component Analysis. We apply both feature sets to three diverse electricity consumption datasets and execute two clustering algorithms, DBSCAN and Spectral Clustering, which handle nonconvex shapes and complex affinities effectively. We evaluate performance using the Davies–Bouldin Index for cluster compactness and separation, and supervised accuracy for alignment with known labels. The results demonstrate clearly that adding cyclic descriptors yields notably better clustering quality, especially for datasets with pronounced temporal patterns, and that embedding functional data analysis methods into classic clustering pipelines improves both interpretability and robustness.