Enhancing Energy Consumption Time Series Clustering with Variational Mode Decomposition

The extensive use of Smart Metering Systems has revolutionized the monitoring and analysis of energy consumption data, enabling the derivation of representative demand profiles. The profiles are essential for optimizing energy distribution, however, clustering energy usage profiles presents a significant challenge, particularly when data inconsistencies arise from unsynchronized timestamps in records. Clustering algorithms need to separate noise from meaningful components while also managing the dimensionality of the data. This work proposes the use of Variational Mode Decomposition (VMD), which isolates intrinsic mode functions (IMFs), providing a clearer representation of energy consumption patterns. By integrating VMD with K-means clustering, we aim to improve the accuracy of demand profile classification. In this work we consider a real data sample from 2021 done by the National Electricity Administration of Paraguay (ANDE), encompassing data on energy use from 122 households in the Asunción metropolitan area.