<![CDATA[The increasing adoption of Internet of Things (IoT) devices in smart homes has generated continuous and complex energy consumption data, requiring effective clustering techniques to identify household energy usage patterns. This study aims to cluster household energy consumption patterns using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm based on sensor time-series data. The study utilized the Smart Home Energy Consumption Dataset, consisting of approximately 90,000 observations with six main variables: Energy Consumption, Peak Hours Usage, Household Size, Average Temperature, Has AC, and Weekday. The research workflow included feature selection, data cleaning, data normalization using StandardScaler, parameter determination through the K-Distance Graph, DBSCAN clustering, and clustering evaluation using the Silhouette Score. Experimental results indicated that the optimal parameters were ε = 0.38 and MinPts = 5, producing 89 clusters, 541 noise observations (0.60%), and a Silhouette Score of 0.0690. Cluster characteristic analysis revealed that energy consumption, peak-hour energy usage, air conditioner ownership, household size, and ambient temperature were the primary factors distinguishing household energy usage patterns. The findings demonstrate that DBSCAN effectively identifies household energy consumption patterns while detecting outliers without requiring the number of clusters to be predefined, making it a promising approach for supporting intelligent energy management systems in smart home environments.]]>
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