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Analysis of Household Electricity Consumption Patterns Using K-Means Clustering
Abstract
The continuous increase in household electricity consumption and irregular usage patterns pose challenges for efficient energy management. Irregular electricity load patterns can lead to energy waste, increased costs, and disruptions to the distribution system. Therefore, a method capable of accurately identifying electricity consumption patterns is needed. This study aims to analyze household electricity consumption patterns using the K-Means Clustering algorithm. The data used comes from PLN UP3 Kendari in the form of historical data for the 2023–2025 period and real-time data processed into daily parameters. The K-Means method was used to cluster the data based on the similarity of electricity consumption characteristics. The determination of the optimal number of clusters was performed using the Elbow Method and the Silhouette Score. The results of the study indicate that the optimal number of clusters is four, with a Silhouette Score of 0.5122 and a Davies-Bouldin Index of 0.6881, indicating that the cluster quality is quite good. Each cluster represents a distinct consumption pattern: low, stable medium, high, and fluctuating. Thus, the K-Means Clustering algorithm is effective for identifying household electricity consumption patterns.
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DOI: https://doi.org/10.17509/coelite.v5i2.98496
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