<![CDATA[The increasing demand for household energy has created significant challenges for energy sustainability, resource management, and the development of effective energy efficiency strategies, thereby necessitating advanced analytical approaches to better understand residential consumption behavior. This study aims to analyze household energy consumption patterns using K-Means clustering and explainable data mining techniques. Household energy consumption data were collected from residential users and subjected to preprocessing procedures, including data cleaning, missing value handling, feature selection, and normalization to ensure data quality and analytical reliability. The K-Means clustering algorithm was then applied to identify homogeneous groups of households based on their energy consumption characteristics, while explainable data mining techniques were employed to interpret cluster profiles and determine the factors influencing cluster membership. The results revealed the existence of three distinct household energy consumption groups, namely low-, moderate-, and high-consumption households, each exhibiting significantly different consumption behaviors, appliance ownership levels, and energy expenditure patterns. Further analysis showed that household size, appliance ownership, and peak electricity usage were the most influential factors differentiating the clusters. These findings demonstrate that the integration of K-Means clustering and explainable data mining provides an effective and interpretable framework for understanding household energy consumption behavior. The proposed approach offers valuable insights for utility companies, policymakers, and consumers by supporting targeted energy efficiency programs, demand-side management initiatives, and evidence-based energy policy development aimed at promoting sustainable household energy consumption.]]>
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