K-Means Clustering Analysis to Identify Electrical Load Consumption Patterns Based on Primary Energy Consumption

Electricity consumption in Indonesia has experienced significant growth along with economic development and infrastructure expansion. However, the main challenge lies in managing energy efficiently to meet the growing demand. A deep understanding of electricity load consumption patterns is essential for effective energy management. The K-Means Clustering method is used in this study to cluster primary energy consumption data and electricity load consumption. This study aims to implement electricity load modeling by finding the closest data distribution distance, which is divided into 3 clusters. From the results of the implementation of the method, a value of 0.067820466 was obtained as the closest minimum value in cluster 1 of the untransposed system load data, 0.533624 as the minimum value distance in cluster 2 of the transposed system load data, and 0.067820466 as the minimum value distance in cluster 3 of the power plant data with a silhouette coefficient value indicating good and imperfect cluster quality in the data used.