Load profiling is essential in power systems operation and planning. Accurate load profiles lead to a better load scheduling as well as load and price forecasting. Clustering techniques are used to provide an enhanced knowledge on electrical load patterns. This paper deals with clustering methods to analyze Queensland's load data. The K-means clustering method is used here, where its accuracy is measured using the clustering dispersion indicator (CDI). This method is applied on the Queensland load curves in 2013, where distinct monthly and yearly load profiles are obtained. In addition, the characteristic of each load profile depending on the day type and weather conditions are analyzed.