The transformation from centralized and fossil-based electricity generation to distributed and renewable energy sources is an inevitable trend in the energy industry. One of the prime challenges in this transformation is the task of load/battery management, especially at the residential level. In solving this task, it is critical that a good strategy for analyzing and grouping residential electricity consumption patterns is in place so that further optimization strategies can be devised for different groups of consumers. Based on the real data from an Australian electricity retailer, we propose a clustering process to determine typical customer load profiles. It can be served as a standard framework for dealing with real-world unsupervised problems. In addition, some statistical techniques, including cumulative sum and calculation of the most frequent value in dataset by using mode, are integrated into our data preprocessing and analysis. CUSUM chart is a graphical method to clearly visualize as well as detect changes in time-series data and then using mode values is to replace missing values in the dataset. Furthermore, in our framework, more practical Elbow method is conducted to determine appropriated number of clusters for k-centers algorithm. We then apply multiple state-of-the-art clustering methods for time series data and benchmark their respective performance. We found that k-centers clustering techniques produces better results compared to exemplar-based methods. Additionally, choosing appropriated number of clusters for k-means can improve performance of clustering model. For example, k-means++ with k=2 has significantly outperformed other methods in our experiment.