Visual Clustering Analysis of Electricity Data Based on t-SNE

The electricity consumption data recorded by smart meters contain plenty of knowledge that contribute to consumers categorization. However, traditional unsupervised methods for electricity data clustering suffer from the following problems: 1) They are not specialized for the high-dimensional electricity data, which have high computational complexity and poor clustering effects. 2) Most of the methods need to specify the model parameters in advance, and the general enumeration methods are inefficient. To solve these problems, we propose a novel Visual Clustering Analysis framework of Electricity Data (VCAED). To improve the validity of electricity data clustering and reduce clustering complexity, the t-SNE dimensionality reduction technology is used to reduce dimensions with information loss as less as possible, and visualization also makes the parameter selection process more intuitive. Relevant experiments on the real-world electricity consumption datasets validate the effectiveness and practicability of the proposed method.