A Load Classification Method Based on Hybrid Clustering of Continuous–Discrete Electricity Consumption Characteristics

There are numerous quantities and types of electrical loads, and their electrical characteristics have similarities and differences. To adapt to the development trend of refined management and scheduling on the load side, it is necessary to explore the electricity consumption patterns of loads and classify them. However, the classification performance is affected by data redundancy, the complexity of feature selection, and the diversity of power consumption behavior. To adapt to the development trend of refined management and scheduling on the load side, it is imperative to classify loads based on their electrical characteristics. Firstly, based on a statistical analysis of load-side electricity consumption data, the monthly electricity consumption of each load throughout the year is extracted to reflect the continuous electricity consumption characteristics of each load. By calculating the annual load rate, maximum load utilization hours, and rated capacity of each load and then using a Gaussian Mixture Model (GMM) for clustering analysis, the discrete electricity consumption characteristics of each load are obtained. Then, based on the K-prototypes clustering model, a load classification method is proposed based on continuous and discrete hybrid electricity characteristics. By setting the weight between continuous and discrete electrical characteristics, the optimal number of categories can be determined through the elbow method. Finally, using 86 industrial electricity-consuming enterprises in a region of Northwest China as experimental subjects, the results demonstrate that the method proposed in this study outperforms the K-means, GMM, and Gower.