An improved selective ensemble learning approach in enabling load classification considering base classifier redundancy and class imbalance

In modern power systems, analyzing the behaviors of the end users can help to improve the system's security, stability, and economy. Load classification provides an efficient way to implement awareness of the user's behaviors. However, due to the development of data collection, transmission, and storage technologies, the volumes of the load data keep increasing. Meanwhile, the structure and knowledge hidden in the data become ever more complicated. Therefore, the parallelized ensemble learning method has been widely employed in recent load classification research. Although the positive performance of ensemble learning has been proven, two critical issues remain: class imbalance and base classifier redundancy. These issues raise challenges of improving the classification accuracy and saving computational resources. Therefore, to solve the issues, this article presents an improved selective ensemble learning approach to enable load classification considering base classifier redundancy and class imbalance. First, a Gaussian SMOTE based on density clustering (GSDC) is introduced to handle the class imbalance, which aims to achieve higher classification accuracy. Second, the classifier pruning strategy and the optimization strategy of the ensemble learning are further introduced to handle the base classifier redundancy. The experimental results indicate that when combined with the popular classifiers, the presented approach shows effectiveness for serving the load classification tasks.

Automated summary

This article presents an improved selective ensemble learning approach to handle class imbalance and base classifier redundancy in load classification. Experimental results show that the approach is effective for load classification tasks.