A High Performance Deep Learning Load Classification Method for the Massive Class Imbalanced Load Data

High performance analysis and processing technology of load data is an important basic of situation awareness technology in the context of power Internet of things. Under this background, a high performance deep learning load classification method for the massive class imbalanced load data is presented. Firstly, the mogrifier long short-term memory (Mogrifier-LSTM) network is presented as the basic classification algorithm to solve the deficiency of information interaction between input layer and hidden layer in traditional LSTM. In addition, attention mechanism and bidirectional feature fusion mechanism are introduced to further improve the ability of learning the association characteristics of time series data. Secondly, the Spark distributed computing framework is employed to realize the parallel processing of massive load data, which can improve the efficiency of classification task. And then, ensemble learning is introduced to improve the classification accuracy of the parallel processing. At last, the ADASYN method is employed to balance the load data, which can solve the potential class imbalance issue. The experiments show that the presented load classification method has advantages in terms of classification accuracy and efficiency.