Charging Pile Fault Prediction Model Based on GRU Network and WOA

Abstract

The global energy structure is transforming, and new energy vehicles are becoming the future of the automobile industry. However, the development of charging piles and related facilities has not kept pace with the growth of new energy vehicles. This study uses the gated recurrent unit network and the whale algorithm to construct a high-performance charging pile fault prediction model. The proposed model, which utilizes the whale algorithm to prevent the gated recurrent unit network from falling into local optima, demonstrates improved predictive information extraction and prediction ability. The experimentally verified results indicate that the proposed model achieved 92.02% prediction accuracy, 85.66% recall, and 93.87% F1 value. Additionally, the proposed model demonstrates excellent computational ability with an average running time of under 5 minutes on both datasets. This result is a substantial reduction from the control model's running time. The experimental findings show that the study's suggested model has a good ability to anticipate fault data. Its sophistication is verified by comparative tests, which can provide a reference for subsequent research.