Multihead Attention‐Driven TimeGAN‐GRU Hybrid Framework for PEMFC Stack Power Prediction

Proton exchange membrane fuel cells (PEMFCs) offer broad prospects due to their high efficiency and environmental friendliness. Stack power, a key performance metric, is highly influenced by operating conditions such as temperature and pressure. Thus, building a data‐driven model that captures the nonlinear dynamics between process variables and stack power is essential for real‐time prediction and optimization. However, the high cost of testing hydrogen fuel cells has resulted in a paucity of experimental data, which has hindered the development of high‐precision data‐driven models. To address these challenges, this paper presents an enhanced prediction method for PEMFC stack power that integrates the multihead attention mechanism into the TimeGAN‐GRU framework. Specifically, the multihead attention‐driven TimeGAN enhances the model's capacity to capture the global dynamic characteristics of time series, thereby mitigating the issue of limited data. The GRU model integrated with the multihead attention mechanism enhances the accuracy of modeling and predicting the complex nonlinear relationship between external process variables and stack power. The results show that, compared with traditional deep learning models, the proposed method improves stack power prediction accuracy across test sets and offers better generalization and data generation ability.