人工神经网络
期限(时间)
超参数
贝叶斯概率
贝叶斯优化
降级(电信)
计算机科学
预测区间
均方误差
质子交换膜燃料电池
性能预测
人工智能
燃料电池
算法
工程类
机器学习
模拟
统计
数学
化学工程
物理
电信
量子力学
作者
Dongfang Chen,Wenlong Wu,Kuo‐Tsai Chang,Yuehua Li,Pucheng Pei,Xiaoming Xu
出处
期刊:Energy
[Elsevier]
日期:2023-12-01
卷期号:285: 129469-129469
被引量:9
标识
DOI:10.1016/j.energy.2023.129469
摘要
Proton exchange membrane (PEM) fuel cell is the core equipment that can directly convert hydrogen energy into electricity. In the process of long-term operation, due to the aging of membrane electrode assembly and other components, the fuel cell performance gradually deteriorates. The voltage prediction of fuel cells is very important for performance and lifetime optimization. Long short-term memory neural network is one of the widely used prediction methods. Based on the prediction method of bidirectional long short-term memory neural network, the hyperparameters of the neural network model by Bayesian optimization algorithm is optimized to improve the accuracy of fuel cell performance degradation prediction. When the sampling time interval is 25 min and the training set is 45 %, the root mean square error and the average absolute percentage error of the prediction results is reduced to 6.3 mV and 0.1245 %, respectively. Moreover, by analyzing the influence of different sampling time intervals and training set proportion on the prediction results, a data set that takes into accounts both efficiency and accuracy is obtained. The proposed method based on Bayesian optimization can achieve more accurate performance degradation prediction.
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