A strategy for enhancing system reliability and grid integration using bagging-driven ensemble framework

Abstract Fuel cell energy systems have a potential as a clean and efficient power generation method, but suffer a high cost of production and engineering of fuel cells, multi-faceted operating principles, loss of performance over time, and vulnerability under the changing operation. Therefore, accurate predictions of energy output from fuel cell systems are critical for performance optimization, ensuring stable integration into the energy grids, and improving overall economic viability. This work presents advanced research on predictive modeling for fuel cell system energy forecasting, with a focus on increasing the accuracy and reliability of energy forecasting. The main innovation of this work is the use of the Bootstrap Aggregation (Bagging) ensemble farmwork that aggregate the outputs of multiple models such as Support Vector Regression, Long Short-Term Memory, Recurrent Neural Networks, and AdaBoost to mitigate individual model biases, reduce variance, and enhance generalization performance. The findings indicates that the Bagging algorithm performed excellently in improving the accuracy of energy output from fuel cell predictions by exploiting the benefits of multiple learners. It shows the best R 2 of 0.984352, with lowest MAE and RMSE equal to 17.00 and 23.65 on training set meaning that it perfectly fitted the predicted and actual values. Notably, Bagging continues with good generalization in the test set having the lowest MAE and RMSE equal to 44.60 and 60.77 and the highest R 2 value of 0.888772.