A strategy for enhancing system reliability and grid integration using bagging-driven ensemble framework
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Chao Li
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 R2 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 R2 value of 0.888772.
Acknowledgments
I would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.
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Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
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Informed consent: This option is not neccessary due to that the data were collected from the references.
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Author contributions: Authors’ contributions: The author contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Chao Li”. Also the first draft of the manuscript was written by Chao Li commented on previous versions of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used Large Language Models, AI, and Machine Learning tools for tasks such as language refinement, data analysis, or figure generation, with all outputs being reviewed and validated by the authors to ensure accuracy and originality. After using these tools/services, the authors reviewed and edited the content and take full responsibility for the content of the published article.
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Conflict of interest: The authors declare no competing of interests.
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Research funding: No Funding.
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Data availability: The authors do not have permissions to share data.
References
1. Al-Othman, A, Tawalbeh, M, Martis, R, Dhou, S, Orhan, M, Qasim, M, et al.. Artificial intelligence and numerical models in hybrid renewable energy systems with fuel cells: advances and prospects. Energy Convers Manag 2022;253:115154. https://doi.org/10.1016/j.enconman.2021.115154.Search in Google Scholar
2. Zaferani, SPG, Amiri, MK, Emami, MRS, Zahmatkesh, S, Hajiaghaei-Keshteli, M, Panchal, H. Prediction and optimization of sustainable fuel cells behavior using artificial intelligence algorithms. Int J Hydrogen Energy 2024;52:746–66.10.1016/j.ijhydene.2023.03.335Search in Google Scholar
3. Song, K, Huang, X, Cai, Z, Huang, P, Li, F. Research on energy management strategy of fuel-cell vehicles based on nonlinear model predictive control. Int J Hydrogen Energy 2024;50:1604–21. https://doi.org/10.1016/j.ijhydene.2023.07.304.Search in Google Scholar
4. Liu, C, Shen, J, Dong, Z, He, Q, Zhao, X. Accuracy improvement of fuel cell prognostics based on voltage prediction. Int J Hydrogen Energy 2024;58:839–51. https://doi.org/10.1016/j.ijhydene.2024.01.238.Search in Google Scholar
5. Piras, M, De Bellis, V, Malfi, E, Desantes, JM, Novella, R, Lopez-Juarez, M. Incorporating speed forecasting and SOC planning into predictive ECMS for heavy-duty fuel cell vehicles. Int J Hydrogen Energy 2024;55:1405–21. https://doi.org/10.1016/j.ijhydene.2023.11.250.Search in Google Scholar
6. Pavlatos, C, Makris, E, Fotis, G, Vita, V, Mladenov, V. Utilization of artificial neural networks for precise electrical load prediction. Technologies (Basel) 2023;11:70. https://doi.org/10.3390/technologies11030070.Search in Google Scholar
7. Nayyef, ZT, Abdulrahman, MM, Kurdi, NA. Optimizing energy efficiency in smart grids using machine learning algorithms: a case study in electrical engineering. SHIFRA 2024;2024:46–54. https://doi.org/10.70470/SHIFRA/2024/006.Search in Google Scholar
8. Pham, AD, Ngo, NT, Truong, TTH, Huynh, NT, Truong, NS. Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability. J Clean Prod 2020;260:121082.10.1016/j.jclepro.2020.121082Search in Google Scholar
9. Olu-Ajayi, R, Alaka, H, Sulaimon, I, Sunmola, F, Ajayi, S. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. J Build Eng 2022;45:103406. https://doi.org/10.1016/j.jobe.2021.103406.Search in Google Scholar
10. Mo, Y, Zhao, D, Syal, M. Effective features to predict residential energy consumption using machine learning. In: ASCE International Conference on Computing in Civil Engineering 2019. American Society of Civil Engineers, Reston, VA, Atlanta; 2019. p. 284–91.10.1061/9780784482445.036Search in Google Scholar
11. Bourhnane, S, Abid, MR, Lghoul, R, Zine-Dine, K, Elkamoun, N, Benhaddou, D. Machine learning for energy consumption prediction and scheduling in smart buildings. SN Appl Sci 2020;2:297. https://doi.org/10.1007/s42452-020-2024-9.Search in Google Scholar
12. Robinson, C, Dilkina, B, Hubbs, J, Zhang, W, Guhathakurta, S, Brown, MA, et al.. Machine learning approaches for estimating commercial building energy consumption. Appl Energy 2017;208:889–904. https://doi.org/10.1016/j.apenergy.2017.09.060.Search in Google Scholar
13. Fan, A, Wang, Y, Yang, L, Tu, X, Yang, J, Shu, Y. Comprehensive evaluation of machine learning models for predicting ship energy consumption based on onboard sensor data. Ocean Coast Manag 2024;248:106946. https://doi.org/10.1016/j.ocecoaman.2023.106946.Search in Google Scholar
14. El-Maraghy, M, Metawie, M, Safaan, M, Saad Eldin, A, Hamdy, A, El Sharkawy, M, et al.. Predicting energy consumption of mosque buildings during the operation stage using deep learning approach. Energy Build 2024;303:113829. https://doi.org/10.1016/j.enbuild.2023.113829.Search in Google Scholar
15. Alizadegan, H, Rashidi Malki, B, Radmehr, A, Karimi, H, Ilani, MA. Comparative study of long short-term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction. Energy Explor Exploit. 01445987241269496 2024;43:281–301. https://doi.org/10.1177/01445987241269496.Search in Google Scholar
16. Han, M, Fan, L. A short-term energy consumption forecasting method for attention mechanisms based on spatio-temporal deep learning. Comput Electr Eng 2024;114:109063. https://doi.org/10.1016/j.compeleceng.2023.109063.Search in Google Scholar
17. Pavlatos, C, Makris, E, Fotis, G, Vita, V, Mladenov, V. Enhancing electrical load prediction using a bidirectional LSTM neural network. Electronics (Basel) 2023;12:4652. https://doi.org/10.3390/electronics12224652.Search in Google Scholar
18. Fotis, G, Vita, V, Ekonomou, L. Machine learning techniques for the prediction of the magnetic and electric field of electrostatic discharges. Electronics (Basel) 2022;11:1858. https://doi.org/10.3390/electronics11121858.Search in Google Scholar
19. Montero-Sousa, JA, Aláiz-Moretón, H, Quintián, H, González-Ayuso, T, Novais, P, Calvo-Rolle, JL. Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach. Energy 2020;205:117986. https://doi.org/10.1016/j.energy.2020.117986.Search in Google Scholar
20. Liu, Y, Li, J, Chen, Z, Qin, D, Zhang, Y. Research on a multi-objective hierarchical prediction energy management strategy for range extended fuel cell vehicles. J Power Sources 2019;429:55–66. https://doi.org/10.1016/j.jpowsour.2019.04.118.Search in Google Scholar
21. Zeng, T, Zhang, C, Hao, D, Cao, D, Chen, J, Chen, J, et al.. Data-driven approach for short-term power demand prediction of fuel cell hybrid vehicles. Energy 2020;208:118319. https://doi.org/10.1016/j.energy.2020.118319.Search in Google Scholar
22. Li, D, Zhang, Z, Zhou, L, Liu, P, Wang, Z, Deng, J. Multi-time-step and multi-parameter prediction for real-world proton exchange membrane fuel cell vehicles (PEMFCVs) toward fault prognosis and energy consumption prediction. Appl Energy 2022;325:119703. https://doi.org/10.1016/j.apenergy.2022.119703.Search in Google Scholar
23. Deng, K, Liu, Y, Hai, D, Peng, H, Löwenstein, L, Pischinger, S, et al.. Deep reinforcement learning based energy management strategy of fuel cell hybrid railway vehicles considering fuel cell aging. Energy Convers Manag 2022;251:115030. https://doi.org/10.1016/j.enconman.2021.115030.Search in Google Scholar
24. Marimuthu, c. Energy forecasting in fuel cell. IEEE Dataport 2022.Search in Google Scholar
25. Awad, M, Khanna, R, Awad, M, Khanna, R. Support vector regression. In: Efficient learning machines: theories, concepts, and applications for engineers and system designers. Berkeley, CA: Apress; 2015:67–80 pp.10.1007/978-1-4302-5990-9_4Search in Google Scholar
26. Zhang, F, O’Donnell, LJ. Support vector regression. In: Machine learning. Amsterdam, Netherlands: Elsevier; 2020:123–40 pp.10.1016/B978-0-12-815739-8.00007-9Search in Google Scholar
27. Grossberg, S. Recurrent neural networks. Scholarpedia 2013;8:1888. https://doi.org/10.4249/scholarpedia.1888.Search in Google Scholar
28. Qin, Z, Yang, S, Zhong, Y. Hierarchically gated recurrent neural network for sequence modeling. Adv Neural Inf Process Syst 2024;36.Search in Google Scholar
29. Margineantu, DD, Dietterich, TG. Pruning adaptive boosting. In: ICML. Princeton, New Jersey: Citeseer; 1997:211–18 pp.Search in Google Scholar
30. Rojas, R. AdaBoost and the super bowl of classifiers a tutorial introduction to adaptive boosting. Freie Univ Berlin Tech Rep 2009;1:1–6.Search in Google Scholar
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