Version 1
: Received: 21 August 2023 / Approved: 22 August 2023 / Online: 22 August 2023 (09:36:04 CEST)
How to cite:
Echabarri, S.; Do, P.; Vu, C.H.; Bornand, B. PEMFC Performance Forecasting Based on XGBRegressor and Tree-Structured Parzen. Preprints2023, 2023081535. https://doi.org/10.20944/preprints202308.1535.v1
Echabarri, S.; Do, P.; Vu, C.H.; Bornand, B. PEMFC Performance Forecasting Based on XGBRegressor and Tree-Structured Parzen. Preprints 2023, 2023081535. https://doi.org/10.20944/preprints202308.1535.v1
Echabarri, S.; Do, P.; Vu, C.H.; Bornand, B. PEMFC Performance Forecasting Based on XGBRegressor and Tree-Structured Parzen. Preprints2023, 2023081535. https://doi.org/10.20944/preprints202308.1535.v1
APA Style
Echabarri, S., Do, P., Vu, C.H., & Bornand, B. (2023). PEMFC Performance Forecasting Based on XGBRegressor and Tree-Structured Parzen. Preprints. https://doi.org/10.20944/preprints202308.1535.v1
Chicago/Turabian Style
Echabarri, S., Canh Hai Vu and Bastien Bornand. 2023 "PEMFC Performance Forecasting Based on XGBRegressor and Tree-Structured Parzen" Preprints. https://doi.org/10.20944/preprints202308.1535.v1
Abstract
The proton exchange membrane fuel cell (PEMFC) is a critical and essential component of zero-emission electro-hydrogen generators. An accurate prediction of its performance is important for optimal operation management and preventive maintenance of these generators. However, the prediction is not simple because the PEMFCs have complex electrochemical reactions with multiple nonlinear relations between operating variables as inputs and voltage as output. In this paper, we propose an efficient prediction approach based on XGBRegressor and Tree-structured Parzen Estimator. In addition, to better select relevant features, Kernel Principal Component Analysis and Mutual Information are jointly used. The proposed approach allows consideration of the dynamic operating conditions of the PEMFC. To test and validate the robustness of the proposed approach, a real data-set of ten PEMFCs was considered. Furthermore, a comparison study with traditional machine learning models, such as artificial neural networks and support vector machine regression, was investigated. The obtained results confirm that our model outperforms the considered traditionel machine learning models.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
