Control-oriented dynamic identification modeling of a planar SOFC stack based on genetic algorithm-least squares support vector regression

Huo, Haibo; Ji, Yi; Zhu, Xun; Kuang, Xinghong; Liu, Yuqing

doi:10.1631/jzus.a1400011

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…This time, the data are still from the fuel cell research center of Huazhong University of science and technology. The DAG method is compared with other methods such as ELM [40], GA-LSSVR [41], SVR [42], S-LSTM [43], SPGP [44], and stk-ANN [45]. The comparison index parameters are MAE and RMSE.…”

Section: Second Verification Of Proposed Methods Effectiveness For Th...mentioning

confidence: 99%

A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency

Wu,

et al. 2023

Sustainability

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Efficiency prediction plays a crucial role in the ongoing development of electrochemical energy technology. Our industries heavily depend on a reliable energy supply for power and electricity, and solid oxide fuel cell (SOFC) systems stand out as renewable devices with immense potential. SOFCs, as one of the various types of fuel cells, are renowned for their capability of combined heat and power generation. They can achieve an efficiency of up to 90% in operation. Furthermore, due to the fact that water is the byproduct of their electricity generation process, they are extremely environmentally friendly, contributing significantly to humanity’s sustainable development. With the advancement of renewable energy technologies and the increasing emphasis on sustainable development requirements, predicting and optimizing the efficiency of SOFC systems is gaining importance. This study leverages data collected from an SOFC system and applies an improved neural network structure, specifically the dendritic network (DN) architecture, to forecast thermoelectric efficiency. The key advantage of this method lies in the adaptive neural network algorithm based on the dendritic network structure without manually setting hidden nodes. Moreover, the predicted model of thermoelectric efficiency is validated using 682 and 1099 h of operational data from the SOFC system, and the results are compared against a conventional machine learning method. After comparison, it is found that when the novel method with adaptive characteristics proposed was used for SOFC system efficiency prediction, the MAE and RMSE values were both lower than 0.014; the result is significantly better than from other traditional methods. Additionally, this study demonstrated its effectiveness in predicting the thermoelectric efficiency of SOFC systems through secondary experiments. This study offers guidance on enhancing SOFC systems thermoelectric efficiency. Therefore, this study provides a foundation for the future industrialization of fuel cell systems.

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Section: Second Verification Of Proposed Methods Effectiveness For Th...mentioning

confidence: 99%

A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency

Wu,

et al. 2023

Sustainability

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“…It has been widely applied in short-term prediction problems, such as traffic flow forecasting (Castro-Neto et al, 2009) and electric load forecasting . Descriptions of identification algorithms for the SVR model can be found in (Castro-Neto et al, 2009;Fan and Tang, 2013;Huo et al, 2014).…”

Section: Formulation Descriptions Of Svr Modelmentioning

confidence: 99%

A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion

Duan

Huang

Han

et al. 2015

J. Zhejiang Univ. Sci. A

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Accurate and reliable short-term prediction of ship motions offers improvements in both safety and control quality in ship motion sensitive maritime operations. Inspired by the satisfactory nonlinear learning capability of a support vector regression (SVR) model and the strong non-stationary processing ability of empirical mode decomposition (EMD), this paper develops a hybrid autoregressive (AR)-EMD-SVR model for the short-term forecast of nonlinear and non-stationary ship motion. The proposed hybrid model is designed by coupling the SVR model with an AR-EMD technique, which employs an AR model in ends extension. In addition to the AR-EMD-SVR model, the linear AR model, non-linear SVR model, and hybrid EMD-AR model are also studied for comparison by using ship motion time series obtained from model testing in a towing tank. Prediction results suggest that the non-stationary difficulty in the SVR model is overcome by using the AR-EMD technique, and better predictions are obtained by the proposed AR-EMD-SVR model than other models.

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Recent approach based heterogeneous comprehensive learning Archimedes optimization algorithm for identifying the optimal parameters of different fuel cells

Fathy

Babu

Abdelkareem

et al. 2022

Energy

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Control-oriented dynamic identification modeling of a planar SOFC stack based on genetic algorithm-least squares support vector regression

Cited by 5 publications

References 31 publications

A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency

A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency

A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion

Recent approach based heterogeneous comprehensive learning Archimedes optimization algorithm for identifying the optimal parameters of different fuel cells

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