A study on the hydrogen consumption calculation of proton exchange membrane fuel cells for linearly increasing loads: Artificial Neural Networks vs Multiple Linear Regression

Özçelep, Yasin; Sevgen, Selçuk; Şamlı, Rüya

doi:10.1016/j.renene.2020.04.085

Cited by 23 publications

(4 citation statements)

References 42 publications

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“…Analisis data menggunakan neural network pada umumnya akan menghasilkan tingkat akurasi yang tinggi dibandingkan dengan penggunaan metode statistika klasik. Penelitian sebelumnya menunjukkan bahwa metode neural network memiliki performa yang lebih baik dibandingkan dengan metode regresi linier berganda, yakni dalam prediksi konsentrasi SO2 [3], prediksi kadar hidrogen pada proses pertukaran di membran sel [4], peramalan penggunaan listrik [5] dan pada beberapa kasus lainnya. Untuk permasalahan klasifikasi, metode neural network juga memiliki performa yang lebih baik apabila dibandingkan dengan metode analisis diskriminan [6], [7] maupun regresi logistik [8]- [10].…”

Section: Pendahuluan 11 Latar Belakangunclassified

Comparison of Feedforward Neural Network and Classical Statistics Methods: Application in Finance

Saputri

Oktaviana²

2023

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The flexibility and elevated accurateness of the statistical machine learning method makes this method widely applied in various fields. One of the statistical machine learning methods is the neural network, which can be used for data analysis. The great performance of the neural network method can be used in the field of finance. In this study, the neural network method was used to predict Non-Performing Loans (NPL) data and forecast credit receivables. In the NPL prediction, the banks used are State-Owned Banks, Regional Government Banks, and National Private Banks with a main capital of more than 6 trillion rupiahs in March 2021, i.e. 26 banks with the period of March 2018 until March 2021. In predicting NPL, a moving window scheme involves several different periods. In the forecast of the number of credit receivables, the data used is the number of financing receivables. The period from November 2012 to December 2020 is used as training data, while data for the period from January to June 2021 is used as testing data. The results of the analysis show that the neural network for NPL prediction and credit receivables forecasting shows better performance compared to classical methods such as multiple linear regression and ARIMA. A comparison of methods for banking NPLs prediction is based on the RMSE data testing values, while forecasting credit receivable is based on RMSE, MAE, and MAPE data testing values.

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Section: Pendahuluan 11 Latar Belakangunclassified

Comparison of Feedforward Neural Network and Classical Statistics Methods: Application in Finance

Saputri

Oktaviana²

2023

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“…The computational tool that represents nonlinear systems has been said to be ANN [31][32][33]. ANN consists of biological neurons, which contain weight and bias to interconnect each other by transferring signals.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Efficient Control of DC Microgrid with Hybrid PV—Fuel Cell and Energy Storage Systems

et al. 2021

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Direct current microgrids are attaining attractiveness due to their simpler configuration and high-energy efficiency. Power transmission losses are also reduced since distributed energy resources (DERs) are located near the load. DERs such as solar panels and fuel cells produce the DC supply; hence, the system is more stable and reliable. DC microgrid has a higher power efficiency than AC microgrid. Energy storage systems that are easier to integrate may provide additional benefits. In this paper, the DC micro-grid consists of solar photovoltaic and fuel cell for power generation, proposes a hybrid energy storage system that includes a supercapacitor and lithium–ion battery for the better improvement of power capability in the energy storage system. The main objective of this research work has been done for the enhanced settling point and voltage stability with the help of different maximum power point tracking (MPPT) methods. Different control techniques such as fuzzy logic controller, neural network, and particle swarm optimization are used to evaluate PV and FC through DC–DC boost converters for this enhanced settling point. When the test results are perceived, it is evidently attained that the fuzzy MPPT method provides an increase in the tracking capability of maximum power point and at the same time reduces steady-state oscillations. In addition, the time to capture the maximum power point is 0.035 s. It is about nearly two times faster than neural network controllers and eighteen times faster than for PSO, and it has also been discovered that the preferred approach is faster compared to other control methods.

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“…It is usually optimized through a learning method based on mathematical statistics. There are many kinds of NN [34]. The backpropagation neural network (BPNN) was employed in this study.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

Geometry optimization and pressure analysis of a proton exchange membrane fuel cell stack

Chen

Tsai

Chang

et al. 2021

International Journal of Hydrogen Energy

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A study on the hydrogen consumption calculation of proton exchange membrane fuel cells for linearly increasing loads: Artificial Neural Networks vs Multiple Linear Regression

Cited by 23 publications

References 42 publications

Comparison of Feedforward Neural Network and Classical Statistics Methods: Application in Finance

Comparison of Feedforward Neural Network and Classical Statistics Methods: Application in Finance

Efficient Control of DC Microgrid with Hybrid PV—Fuel Cell and Energy Storage Systems

Geometry optimization and pressure analysis of a proton exchange membrane fuel cell stack

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