# Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System

^{1}

^{2}

^{3}

^{4}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Case Study

#### 2.1. Fuel Cell

#### 2.2. Power System

## 3. Model Approach

#### 3.1. Data Processing

#### 3.2. K-Means Algorithm

- A random set of data samples are chosen as the first set of centroids due to, at the beginning, the center of each group is not known;
- A set of data samples will create a cluster if this set of points are the nearest to this cluster centroid;
- Once the clusters are defined, it is necessary to calculate their associated centroid. These new centroids are chosen as the center of each cluster.

#### 3.3. Artificial Neural Networks

- Number of layers;
- Number of neurons per layer;
- Links between neurons;
- Activation functions.

#### 3.4. Polynomial Regression

#### 3.5. Support Vector Machines for Regression

- ${I}_{n}$ is a vector of n ones;
- T means transpose of a matrix or vector;
- $\gamma $ a weight vector;
- b regression vector;
- ${b}_{0}$ is the model offset.

## 4. Results

#### 4.1. Clustering Results

#### 4.2. Modeling Results

#### 4.2.1. Artifical Neural Networks

#### 4.2.2. Polynomial Regression

#### 4.2.3. Support Vector Machines for Regression

#### 4.2.4. Best Regression Local Models Selection

#### 4.3. Validation Results

- Mean Squared Error −$MSE=23.8121$;
- Normalized Mean Squared Error −$NMSE=0.4438$;
- Mean Absolute Error −$MAE=3.7318$;
- Mean Absolute Percentage Error −$MAPE=110.4193$.

## 5. Conclusions and Future Works

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Kuwae, T.; Hori, M. Global Environmental Issues. In Blue Carbon in Shallow Coastal Ecosystems: Carbon Dynamics, Policy, and Implementation; Springer: Berlin, Germany, 2019. [Google Scholar]
- Karunathilake, H.; Hewage, K.; Mérida, W.; Sadiq, R. Renewable energy selection for net-zero energy communities: Life cycle based decision making under uncertainty. Renew. Energy
**2019**, 130, 558–573. [Google Scholar] [CrossRef] - Burduk, A.; Bozejko, W.; Pempera, J.; Musial, K. On the simulated annealing adaptation for tasks transportation optimization. Logic J. IGPL
**2018**, 26, 581–592. [Google Scholar] [CrossRef] - Wei, M.; Patadia, S.; Kammen, D.M. Putting renewables and energy efficiency to work: How many jobs can the clean energy industry generate in the US? Energy Policy
**2010**, 38, 919–931. [Google Scholar] [CrossRef] - Giacone, E.; Mancò, S. Energy efficiency measurement in industrial processes. Energy
**2012**, 38, 331–345. [Google Scholar] [CrossRef] - Dunn, B.; Kamath, H.; Tarascon, J.M. Electrical energy storage for the grid: A battery of choices. Science
**2011**, 334, 928–935. [Google Scholar] [CrossRef] - Montero-Sousa, J.A.; Casteleiro-Roca, J.L.; Calvo-Rolle, J.L. Evolution of the electricity sector after the 2nd world war. DYNA
**2017**, 92, 280–284. [Google Scholar] - Montero-Sousa, J.A.; Casteleiro-Roca, J.L.; Calvo-Rolle, J.L. The electricity sector since its inception until the second world war. DYNA
**2017**, 92, 43–47. [Google Scholar] - de Souza Dutra, M.D.; Anjos, M.F.; Digabel, S.L. A general framework for customized transition to smart homes. Energy
**2019**, 189, 116138. [Google Scholar] [CrossRef] - Nizami, M.; Haque, A.; Nguyen, P.; Hossain, M. On the application of Home Energy Management Systems for power grid support. Energy
**2019**, 188, 116104. [Google Scholar] [CrossRef] - Jove, E.; Casteleiro-Roca, J.L.; Quintián, H.; Méndez-Pérez, J.A.; Calvo-Rolle, J.L. Anomaly detection based on intelligent techniques over a bicomponent production plant used on wind generator blades manufacturing. Rev. Iberoam. Autom. Inform. Ind.
**2019**. [Google Scholar] [CrossRef] - Fernandez-Serantes, L.A.; Montero-Sousa, J.A.; Casteleiro-Roca, J.L.; Vilar-Martinez, X.M.; Calvo-Rolle, J.L. Gestión de almacenamiento energético para instalaciones de generación-distribución. DYNA Ing. Ind.
**2017**, 92, 140–141. [Google Scholar] [CrossRef] - Good, N.; Ceseña, E.A.M.; Heltorp, C.; Mancarella, P. A transactive energy modelling and assessment framework for demand response business cases in smart distributed multi-energy systems. Energy
**2019**, 184, 165–179. [Google Scholar] [CrossRef][Green Version] - Yang, C.J.; Jackson, R.B. Opportunities and barriers to pumped-hydro energy storage in the United States. Renew. Sustain. Energy Rev.
**2011**, 15, 839–844. [Google Scholar] [CrossRef] - Hache, E.; Palle, A. Renewable energy source integration into power networks, research trends and policy implications: A bibliometric and research actors survey analysis. Energy Policy
**2019**, 124, 23–35. [Google Scholar] [CrossRef] - Westbrook, M.H. The Electric Car: Development and Future of Battery, Hybrid and Fuel-Cell Cars; IET Digital Library: London, UK, 2001. [Google Scholar]
- Hall, P.J.; Bain, E.J. Energy-storage technologies and electricity generation. Energy Policy
**2008**, 36, 4352–4355. [Google Scholar] [CrossRef][Green Version] - Ghanaatian, M.; Lotfifard, S. Control of Flywheel Energy Storage Systems in the Presence of Uncertainties. IEEE Trans. Sustain. Energy
**2019**, 10, 36–45. [Google Scholar] [CrossRef] - Slocum, A.H.; Fennell, G.E.; Dundar, G.; Hodder, B.G.; Meredith, J.D.C.; Sager, M.A. Ocean Renewable Energy Storage (ORES) System: Analysis of an Undersea Energy Storage Concept. Proc. IEEE
**2013**, 101, 906–924. [Google Scholar] [CrossRef] - Bruninx, K.; Dvorkin, Y.; Delarue, E.; Pandžić, H.; D’haeseleer, W.; Kirschen, D.S. Coupling Pumped Hydro Energy Storage With Unit Commitment. IEEE Trans. Sustain. Energy
**2016**, 7, 786–796. [Google Scholar] [CrossRef] - Molina-Cabello, M.A.; López-Rubio, E.; M Luque-Baena, R.; Domínguez, E.; Palomo, E.J. Foreground object detection for video surveillance by fuzzy logic based estimation of pixel illumination states. Logic J. IGPL
**2018**, 26, 593–604. [Google Scholar] [CrossRef] - Potter, C.W.; Archambault, A.; Westrick, K. Building a smarter smart grid through better renewable energy information. In Proceedings of the 2009 IEEE/PES Power Systems Conference and Exposition, Seattle, WA, USA, 15–18 March 2009; pp. 1–5. [Google Scholar]
- Montero-Sousa, J.A.; Fernandez-Serantes, L.A.; Casteleiro-Roca, J.L.; Vilar-Martnez, X.M.; Calvo-Rolle, J.L. Energy storage management for generation-distribution facilities. DYNA
**2017**, 92, 140–141. [Google Scholar] - Segovia, F.; Górriz, J.M.; Ramírez, J.; Martinez-Murcia, F.J.; García-Pérez, M. Using deep neural networks along with dimensionality reduction techniques to assist the diagnosis of neurodegenerative disorders. Logic J. IGPL
**2018**, 26, 618–628. [Google Scholar] [CrossRef] [PubMed] - Chalki, A.; Koutras, C.D.; Zikos, Y. A quick guided tour to the modal logic S4.2. Logic J. IGPL
**2018**, 26, 429–451. [Google Scholar] [CrossRef] - Jove, E.; López, J.A.V.; Fernández-Ibáñez, I.; Casteleiro-Roca, J.L.; Calvo-Rolle, J.L. Hybrid intelligent system topredict the individual academic performance of engineering students. Int. J. Eng. Educ.
**2018**, 34, 895–904. [Google Scholar] - Rincon, J.A.; Julian, V.; Carrascosa, C.; Costa, A.; Novais, P. Detecting emotions through non-invasive wearables. Logic J. IGPL
**2018**, 26, 605–617. [Google Scholar] [CrossRef] - Casteleiro-Roca, J.L.; Calvo-Rolle, J.L.; Méndez Pérez, J.A.; Roqueñí Gutiérrez, N.; de Cos Juez, F.J. Hybrid intelligent system to perform fault detection on BIS sensor during surgeries. Sensors
**2017**, 17, 179. [Google Scholar] [CrossRef] [PubMed] - Mehta, V.; Cooper, J. Review and analysis of PEM fuel cell design and manufacturing. J. Power Sources
**2003**, 114, 32–53. [Google Scholar] [CrossRef] - Segura, F.; Bartolucci, V.; Andújar, J. Hardware/software data acquisition system for real time cell temperature monitoring in air-cooled polymer electrolyte fuel cells. Sensors
**2017**, 17. [Google Scholar] [CrossRef] - Casteleiro-Roca, J.L.; Barragán, A.J.; Segura, F.; Calvo-Rolle, J.L.; Andújar, J.M. Fuel cell output current prediction with a hybrid intelligent system. Complexity
**2019**, 2019. [Google Scholar] [CrossRef] - Casteleiro-Roca, J.L.; Barragán, A.J.; Segura, F.; Calvo-Rolle, J.L.; Andújar, J.M. Intelligent hybrid system for the prediction of the voltage-current characteristic curve of a hydrogen-based fuel cell. Rev. Iberoam. Autom. Inform. Ind.
**2019**, 16, 492–501. [Google Scholar] [CrossRef] - Ballard. FCgen1020-ACS Fuel Cell from Ballard Power Systems. Available online: https://www.ballard.com/docs/default-source/backup-power-documents/fcgen-1020acs.pdf (accessed on 28 October 2018).
- Scikit-learn. Min Max Scaler; INRIA: Rocquencourt, France, 2018. [Google Scholar]
- Orallo, J.; Quintana, M.; Ramírez, C. Introducción a la Minería de Datos; Editorial Alhambra S.A.: Madrid, Spain, 2004. [Google Scholar]
- Viñuela, P.; León, I. Redes de Neuronas Artificiales: Un Enfoque Práctico; Pearson Educación–Prentice Hall: Upper Saddle River, NJ, USA, 2004. [Google Scholar]
- Harston, A.M.C.; Pap, R. Handbook of Neural Computing Applications; Elsevier Science: Amsterdam, The Netherlands, 2014. [Google Scholar]
- del Brío, B.; Molina, A. Redes Neuronales y Sistemas Borrosos; Ra-Ma: Madrid, Spain, 2006. [Google Scholar]
- Wang, L.; Wu, J. Neural network ensemble model using PPR and LS-SVR for stock et eorecasting. In International Conference on Intelligent Computing; Springer: Berlin/Heidelberg, Germany, 2012; pp. 1–8. [Google Scholar] [CrossRef]
- Steinwart, I.; Christmann, A. Support Vector Machines; Springer Publishing Company: New York, NY, USA, 2008. [Google Scholar]

Cl-1 | Cl-2 | Cl-3 | Cl-4 | |
---|---|---|---|---|

Global | 187 | |||

Hybrid 2 | 92 | 95 | ||

Hybrid 3 | 52 | 67 | 68 | |

Hybrid 4 | 42 | 45 | 47 | 53 |

**Table 2.**Mean Absolute Error (MAE) using Artificial Neural Network (ANN) with 13 neurons in the hidden layer.

Cl-1 | Cl-2 | Cl-3 | Cl-4 | |
---|---|---|---|---|

Global | 7.7725 | |||

Hybrid 2 | 43.6784 | 77.1169 | ||

Hybrid 3 | 17.8174 | 26.0211 | 41.1911 | |

Hybrid 4 | 14.8003 | 25.2539 | 10.7302 | 23.8377 |

Cl-1 | Cl-2 | Cl-3 | Cl-4 | |
---|---|---|---|---|

Global | 4.0405 | |||

Hybrid 2 | 6.5793 | 13.0030 | ||

Hybrid 3 | 29.8819 | 74.4182 | 14.6885 | |

Hybrid 4 | 52.8102 | 50.7558 | 53.4093 | 124.6589 |

Cl-1 | Cl-2 | Cl-3 | Cl-4 | |
---|---|---|---|---|

Global | 4.0405 | |||

Hybrid 2 | 6.5793 | 13.0030 | ||

Hybrid 3 | 29.8819 | 74.4182 | 14.6885 | |

Hybrid 4 | 52.8102 | 50.7558 | 53.4093 | 124.6589 |

Cl-1 | Cl-2 | Cl-3 | Cl-4 | |
---|---|---|---|---|

Global | LS-SVR | |||

Hybrid 2 | LS-SVR | LS-SVR | ||

Hybrid 3 | LS-SVR | LS-SVR | LS-SVR | |

Hybrid 4 | LS-SVR | LS-SVR | LS-SVR | LS-SVR |

Cl-1 | Cl-2 | Cl-3 | Cl-4 | |
---|---|---|---|---|

Global | 18.3348 | |||

Hybrid 2 | 11.0268 | 28.0035 | ||

Hybrid 3 | 20.3464 | 37.9476 | 10.7460 | |

Hybrid 4 | 65.2756 | 15.5356 | 10.2792 | 94.5818 |

Global | Hybrid Model (Local Models) | |||
---|---|---|---|---|

2 | 3 | 4 | ||

MSE | 24.0758 | 33.8591 | 23.8121 | 398.9072 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Alaiz-Moretón, H.; Jove, E.; Casteleiro-Roca, J.-L.; Quintián, H.; López García, H.; Benítez-Andrades, J.A.; Novais, P.; Calvo-Rolle, J.L.
Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System. *Processes* **2019**, *7*, 825.
https://doi.org/10.3390/pr7110825

**AMA Style**

Alaiz-Moretón H, Jove E, Casteleiro-Roca J-L, Quintián H, López García H, Benítez-Andrades JA, Novais P, Calvo-Rolle JL.
Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System. *Processes*. 2019; 7(11):825.
https://doi.org/10.3390/pr7110825

**Chicago/Turabian Style**

Alaiz-Moretón, Héctor, Esteban Jove, José-Luis Casteleiro-Roca, Héctor Quintián, Hilario López García, José Alberto Benítez-Andrades, Paulo Novais, and Jose Luis Calvo-Rolle.
2019. "Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System" *Processes* 7, no. 11: 825.
https://doi.org/10.3390/pr7110825