Characterisation and modeling of organic solar cells by using radial basis neural networks

Dor Gotleyb; Grazia Lo Sciuto; Christian Napoli; Rafi Shikler; Emiliano Tramontana; Marcin Wózniak

doi:10.1007/978-3-319-39378-0_9

Characterisation and modeling of organic solar cells by using radial basis neural networks

Dor Gotleyb, Grazia Lo Sciuto, Christian Napoli, Rafi Shikler, Emiliano Tramontana, Marcin Wózniak

Department of Electrical & Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

Neural network architectures have been proven useful to model the intrinsic characteristics of photovoltaic cells. The possibility to get rid of an a priori model is one of the many advantages of such an approach as well as the resulting accuracy, robustness and speed. Neural networks have been used to model the characteristics of traditional silicon-based photovoltaic modules, and in this work we have investigated a model for new generation organic solar cells. Silicon-based cells were generally prone to be modeled by simple circuital parameter sets, however for organic cells the process is generally impervious. For this reason, we show that the application of Radial Basis Neural Networks has resulted advantageous to modeling. We have used such networks together with an algorithmic solution to automatically parametrize the Voltage-Current characteristics of organic photovoltaic modules.

Original language	English
Title of host publication	Artificial Intelligence and Soft Computing - 15th International Conference, ICAISC 2016, Proceedings
Editors	Ryszard Tadeusiewicz, Lotfi A. Zadeh, Leszek Rutkowski, Marcin Korytkowski, Rafał Scherer, Jacek M. Zurada
Publisher	Springer Verlag
Pages	91-103
Number of pages	13
ISBN (Print)	9783319393773
DOIs	https://doi.org/10.1007/978-3-319-39378-0_9
State	Published - 1 Jan 2016
Event	15th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2016 - Zakopane, Poland Duration: 12 Jun 2016 → 16 Jun 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9692
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2016
Country/Territory	Poland
City	Zakopane
Period	12/06/16 → 16/06/16

Keywords

Organic solar cells
Parametrical models
Photovoltaic systems
Radial basis neural networks
Renewable energy

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-39378-0_9

Cite this

Gotleyb, D., Sciuto, G. L., Napoli, C., Shikler, R., Tramontana, E., & Wózniak, M. (2016). Characterisation and modeling of organic solar cells by using radial basis neural networks. In R. Tadeusiewicz, L. A. Zadeh, L. Rutkowski, M. Korytkowski, R. Scherer, & J. M. Zurada (Eds.), Artificial Intelligence and Soft Computing - 15th International Conference, ICAISC 2016, Proceedings (pp. 91-103). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9692). Springer Verlag. https://doi.org/10.1007/978-3-319-39378-0_9

Gotleyb, Dor ; Sciuto, Grazia Lo ; Napoli, Christian et al. / Characterisation and modeling of organic solar cells by using radial basis neural networks. Artificial Intelligence and Soft Computing - 15th International Conference, ICAISC 2016, Proceedings. editor / Ryszard Tadeusiewicz ; Lotfi A. Zadeh ; Leszek Rutkowski ; Marcin Korytkowski ; Rafał Scherer ; Jacek M. Zurada. Springer Verlag, 2016. pp. 91-103 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Neural network architectures have been proven useful to model the intrinsic characteristics of photovoltaic cells. The possibility to get rid of an a priori model is one of the many advantages of such an approach as well as the resulting accuracy, robustness and speed. Neural networks have been used to model the characteristics of traditional silicon-based photovoltaic modules, and in this work we have investigated a model for new generation organic solar cells. Silicon-based cells were generally prone to be modeled by simple circuital parameter sets, however for organic cells the process is generally impervious. For this reason, we show that the application of Radial Basis Neural Networks has resulted advantageous to modeling. We have used such networks together with an algorithmic solution to automatically parametrize the Voltage-Current characteristics of organic photovoltaic modules.",

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author = "Dor Gotleyb and Sciuto, {Grazia Lo} and Christian Napoli and Rafi Shikler and Emiliano Tramontana and Marcin W{\'o}zniak",

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Gotleyb, D, Sciuto, GL, Napoli, C, Shikler, R, Tramontana, E & Wózniak, M 2016, Characterisation and modeling of organic solar cells by using radial basis neural networks. in R Tadeusiewicz, LA Zadeh, L Rutkowski, M Korytkowski, R Scherer & JM Zurada (eds), Artificial Intelligence and Soft Computing - 15th International Conference, ICAISC 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9692, Springer Verlag, pp. 91-103, 15th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2016, Zakopane, Poland, 12/06/16. https://doi.org/10.1007/978-3-319-39378-0_9

Characterisation and modeling of organic solar cells by using radial basis neural networks. / Gotleyb, Dor; Sciuto, Grazia Lo; Napoli, Christian et al.
Artificial Intelligence and Soft Computing - 15th International Conference, ICAISC 2016, Proceedings. ed. / Ryszard Tadeusiewicz; Lotfi A. Zadeh; Leszek Rutkowski; Marcin Korytkowski; Rafał Scherer; Jacek M. Zurada. Springer Verlag, 2016. p. 91-103 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9692).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Characterisation and modeling of organic solar cells by using radial basis neural networks

AU - Gotleyb, Dor

AU - Sciuto, Grazia Lo

AU - Napoli, Christian

AU - Shikler, Rafi

AU - Tramontana, Emiliano

AU - Wózniak, Marcin

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Neural network architectures have been proven useful to model the intrinsic characteristics of photovoltaic cells. The possibility to get rid of an a priori model is one of the many advantages of such an approach as well as the resulting accuracy, robustness and speed. Neural networks have been used to model the characteristics of traditional silicon-based photovoltaic modules, and in this work we have investigated a model for new generation organic solar cells. Silicon-based cells were generally prone to be modeled by simple circuital parameter sets, however for organic cells the process is generally impervious. For this reason, we show that the application of Radial Basis Neural Networks has resulted advantageous to modeling. We have used such networks together with an algorithmic solution to automatically parametrize the Voltage-Current characteristics of organic photovoltaic modules.

AB - Neural network architectures have been proven useful to model the intrinsic characteristics of photovoltaic cells. The possibility to get rid of an a priori model is one of the many advantages of such an approach as well as the resulting accuracy, robustness and speed. Neural networks have been used to model the characteristics of traditional silicon-based photovoltaic modules, and in this work we have investigated a model for new generation organic solar cells. Silicon-based cells were generally prone to be modeled by simple circuital parameter sets, however for organic cells the process is generally impervious. For this reason, we show that the application of Radial Basis Neural Networks has resulted advantageous to modeling. We have used such networks together with an algorithmic solution to automatically parametrize the Voltage-Current characteristics of organic photovoltaic modules.

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KW - Parametrical models

KW - Photovoltaic systems

KW - Radial basis neural networks

KW - Renewable energy

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DO - 10.1007/978-3-319-39378-0_9

M3 - Conference contribution

AN - SCOPUS:84976647930

SN - 9783319393773

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 91

EP - 103

BT - Artificial Intelligence and Soft Computing - 15th International Conference, ICAISC 2016, Proceedings

A2 - Tadeusiewicz, Ryszard

A2 - Zadeh, Lotfi A.

A2 - Rutkowski, Leszek

A2 - Korytkowski, Marcin

A2 - Scherer, Rafał

A2 - Zurada, Jacek M.

PB - Springer Verlag

T2 - 15th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2016

Y2 - 12 June 2016 through 16 June 2016

ER -

Gotleyb D, Sciuto GL, Napoli C, Shikler R, Tramontana E, Wózniak M. Characterisation and modeling of organic solar cells by using radial basis neural networks. In Tadeusiewicz R, Zadeh LA, Rutkowski L, Korytkowski M, Scherer R, Zurada JM, editors, Artificial Intelligence and Soft Computing - 15th International Conference, ICAISC 2016, Proceedings. Springer Verlag. 2016. p. 91-103. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-39378-0_9

Characterisation and modeling of organic solar cells by using radial basis neural networks

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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