Gpu-based parameter estimation method for photovoltaic electrical models

Jieming Ma; T. O. Ting; Huiqing Wen; Baochuan Fu; Jianmin Ban

doi:10.1007/978-3-319-23862-3_29

Gpu-based parameter estimation method for photovoltaic electrical models

Jieming Ma^*, T. O. Ting, Huiqing Wen, Baochuan Fu, Jianmin Ban

^*Corresponding author for this work

Suzhou University of Science and Technology

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

Abstract

Parameter estimation (PE) is one of the most challenging problems in photovoltaic (PV) system modeling. Owing to the ability to handle nonlinear functions regardless of the derivatives information, meta-heuristics have attracted many researchers. Recently, many implementations of particle swarm optimization (PSO) based PE method have been proposed in the literature. However, these algorithms utilize multiple agents or particles in the search process, and are normally compute intensive. In this paper, we describe our implementation of PSO on graphic processing units (GPUs) using open computing language (OpenCL). The proposed method has been specifically designed and entirely executed on the GPUs to provide a reduction of computational costs. Results show that the GPU-based PE is faster in comparison with its sequential implementation of PSO, and this proves the efficacy of the GPU framework.

Original language	English
Title of host publication	Intelligence Science and Big Data Engineering
Subtitle of host publication	Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers
Editors	Zhi-Hua Zhou, Baochuan Fu, Fuyuan Hu, Zhancheng Zhang, Zhi-Yong Liu, Yanning Zhang, Xiaofei He, Xinbo Gao
Publisher	Springer Verlag
Pages	298-307
Number of pages	10
ISBN (Print)	9783319238616
DOIs	https://doi.org/10.1007/978-3-319-23862-3_29
Publication status	Published - 2015
Event	5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015 - Suzhou, China Duration: 14 Jun 2015 → 16 Jun 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9243
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015
Country/Territory	China
City	Suzhou
Period	14/06/15 → 16/06/15

Keywords

Modeling
Parameter estimation
Particle swarm optimization
Photovolatic

UN SDGs

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

Access to Document

10.1007/978-3-319-23862-3_29

Cite this

Ma, J., Ting, T. O., Wen, H., Fu, B., & Ban, J. (2015). Gpu-based parameter estimation method for photovoltaic electrical models. In Z.-H. Zhou, B. Fu, F. Hu, Z. Zhang, Z.-Y. Liu, Y. Zhang, X. He, & X. Gao (Eds.), Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers (pp. 298-307). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9243). Springer Verlag. https://doi.org/10.1007/978-3-319-23862-3_29

Ma, Jieming ; Ting, T. O. ; Wen, Huiqing et al. / Gpu-based parameter estimation method for photovoltaic electrical models. Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers. editor / Zhi-Hua Zhou ; Baochuan Fu ; Fuyuan Hu ; Zhancheng Zhang ; Zhi-Yong Liu ; Yanning Zhang ; Xiaofei He ; Xinbo Gao. Springer Verlag, 2015. pp. 298-307 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Gpu-based parameter estimation method for photovoltaic electrical models",

abstract = "Parameter estimation (PE) is one of the most challenging problems in photovoltaic (PV) system modeling. Owing to the ability to handle nonlinear functions regardless of the derivatives information, meta-heuristics have attracted many researchers. Recently, many implementations of particle swarm optimization (PSO) based PE method have been proposed in the literature. However, these algorithms utilize multiple agents or particles in the search process, and are normally compute intensive. In this paper, we describe our implementation of PSO on graphic processing units (GPUs) using open computing language (OpenCL). The proposed method has been specifically designed and entirely executed on the GPUs to provide a reduction of computational costs. Results show that the GPU-based PE is faster in comparison with its sequential implementation of PSO, and this proves the efficacy of the GPU framework.",

keywords = "Modeling, Parameter estimation, Particle swarm optimization, Photovolatic",

author = "Jieming Ma and Ting, {T. O.} and Huiqing Wen and Baochuan Fu and Jianmin Ban",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015 ; Conference date: 14-06-2015 Through 16-06-2015",

year = "2015",

doi = "10.1007/978-3-319-23862-3_29",

language = "English",

isbn = "9783319238616",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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}

Ma, J, Ting, TO, Wen, H, Fu, B & Ban, J 2015, Gpu-based parameter estimation method for photovoltaic electrical models. in Z-H Zhou, B Fu, F Hu, Z Zhang, Z-Y Liu, Y Zhang, X He & X Gao (eds), Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9243, Springer Verlag, pp. 298-307, 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015, Suzhou, China, 14/06/15. https://doi.org/10.1007/978-3-319-23862-3_29

Gpu-based parameter estimation method for photovoltaic electrical models. / Ma, Jieming; Ting, T. O.; Wen, Huiqing et al.
Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers. ed. / Zhi-Hua Zhou; Baochuan Fu; Fuyuan Hu; Zhancheng Zhang; Zhi-Yong Liu; Yanning Zhang; Xiaofei He; Xinbo Gao. Springer Verlag, 2015. p. 298-307 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9243).

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

TY - GEN

T1 - Gpu-based parameter estimation method for photovoltaic electrical models

AU - Ma, Jieming

AU - Ting, T. O.

AU - Wen, Huiqing

AU - Fu, Baochuan

AU - Ban, Jianmin

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Parameter estimation (PE) is one of the most challenging problems in photovoltaic (PV) system modeling. Owing to the ability to handle nonlinear functions regardless of the derivatives information, meta-heuristics have attracted many researchers. Recently, many implementations of particle swarm optimization (PSO) based PE method have been proposed in the literature. However, these algorithms utilize multiple agents or particles in the search process, and are normally compute intensive. In this paper, we describe our implementation of PSO on graphic processing units (GPUs) using open computing language (OpenCL). The proposed method has been specifically designed and entirely executed on the GPUs to provide a reduction of computational costs. Results show that the GPU-based PE is faster in comparison with its sequential implementation of PSO, and this proves the efficacy of the GPU framework.

AB - Parameter estimation (PE) is one of the most challenging problems in photovoltaic (PV) system modeling. Owing to the ability to handle nonlinear functions regardless of the derivatives information, meta-heuristics have attracted many researchers. Recently, many implementations of particle swarm optimization (PSO) based PE method have been proposed in the literature. However, these algorithms utilize multiple agents or particles in the search process, and are normally compute intensive. In this paper, we describe our implementation of PSO on graphic processing units (GPUs) using open computing language (OpenCL). The proposed method has been specifically designed and entirely executed on the GPUs to provide a reduction of computational costs. Results show that the GPU-based PE is faster in comparison with its sequential implementation of PSO, and this proves the efficacy of the GPU framework.

KW - Modeling

KW - Parameter estimation

KW - Particle swarm optimization

KW - Photovolatic

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U2 - 10.1007/978-3-319-23862-3_29

DO - 10.1007/978-3-319-23862-3_29

M3 - Conference Proceeding

AN - SCOPUS:84945933528

SN - 9783319238616

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

SP - 298

EP - 307

BT - Intelligence Science and Big Data Engineering

A2 - Zhou, Zhi-Hua

A2 - Fu, Baochuan

A2 - Hu, Fuyuan

A2 - Zhang, Zhancheng

A2 - Liu, Zhi-Yong

A2 - Zhang, Yanning

A2 - He, Xiaofei

A2 - Gao, Xinbo

PB - Springer Verlag

T2 - 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015

Y2 - 14 June 2015 through 16 June 2015

ER -

Ma J, Ting TO, Wen H, Fu B, Ban J. Gpu-based parameter estimation method for photovoltaic electrical models. In Zhou ZH, Fu B, Hu F, Zhang Z, Liu ZY, Zhang Y, He X, Gao X, editors, Intelligence Science and Big Data Engineering: Big Data and Machine Learning Techniques - 5th International Conference, IScIDE 2015, Revised Selected Papers. Springer Verlag. 2015. p. 298-307. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-23862-3_29

Gpu-based parameter estimation method for photovoltaic electrical models

Abstract

Publication series

Conference

Keywords

UN SDGs

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