A Simulation Method of Solar Irradiance Data Based on Feature Clustering and Markov Transition Probability Matrix

Xingbo Fu; Feng Gao; Jiang Wu; Xiaohong Guan; Xuan Li; Pengyuan Liu; Pai Li

doi:10.1109/WCICA.2018.8630379

A Simulation Method of Solar Irradiance Data Based on Feature Clustering and Markov Transition Probability Matrix

Xingbo Fu
, Feng Gao
, Jiang Wu
, Xiaohong Guan
, Xuan Li
, Pengyuan Liu
, Pai Li

Faculty of Electronic and Information Engineering

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

3 Scopus citations

Abstract

Solar irradiance is one of the significant influential factors of solar photovoltaic power generation and it is necessary to model and simulate abundant solar irradiance data. In this paper, we propose a simulation approach of solar irradiance data based on feature clustering and Markov transition probability matrix. We introduce the features of solar irradiance data, k-means algorithm and Markov transition probability matrix of solar irradiance conditions, which make up simulation algorithm of solar irradiance. According to this method, a simulation example of National Renewable Energy Laboratory (NREL) one-minute data is presented and the paper gives analysis and evaluation of the results. Finally, there are the conclusion and some possible extensions.

Original language	English
Title of host publication	Proceedings of the 2018 13th World Congress on Intelligent Control and Automation, WCICA 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1741-1746
Number of pages	6
ISBN (Electronic)	9781538673454
DOIs	https://doi.org/10.1109/WCICA.2018.8630379
State	Published - 2 Jul 2018
Event	13th World Congress on Intelligent Control and Automation, WCICA 2018 - Changsa, China Duration: 4 Jul 2018 → 8 Jul 2018

Publication series

Name	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume	2018-July

Conference

Conference	13th World Congress on Intelligent Control and Automation, WCICA 2018
Country/Territory	China
City	Changsa
Period	4/07/18 → 8/07/18

Keywords

Feature clustering
Markov transition probability matrix
Solar irradiance

UN SDGs

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

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10.1109/WCICA.2018.8630379

Cite this

Fu, X., Gao, F., Wu, J., Guan, X., Li, X., Liu, P., & Li, P. (2018). A Simulation Method of Solar Irradiance Data Based on Feature Clustering and Markov Transition Probability Matrix. In Proceedings of the 2018 13th World Congress on Intelligent Control and Automation, WCICA 2018 (pp. 1741-1746). Article 8630379 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCICA.2018.8630379

Fu, Xingbo ; Gao, Feng ; Wu, Jiang et al. / A Simulation Method of Solar Irradiance Data Based on Feature Clustering and Markov Transition Probability Matrix. Proceedings of the 2018 13th World Congress on Intelligent Control and Automation, WCICA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1741-1746 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA)).

@inproceedings{d38bd08992a248129e0f665dfdf0ffe3,

title = "A Simulation Method of Solar Irradiance Data Based on Feature Clustering and Markov Transition Probability Matrix",

abstract = "Solar irradiance is one of the significant influential factors of solar photovoltaic power generation and it is necessary to model and simulate abundant solar irradiance data. In this paper, we propose a simulation approach of solar irradiance data based on feature clustering and Markov transition probability matrix. We introduce the features of solar irradiance data, k-means algorithm and Markov transition probability matrix of solar irradiance conditions, which make up simulation algorithm of solar irradiance. According to this method, a simulation example of National Renewable Energy Laboratory (NREL) one-minute data is presented and the paper gives analysis and evaluation of the results. Finally, there are the conclusion and some possible extensions.",

keywords = "Feature clustering, Markov transition probability matrix, Solar irradiance",

author = "Xingbo Fu and Feng Gao and Jiang Wu and Xiaohong Guan and Xuan Li and Pengyuan Liu and Pai Li",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 13th World Congress on Intelligent Control and Automation, WCICA 2018 ; Conference date: 04-07-2018 Through 08-07-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/WCICA.2018.8630379",

language = "英语",

series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1741--1746",

booktitle = "Proceedings of the 2018 13th World Congress on Intelligent Control and Automation, WCICA 2018",

}

Fu, X, Gao, F, Wu, J , Guan, X, Li, X, Liu, P & Li, P 2018, A Simulation Method of Solar Irradiance Data Based on Feature Clustering and Markov Transition Probability Matrix. in Proceedings of the 2018 13th World Congress on Intelligent Control and Automation, WCICA 2018., 8630379, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 1741-1746, 13th World Congress on Intelligent Control and Automation, WCICA 2018, Changsa, China, 4/07/18. https://doi.org/10.1109/WCICA.2018.8630379

A Simulation Method of Solar Irradiance Data Based on Feature Clustering and Markov Transition Probability Matrix. / Fu, Xingbo; Gao, Feng; Wu, Jiang et al.
Proceedings of the 2018 13th World Congress on Intelligent Control and Automation, WCICA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1741-1746 8630379 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2018-July).

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

TY - GEN

T1 - A Simulation Method of Solar Irradiance Data Based on Feature Clustering and Markov Transition Probability Matrix

AU - Fu, Xingbo

AU - Gao, Feng

AU - Wu, Jiang

AU - Guan, Xiaohong

AU - Li, Xuan

AU - Liu, Pengyuan

AU - Li, Pai

PY - 2018/7/2

Y1 - 2018/7/2

N2 - Solar irradiance is one of the significant influential factors of solar photovoltaic power generation and it is necessary to model and simulate abundant solar irradiance data. In this paper, we propose a simulation approach of solar irradiance data based on feature clustering and Markov transition probability matrix. We introduce the features of solar irradiance data, k-means algorithm and Markov transition probability matrix of solar irradiance conditions, which make up simulation algorithm of solar irradiance. According to this method, a simulation example of National Renewable Energy Laboratory (NREL) one-minute data is presented and the paper gives analysis and evaluation of the results. Finally, there are the conclusion and some possible extensions.

AB - Solar irradiance is one of the significant influential factors of solar photovoltaic power generation and it is necessary to model and simulate abundant solar irradiance data. In this paper, we propose a simulation approach of solar irradiance data based on feature clustering and Markov transition probability matrix. We introduce the features of solar irradiance data, k-means algorithm and Markov transition probability matrix of solar irradiance conditions, which make up simulation algorithm of solar irradiance. According to this method, a simulation example of National Renewable Energy Laboratory (NREL) one-minute data is presented and the paper gives analysis and evaluation of the results. Finally, there are the conclusion and some possible extensions.

KW - Feature clustering

KW - Markov transition probability matrix

KW - Solar irradiance

UR - https://www.scopus.com/pages/publications/85062548097

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DO - 10.1109/WCICA.2018.8630379

M3 - 会议稿件

AN - SCOPUS:85062548097

T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

SP - 1741

EP - 1746

BT - Proceedings of the 2018 13th World Congress on Intelligent Control and Automation, WCICA 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th World Congress on Intelligent Control and Automation, WCICA 2018

Y2 - 4 July 2018 through 8 July 2018

ER -

Fu X, Gao F, Wu J , Guan X, Li X, Liu P et al. A Simulation Method of Solar Irradiance Data Based on Feature Clustering and Markov Transition Probability Matrix. In Proceedings of the 2018 13th World Congress on Intelligent Control and Automation, WCICA 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1741-1746. 8630379. (Proceedings of the World Congress on Intelligent Control and Automation (WCICA)). doi: 10.1109/WCICA.2018.8630379

A Simulation Method of Solar Irradiance Data Based on Feature Clustering and Markov Transition Probability Matrix

Abstract

Publication series

Conference

Keywords

UN SDGs

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