A TS-PSO based artificial neural network for short-term load forecast

Shuihua Wang; Genlin Ji; Jiquan Yang; Xingxing Zhou; Yudong Zhang

doi:10.1007/978-3-319-32557-6_3

A TS-PSO based artificial neural network for short-term load forecast

Shuihua Wang, Genlin Ji, Jiquan Yang, Xingxing Zhou, Yudong Zhang^*

^*Corresponding author for this work

Nanjing Normal University

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

2 Citations (Scopus)

Abstract

(Aim) A short-term load forecast is an arduous problem due to the nonlinear characteristics of the load series. (Method) The artificial neural network (ANN) was employed. To train the ANN, a novel hybridization of Tabu Search and Particle Swarm Optimization (TS-PSO) methods was introduced. TS-PSO is a novel and powerful global optimization method, which combined the merits of both TS and PSO, and removed the disadvantages of both. (Results) Experiments demonstrated that the proposed TS-PSO-ANN is superior to GA-ANN, PSOANN, and BFO-ANN with respect to a mean squared error (MSE). (Conclusion) The TS-PSO-ANN is effective in a short-term load forecast.

Original language	English
Title of host publication	High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers
Editors	Craig C. Douglas, Jiang Xie, Wu Zhang, Zhangxin Chen, Yan Chen, Yan Chen
Publisher	Springer Verlag
Pages	31-37
Number of pages	7
ISBN (Print)	9783319325569
DOIs	https://doi.org/10.1007/978-3-319-32557-6_3
Publication status	Published - 2016
Externally published	Yes
Event	3rd International Conference on High Performance Computing and Applications, HPCA 2015 - Shanghai, China Duration: 26 Jul 2015 → 30 Jul 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9576
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on High Performance Computing and Applications, HPCA 2015
Country/Territory	China
City	Shanghai
Period	26/07/15 → 30/07/15

Keywords

Artificial neural network (ANN)
Mean quared error (MSE)
Particle swarm optimization (PSO)
Short-term load forecast
Tabu search

Access to Document

10.1007/978-3-319-32557-6_3

Cite this

Wang, S., Ji, G., Yang, J., Zhou, X., & Zhang, Y. (2016). A TS-PSO based artificial neural network for short-term load forecast. In C. C. Douglas, J. Xie, W. Zhang, Z. Chen, Y. Chen, & Y. Chen (Eds.), High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers (pp. 31-37). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9576). Springer Verlag. https://doi.org/10.1007/978-3-319-32557-6_3

Wang, Shuihua ; Ji, Genlin ; Yang, Jiquan et al. / A TS-PSO based artificial neural network for short-term load forecast. High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers. editor / Craig C. Douglas ; Jiang Xie ; Wu Zhang ; Zhangxin Chen ; Yan Chen ; Yan Chen. Springer Verlag, 2016. pp. 31-37 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{09ac393ef4ea4d8d8d4695837ead1b00,

title = "A TS-PSO based artificial neural network for short-term load forecast",

abstract = "(Aim) A short-term load forecast is an arduous problem due to the nonlinear characteristics of the load series. (Method) The artificial neural network (ANN) was employed. To train the ANN, a novel hybridization of Tabu Search and Particle Swarm Optimization (TS-PSO) methods was introduced. TS-PSO is a novel and powerful global optimization method, which combined the merits of both TS and PSO, and removed the disadvantages of both. (Results) Experiments demonstrated that the proposed TS-PSO-ANN is superior to GA-ANN, PSOANN, and BFO-ANN with respect to a mean squared error (MSE). (Conclusion) The TS-PSO-ANN is effective in a short-term load forecast.",

keywords = "Artificial neural network (ANN), Mean quared error (MSE), Particle swarm optimization (PSO), Short-term load forecast, Tabu search",

author = "Shuihua Wang and Genlin Ji and Jiquan Yang and Xingxing Zhou and Yudong Zhang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 3rd International Conference on High Performance Computing and Applications, HPCA 2015 ; Conference date: 26-07-2015 Through 30-07-2015",

year = "2016",

doi = "10.1007/978-3-319-32557-6_3",

language = "English",

isbn = "9783319325569",

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

publisher = "Springer Verlag",

pages = "31--37",

editor = "Douglas, {Craig C.} and Jiang Xie and Wu Zhang and Zhangxin Chen and Yan Chen and Yan Chen",

booktitle = "High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers",

}

Wang, S, Ji, G, Yang, J, Zhou, X & Zhang, Y 2016, A TS-PSO based artificial neural network for short-term load forecast. in CC Douglas, J Xie, W Zhang, Z Chen, Y Chen & Y Chen (eds), High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9576, Springer Verlag, pp. 31-37, 3rd International Conference on High Performance Computing and Applications, HPCA 2015, Shanghai, China, 26/07/15. https://doi.org/10.1007/978-3-319-32557-6_3

A TS-PSO based artificial neural network for short-term load forecast. / Wang, Shuihua; Ji, Genlin; Yang, Jiquan et al.
High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers. ed. / Craig C. Douglas; Jiang Xie; Wu Zhang; Zhangxin Chen; Yan Chen; Yan Chen. Springer Verlag, 2016. p. 31-37 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9576).

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

TY - GEN

T1 - A TS-PSO based artificial neural network for short-term load forecast

AU - Wang, Shuihua

AU - Ji, Genlin

AU - Yang, Jiquan

AU - Zhou, Xingxing

AU - Zhang, Yudong

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

PY - 2016

Y1 - 2016

N2 - (Aim) A short-term load forecast is an arduous problem due to the nonlinear characteristics of the load series. (Method) The artificial neural network (ANN) was employed. To train the ANN, a novel hybridization of Tabu Search and Particle Swarm Optimization (TS-PSO) methods was introduced. TS-PSO is a novel and powerful global optimization method, which combined the merits of both TS and PSO, and removed the disadvantages of both. (Results) Experiments demonstrated that the proposed TS-PSO-ANN is superior to GA-ANN, PSOANN, and BFO-ANN with respect to a mean squared error (MSE). (Conclusion) The TS-PSO-ANN is effective in a short-term load forecast.

AB - (Aim) A short-term load forecast is an arduous problem due to the nonlinear characteristics of the load series. (Method) The artificial neural network (ANN) was employed. To train the ANN, a novel hybridization of Tabu Search and Particle Swarm Optimization (TS-PSO) methods was introduced. TS-PSO is a novel and powerful global optimization method, which combined the merits of both TS and PSO, and removed the disadvantages of both. (Results) Experiments demonstrated that the proposed TS-PSO-ANN is superior to GA-ANN, PSOANN, and BFO-ANN with respect to a mean squared error (MSE). (Conclusion) The TS-PSO-ANN is effective in a short-term load forecast.

KW - Artificial neural network (ANN)

KW - Mean quared error (MSE)

KW - Particle swarm optimization (PSO)

KW - Short-term load forecast

KW - Tabu search

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U2 - 10.1007/978-3-319-32557-6_3

DO - 10.1007/978-3-319-32557-6_3

M3 - Conference Proceeding

AN - SCOPUS:84979211172

SN - 9783319325569

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

SP - 31

EP - 37

BT - High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers

A2 - Douglas, Craig C.

A2 - Xie, Jiang

A2 - Zhang, Wu

A2 - Chen, Zhangxin

A2 - Chen, Yan

PB - Springer Verlag

T2 - 3rd International Conference on High Performance Computing and Applications, HPCA 2015

Y2 - 26 July 2015 through 30 July 2015

ER -

Wang S, Ji G, Yang J, Zhou X, Zhang Y. A TS-PSO based artificial neural network for short-term load forecast. In Douglas CC, Xie J, Zhang W, Chen Z, Chen Y, Chen Y, editors, High Performance Computing and Applications - 3rd International Conference, HPCA 2015, Revised Selected Papers. Springer Verlag. 2016. p. 31-37. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-32557-6_3

A TS-PSO based artificial neural network for short-term load forecast

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Keywords

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