A hybrid GA-PSO adaptive neuro-fuzzy inference system for short-term wind power prediction

R. Mbuvha; I. Boulkaibet; T. Marwala; F.B. de Lima Neto

doi:10.1007/978-3-319-93815-8_47

A hybrid GA-PSO adaptive neuro-fuzzy inference system for short-term wind power prediction

R. Mbuvha^*, I. Boulkaibet, T. Marwala, F.B. de Lima Neto

^*Corresponding author for this work

Applied Mathematics

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

Abstract

The intermittency of wind remains the greatest challenge to its large scale adoption and sustainability of wind farms. Accurate wind power predictions therefore play a critical role for grid efficiency where wind energy is integrated. In this paper, we investigate two hybrid approaches based on the genetic algorithm (GA) and particle swarm optimisation (PSO). We use these techniques to optimise an Adaptive Neuro-Fuzzy Inference system (ANFIS) in order to perform one-hour ahead wind power prediction. The results show that the proposed techniques display statistically significant out-performance relative to the traditional backpropagation least-squares method. Furthermore, the hybrid techniques also display statistically significant out-performance when compared to the standard genetic algorithm.

Original language	English
Title of host publication	Advances in Swarm Intelligence
Subtitle of host publication	9th International Conference, ICSI 2018, Shanghai, China, June 17-22, 2018, Proceedings, Part I
Editors	Ying Tan, Yuhui Shi, Qirong Tang
Place of Publication	Cham
Publisher	Springer Cham
Pages	498–506
Number of pages	9
ISBN (Electronic)	9783319938158
ISBN (Print)	9783319938141
DOIs	https://doi.org/10.1007/978-3-319-93815-8_47
Publication status	Published - 16 Jun 2018
Event	9th International Conference on Swarm Intelligence - Shanghai, China Duration: 17 Jun 2018 → 22 Jun 2018 Conference number: 214599

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10941
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Swarm Intelligence
Abbreviated title	ICSI 2018
Country/Territory	China
City	Shanghai
Period	17/06/18 → 22/06/18

Keywords

ANFIS
GA
PSO
Hybrid GA-PSO
wind power
prediction

UN SDGs

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

Access to Document

10.1007/978-3-319-93815-8_47

Cite this

Mbuvha, R., Boulkaibet, I., Marwala, T., & de Lima Neto, F. B. (2018). A hybrid GA-PSO adaptive neuro-fuzzy inference system for short-term wind power prediction. In Y. Tan, Y. Shi, & Q. Tang (Eds.), Advances in Swarm Intelligence: 9th International Conference, ICSI 2018, Shanghai, China, June 17-22, 2018, Proceedings, Part I (pp. 498–506). (Lecture Notes in Computer Science; Vol. 10941). Springer Cham. https://doi.org/10.1007/978-3-319-93815-8_47

Mbuvha, R. ; Boulkaibet, I. ; Marwala, T. et al. / A hybrid GA-PSO adaptive neuro-fuzzy inference system for short-term wind power prediction. Advances in Swarm Intelligence: 9th International Conference, ICSI 2018, Shanghai, China, June 17-22, 2018, Proceedings, Part I. editor / Ying Tan ; Yuhui Shi ; Qirong Tang. Cham : Springer Cham, 2018. pp. 498–506 (Lecture Notes in Computer Science).

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title = "A hybrid GA-PSO adaptive neuro-fuzzy inference system for short-term wind power prediction",

abstract = "The intermittency of wind remains the greatest challenge to its large scale adoption and sustainability of wind farms. Accurate wind power predictions therefore play a critical role for grid efficiency where wind energy is integrated. In this paper, we investigate two hybrid approaches based on the genetic algorithm (GA) and particle swarm optimisation (PSO). We use these techniques to optimise an Adaptive Neuro-Fuzzy Inference system (ANFIS) in order to perform one-hour ahead wind power prediction. The results show that the proposed techniques display statistically significant out-performance relative to the traditional backpropagation least-squares method. Furthermore, the hybrid techniques also display statistically significant out-performance when compared to the standard genetic algorithm.",

keywords = "ANFIS, GA, PSO, Hybrid GA-PSO, wind power, prediction",

author = "R. Mbuvha and I. Boulkaibet and T. Marwala and {de Lima Neto}, F.B.",

year = "2018",

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doi = "10.1007/978-3-319-93815-8_47",

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Mbuvha, R, Boulkaibet, I, Marwala, T & de Lima Neto, FB 2018, A hybrid GA-PSO adaptive neuro-fuzzy inference system for short-term wind power prediction. in Y Tan, Y Shi & Q Tang (eds), Advances in Swarm Intelligence: 9th International Conference, ICSI 2018, Shanghai, China, June 17-22, 2018, Proceedings, Part I. Lecture Notes in Computer Science, vol. 10941, Springer Cham, Cham, pp. 498–506, 9th International Conference on Swarm Intelligence, Shanghai, China, 17/06/18. https://doi.org/10.1007/978-3-319-93815-8_47

A hybrid GA-PSO adaptive neuro-fuzzy inference system for short-term wind power prediction. / Mbuvha, R.; Boulkaibet, I.; Marwala, T. et al.
Advances in Swarm Intelligence: 9th International Conference, ICSI 2018, Shanghai, China, June 17-22, 2018, Proceedings, Part I. ed. / Ying Tan; Yuhui Shi; Qirong Tang. Cham: Springer Cham, 2018. p. 498–506 (Lecture Notes in Computer Science; Vol. 10941).

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

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T1 - A hybrid GA-PSO adaptive neuro-fuzzy inference system for short-term wind power prediction

AU - Mbuvha, R.

AU - Boulkaibet, I.

AU - Marwala, T.

AU - de Lima Neto, F.B.

N1 - Conference code: 214599

PY - 2018/6/16

Y1 - 2018/6/16

N2 - The intermittency of wind remains the greatest challenge to its large scale adoption and sustainability of wind farms. Accurate wind power predictions therefore play a critical role for grid efficiency where wind energy is integrated. In this paper, we investigate two hybrid approaches based on the genetic algorithm (GA) and particle swarm optimisation (PSO). We use these techniques to optimise an Adaptive Neuro-Fuzzy Inference system (ANFIS) in order to perform one-hour ahead wind power prediction. The results show that the proposed techniques display statistically significant out-performance relative to the traditional backpropagation least-squares method. Furthermore, the hybrid techniques also display statistically significant out-performance when compared to the standard genetic algorithm.

AB - The intermittency of wind remains the greatest challenge to its large scale adoption and sustainability of wind farms. Accurate wind power predictions therefore play a critical role for grid efficiency where wind energy is integrated. In this paper, we investigate two hybrid approaches based on the genetic algorithm (GA) and particle swarm optimisation (PSO). We use these techniques to optimise an Adaptive Neuro-Fuzzy Inference system (ANFIS) in order to perform one-hour ahead wind power prediction. The results show that the proposed techniques display statistically significant out-performance relative to the traditional backpropagation least-squares method. Furthermore, the hybrid techniques also display statistically significant out-performance when compared to the standard genetic algorithm.

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T3 - Lecture Notes in Computer Science

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EP - 506

BT - Advances in Swarm Intelligence

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A2 - Shi, Yuhui

A2 - Tang, Qirong

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CY - Cham

T2 - 9th International Conference on Swarm Intelligence

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Mbuvha R, Boulkaibet I, Marwala T, de Lima Neto FB. A hybrid GA-PSO adaptive neuro-fuzzy inference system for short-term wind power prediction. In Tan Y, Shi Y, Tang Q, editors, Advances in Swarm Intelligence: 9th International Conference, ICSI 2018, Shanghai, China, June 17-22, 2018, Proceedings, Part I. Cham: Springer Cham. 2018. p. 498–506. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-93815-8_47

A hybrid GA-PSO adaptive neuro-fuzzy inference system for short-term wind power prediction

Abstract

Publication series

Conference

Keywords

UN SDGs

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