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

Rendani Mbuvha; Ilyes Boulkaibet; Tshilidzi Marwala; Fernando Buarque 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

Rendani Mbuvha, Ilyes Boulkaibet, Tshilidzi Marwala, Fernando Buarque de Lima Neto

Faculty of Engineering and the Built Environment

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

8 Citations (Scopus)

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 - 9th International Conference, ICSI 2018, Proceedings
Editors	Ying Tan, Yuhui Shi, Qirong Tang
Publisher	Springer Verlag
Pages	498-506
Number of pages	9
ISBN (Print)	9783319938141
DOIs	https://doi.org/10.1007/978-3-319-93815-8_47
Publication status	Published - 2018
Event	9th International Conference on Swarm Intelligence, ICSI 2018 - Shanghai, China Duration: 17 Jun 2018 → 22 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10941 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

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

Keywords

ANFIS
GA
Hybrid GA-PSO
PSO
Prediction
Wind power

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

10.1007/978-3-319-93815-8_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, Proceedings (pp. 498-506). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10941 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93815-8_47

Mbuvha, Rendani ; Boulkaibet, Ilyes ; Marwala, Tshilidzi 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, Proceedings. editor / Ying Tan ; Yuhui Shi ; Qirong Tang. Springer Verlag, 2018. pp. 498-506 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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, Hybrid GA-PSO, PSO, Prediction, Wind power",

author = "Rendani Mbuvha and Ilyes Boulkaibet and Tshilidzi Marwala and {de Lima Neto}, {Fernando Buarque}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 9th International Conference on Swarm Intelligence, ICSI 2018 ; Conference date: 17-06-2018 Through 22-06-2018",

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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, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10941 LNCS, Springer Verlag, pp. 498-506, 9th International Conference on Swarm Intelligence, ICSI 2018, 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, Rendani; Boulkaibet, Ilyes; Marwala, Tshilidzi et al.
Advances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings. ed. / Ying Tan; Yuhui Shi; Qirong Tang. Springer Verlag, 2018. p. 498-506 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10941 LNCS).

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

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AU - Mbuvha, Rendani

AU - Boulkaibet, Ilyes

AU - Marwala, Tshilidzi

AU - de Lima Neto, Fernando Buarque

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

Y1 - 2018

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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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, Proceedings. Springer Verlag. 2018. p. 498-506. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). 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

ASJC Scopus subject areas

UN SDGs

Access to Document

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