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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)


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 languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings
EditorsYing Tan, Yuhui Shi, Qirong Tang
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319938141
Publication statusPublished - 2018
Event9th International Conference on Swarm Intelligence, ICSI 2018 - Shanghai, China
Duration: 17 Jun 201822 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10941 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on Swarm Intelligence, ICSI 2018


  • GA
  • Hybrid GA-PSO
  • PSO
  • Prediction
  • Wind power

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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