Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power

Jeng-Shyang Pan, Pei Hu, Shu-Chuan Chu

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Wind and other renewable energy protects the ecological environment and improves economic efficiency. However, it is difficult to accurately predict wind power because of the randomness and volatility of wind. This paper proposes a new parallel heterogeneous model to predict the wind power. Parallel meta-heuristic saves computation time and improves solution quality. Four communication strategies, which include ranking, combination, dynamic change and hybrid, are introduced to balance exploration and exploitation. The dynamic change strategy is to dynamically increase or decrease the members of subgroup to keep the diversity of the population. The benchmark functions show that the algorithms have excellent performance in exploration and exploitation. In the end, they are applied to successfully realize the prediction for wind power by training the parameters of the neural network.

Original languageEnglish
Article number845
JournalProcesses
Volume7
Issue number11
DOIs
Publication statusPublished - 11 Nov 2019
Externally publishedYes

Bibliographical note

c 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Keywords

  • Communication strategies
  • Dynamic change
  • Heterogeneous
  • Neural network
  • Parallel
  • Prediction
  • Wind power

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