New Hybrid Algorithms for Prediction of Daily Load of Power Network

Pei Hu, Jeng-Shyang Pan, Shu-Chuan Chu, Qing-Wei Chai, Tao Liu, Zhong-Cui Li

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Two new hybrid algorithms are proposed to improve the performances of the meta-heuristic optimization algorithms, namely the Grey Wolf Optimizer (GWO) and Shuffled Frog Leaping Algorithm (SFLA). Firstly, it advances the hierarchy and position updating of the mathematical model of GWO, and then the SGWO algorithm is proposed based on the advantages of SFLA and GWO. It not only improves the ability of local search, but also speeds up the global convergence. Secondly, the SGWOD algorithm based on SGWO is proposed by using the benefit of differential evolution strategy. Through the experiments of the 29 benchmark functions, which are composed of the functions of unimodal, multimodal, fixed-dimension and composite multimodal, the performances of the new algorithms are better than that of GWO, SFLA and GWO-DE, and they greatly balances the exploration and exploitation. The proposed SGWO and SGWOD algorithms are also applied to the prediction model based on the neural network. Experimental results show the usefulness for forecasting the power daily load.

Original languageEnglish
Article number4514
JournalApplied Sciences (Switzerland)
Volume9
Issue number21
DOIs
Publication statusPublished - 24 Oct 2019
Externally publishedYes

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited license (http://creativecommons.org/licenses/by/4.0/). c 2019 by the authors.

Keywords

  • hybrid algorithms
  • GWO
  • SFLA
  • Network
  • Hybrid algorithms
  • Neural network

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  • Cite this

    Hu, P., Pan, J-S., Chu, S-C., Chai, Q-W., Liu, T., & Li, Z-C. (2019). New Hybrid Algorithms for Prediction of Daily Load of Power Network. Applied Sciences (Switzerland), 9(21), [4514]. https://doi.org/10.3390/app9214514