Small-world neural network and its performance for wind power forecasting

Shuangxin Wang, Xin Zhao, Hong Wang, Meng Li

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

A small-world neural network has stronger generalization ability with high transfer efficiency than that of the regular neural networks. This paper presents two novel small-world neural networks, the Watts-Strogatz small-world based on a BP neural network (WSBP) and a Newman-Watts small-world neural network based on a BP neural network (NWBP), related to previous research of complex networks. The algorithms are developed separately by adopting WS and NW small-world networks as their topological structures, and their derivation and convergence criterion are progressively discussed. After that, the proposed models are subsequently tested by two typical nonlinear functions which confirm their significant improvement over the regular BP networks and other algorithms. Finally, a wind power prediction system is advanced to verify their generalization abilities, and show that the models are practically feasible and effective with improved accuracy and acceptable forecasting errors caused by wind fluctuation and randomness with a time scale up to 24 h.

Original languageEnglish
Pages (from-to)362-373
Number of pages12
JournalCSEE Journal of Power and Energy Systems
Volume6
Issue number2
DOIs
StatePublished - Jun 2020

Funding

Manuscript received January 7, 2019; revised June 10, 2019; accepted July 4, 2019. Date of online publication August 1, 2019; date of current version March 5, 2020. This work was supported by National Natural Science Foundation of China (Grant No. 50776005 and 51577008). ACKNOWLEDGEMENT This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This work was supported by National Natural Science Foundation of China (Grant No. 50776005 and 51577008). This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

FundersFunder number
DOE Public Access Plan
United States Government
U.S. Department of Energy
UT-BattelleDE-AC05-00OR22725
National Natural Science Foundation of China50776005, 51577008

    Keywords

    • Convergence
    • Function approximation
    • Small-world neural network
    • Topology

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