A localized NARX Neural Network model for Short-term load forecasting based upon Self-Organizing Mapping

Hanshen Li, Yuan Zhu, Jinglu Hu, Zhe Li

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

As a routine within the planning and operation of the electrical power system, Short-term load forecasting (STLF) is an essential issue in energy fields. Its prediction accuracy and precision specifically have an effect on the basic safety, stability and economic efficiency of the power system. Moreover, the actual forecasting result also has an impact on operations such as startup and shutdown of the power units, power switching, equipment maintenance, etc. Nonlinear Autoregressive models with Exogenous Input (NARX) Neural Network has been utilized for STLF and proved its effectiveness. This paper proposes a localized Bayesian-Regularization NARX Neural Network model combined with Self-Organizing Mapping (SOM). SOM Neural Network is utilized to extract the meteorological distribution and K-means is utilized to cluster the data. Assessment results depending on half-hourly Australian Grid data and Meteorological data demonstrate that the enhanced model can provide a higher accuracy prediction, which could bring additional economic benefits and extensive social advantages.

本文言語English
ホスト出版物のタイトル2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia, IFEEC - ECCE Asia 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ページ749-754
ページ数6
ISBN(電子版)9781509051571
DOI
出版ステータスPublished - 2017 7 25
イベント3rd IEEE International Future Energy Electronics Conference and ECCE Asia, IFEEC - ECCE Asia 2017 - Kaohsiung, Taiwan, Province of China
継続期間: 2017 6 32017 6 7

出版物シリーズ

名前2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia, IFEEC - ECCE Asia 2017

Other

Other3rd IEEE International Future Energy Electronics Conference and ECCE Asia, IFEEC - ECCE Asia 2017
国/地域Taiwan, Province of China
CityKaohsiung
Period17/6/317/6/7

ASJC Scopus subject areas

  • エネルギー工学および電力技術
  • 電子工学および電気工学
  • 制御と最適化

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