首页    期刊浏览 2025年02月22日 星期六
登录注册

文章基本信息

  • 标题:Two-Stage Artificial Neural Network Model for Short-Term Load Forecasting
  • 本地全文:下载
  • 作者:Yuan-Yu Hsu ; Tao-Ting Tung ; Hung-Chih Yeh
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2018
  • 卷号:51
  • 期号:28
  • 页码:678-683
  • DOI:10.1016/j.ifacol.2018.11.783
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractShort-term load forecast (STLF) is important to ensure stable, reliable and efficient power system operations. In this paper, we propose a two-stage artificial neural network (ANN) model for load forecasting application. The proposed system is currently being tested in the Taiwan Power Company (TPC) with potential for future adoption in their decision support systems. The accuracy of the proposed forecast model is tested using the historical data obtained from TPC; the results show that the proposed two-stage ANN model can outperform a previously proposed single stage ANN load forecast model.
  • 关键词:KeywordsShort-term load forecastpower system operationartificial neural networkload adjustmentday-ahead electricity market
国家哲学社会科学文献中心版权所有