A center-of-concentrated-based prediction interval for wind power forecasting

Hao Han Tsao, Yih Guang Leu*, Li Fen Chou


研究成果: 雜誌貢獻期刊論文同行評審

21 引文 斯高帕斯(Scopus)


Because of the problems of the various sources of uncertainties, wind power point forecasting models may lead to risks for power system operation and planning. The uncertainty sources include input uncertainty, model uncertainty, parameters uncertainty, and so forth. In general, in order to enhance the reliability and credibility of the wind power forecasting model outputs, prediction interval forecasting instead of point forecasting is used and provides a range of future values. However, for wind power prediction interval forecasting models, conventional methods for building prediction interval suffer from the assumption of data distribution, large computational complexity or difficult computation, resulting in generating inappropriate prediction interval. Besides, because of increased uncertainty from longer forecasting steps, constructing effectively the prediction interval of multistep-ahead forecasting is an important issue. Therefore, in this paper, we develop a center-of-concentrated-based neural network method for building the prediction interval of wind power forecasting systems in order to avoid the restrictive condition of data distribution, and the problem of difficult computation. Moreover, simulation results using different neural networks and heuristic optimizations are compared and analyzed to illustrate the effectiveness and feasibility of the proposed method.

出版狀態已發佈 - 2021 12月 15

ASJC Scopus subject areas

  • 土木與結構工程
  • 建築與營造
  • 建模與模擬
  • 可再生能源、永續發展與環境
  • 燃料技術
  • 能源工程與電力技術
  • 污染
  • 一般能源
  • 機械工業
  • 工業與製造工程
  • 管理、監督、政策法律
  • 電氣與電子工程


深入研究「A center-of-concentrated-based prediction interval for wind power forecasting」主題。共同形成了獨特的指紋。