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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number121467
JournalEnergy
Volume237
DOIs
Publication statusPublished - 2021 Dec 15

Keywords

  • Forecasting models
  • Prediction interval
  • Uncertainty
  • Wind power forecasting

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Modelling and Simulation
  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Pollution
  • Energy(all)
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Management, Monitoring, Policy and Law
  • Electrical and Electronic Engineering

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