Forecasting wind speed with recurrent neural networks

Qing Cao, Bradley T. Ewing, Mark A. Thompson

Research output: Contribution to journalArticlepeer-review

189 Scopus citations


This research presents a comparative analysis of the wind speed forecasting accuracy of univariate and multivariate ARIMA models with their recurrent neural network counterparts. The analysis utilizes contemporaneous wind speed time histories taken from the same tower location at five different heights above ground level. A unique aspect of the study is the exploitation of information contained in the wind histories for the various heights when producing forecasts of wind speed for the various heights. The findings indicate that multivariate models perform better than univariate models and that the recurrent neural network models outperform the ARIMA models. The results have important implications for a variety of engineering applications and business related operations.

Original languageEnglish (US)
Pages (from-to)148-154
Number of pages7
JournalEuropean Journal of Operational Research
Issue number1
StatePublished - Aug 16 2012
Externally publishedYes


  • Forecasting
  • Neural networks
  • Time series
  • Wind speed

ASJC Scopus subject areas

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


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