Forecasting wind speed with recurrent neural networks

Qing Cao, Bradley Ewing and Mark A. Thompson

European Journal of Operational Research, 2012, vol. 221, issue 1, 148-154

Abstract: 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.

Keywords: Forecasting; Time series; Neural networks; Wind speed (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (51)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:221:y:2012:i:1:p:148-154

DOI: 10.1016/j.ejor.2012.02.042

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