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Forecasting wind speed with recurrent neural networks. (English) Zbl 1253.86008

Summary: 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.

MSC:

86A32 Geostatistics
62M20 Inference from stochastic processes and prediction
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62M45 Neural nets and related approaches to inference from stochastic processes
86A10 Meteorology and atmospheric physics
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