Wind speed forecasting using neural networks. Issue 1 (February 2020)

• Record Type: Journal Article
• Authors: Blanchard, Tyler; Samanta, Biswanath
• Is Part Of: Wind engineering. Volume 44:Issue 1(2020)
• Page Start: 33
• Page End: 48
• Publication Date: 2020-02
• Subjects: Artificial neural network -- forecasting -- multi-step-ahead prediction -- nonlinear autoregressive networks -- nonlinear autoregressive neural network with exogenous inputs networks -- single-step-ahead prediction -- time series prediction -- wind speed prediction -- wind energy site selection; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X19849846
• Languages: English
• ISSNs: 0309-524X

Abstract

The prediction of wind speed is critical in the assessment of feasibility of a potential wind turbine site. This work presents a study on prediction of wind speed using artificial neural networks. Two variations of artificial neural networks, namely, nonlinear autoregressive neural network and nonlinear autoregressive neural network with exogenous inputs, were used to predict wind speed utilizing 1 year of hourly weather data from four locations around the United States to train, validate, and test these networks. This study optimized both neural network configurations and it demonstrated that both models were suitable for wind speed prediction. Both models outperformed persistence model (with a factor of about 2 to 10 in root mean square error ratio). Both artificial neural network models were implemented for single-step and multi-step-ahead prediction of wind speed for all four locations and results were compared. Nonlinear autoregressive neural network with exogenous inputs model gave better prediction performance than nonlinear autoregressive model and the difference was statistically significant.