Multistep short-term wind speed prediction using nonlinear auto-regressive neural network with exogenous variable selection

Alexandria Engineering Journal

Ali Q. Al-Shetwi¹  Janaka Ekanayake²  Ahmed Ibrahim Alzahrani³  Ammar Ahmed Alkahtani⁴  Sieh Kiong Tiong⁴  Gamal Alkawsi⁴  Nasser Alalwan⁴  Fuad Noman⁴ 

[1] Corresponding author.;Computer Science Department, Community College, King Saud University, Saudi Arabia;Department of Electrical and Electronic Engineering, University of Peradeniya, Kandy, Sri Lanka;Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional, Kajang, Malaysia;

关键词: Multistep;    Multivariate analysis;    NARX;    Neural networks;    Wind speed prediction;   

DOI  :  

来源: DOAJ

【 摘 要 】

Precise wind speed prediction is a key factor in many energy applications, especially when wind energy is integrated with power grids. However, because of the intermittent and nonstationary nature of wind speed, modeling and predicting it is a challenge. In addition, using uncorrelated multivariate variables as exogenous input variables often adversely impacts the performance of prediction models. In this paper, we present a multistep short-term wind speed prediction using multivariate exogenous input variables. We implement different variable selection methods to select the best set of variables that significantly improve the performance of prediction models. We evaluate the performance of eight transfer learning methods, four shallow neural networks (NNs), and the persistence method on predicting the future values of wind speed using ultrashort-term, short-term, and multistep time horizons. We performed the evaluation over two-year high-sampled wind speed data averaged at 10-minute intervals. Results show that Nonlinear Auto-Regressive Exogenous (NARX) model outperformed all other methods, achieving an average mean absolute error (MAE) and root mean square error (RMSE) of 0.2205 and 0.3405 for multistep predictions, respectively. Despite the lower performance of the transfer learning methods (i.e., 0.43 and 0.58 for MAE and RMSE, respectively), it is believed that results could be further improved with a better enhancement of the feature selection and model parameters.

【 授权许可】

Unknown