Multistep short-term wind speed prediction using nonlinear auto-regressive neural network with exogenous variable selection
Electric power transmission networks; Forecasting; Mean square error; Neural networks; Predictive analytics; Speed; Transfer learning; Wind; Wind power; Exogenous variables; Multi-step prediction; Neural networks (NNS); Root mean square errors; Short-term wind speed predictions; Transfer learning me...
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2023
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my.uniten.dspace-263372023-05-29T17:09:16Z Multistep short-term wind speed prediction using nonlinear auto-regressive neural network with exogenous variable selection Noman F. Alkawsi G. Alkahtani A.A. Al-Shetwi A.Q. Kiong Tiong S. Alalwan N. Ekanayake J. Alzahrani A.I. 55327881300 57191982354 55646765500 57004922700 57219799117 35309472000 7003409510 54912750300 Electric power transmission networks; Forecasting; Mean square error; Neural networks; Predictive analytics; Speed; Transfer learning; Wind; Wind power; Exogenous variables; Multi-step prediction; Neural networks (NNS); Root mean square errors; Short-term wind speed predictions; Transfer learning methods; Variable selection methods; Wind speed prediction; Learning systems 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. � 2020 THE AUTHORS Final 2023-05-29T09:09:16Z 2023-05-29T09:09:16Z 2021 Article 10.1016/j.aej.2020.10.045 2-s2.0-85095564934 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095564934&doi=10.1016%2fj.aej.2020.10.045&partnerID=40&md5=c8ede2d5f6c7fd8f0ba6836dc4c7a976 https://irepository.uniten.edu.my/handle/123456789/26337 60 1 1221 1229 All Open Access, Gold Elsevier B.V. Scopus |
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| description |
Electric power transmission networks; Forecasting; Mean square error; Neural networks; Predictive analytics; Speed; Transfer learning; Wind; Wind power; Exogenous variables; Multi-step prediction; Neural networks (NNS); Root mean square errors; Short-term wind speed predictions; Transfer learning methods; Variable selection methods; Wind speed prediction; Learning systems |
| author2 |
55327881300 |
| author_facet |
55327881300 Noman F. Alkawsi G. Alkahtani A.A. Al-Shetwi A.Q. Kiong Tiong S. Alalwan N. Ekanayake J. Alzahrani A.I. |
| format |
Article |
| author |
Noman F. Alkawsi G. Alkahtani A.A. Al-Shetwi A.Q. Kiong Tiong S. Alalwan N. Ekanayake J. Alzahrani A.I. |
| spellingShingle |
Noman F. Alkawsi G. Alkahtani A.A. Al-Shetwi A.Q. Kiong Tiong S. Alalwan N. Ekanayake J. Alzahrani A.I. Multistep short-term wind speed prediction using nonlinear auto-regressive neural network with exogenous variable selection |
| author_sort |
Noman F. |
| title |
Multistep short-term wind speed prediction using nonlinear auto-regressive neural network with exogenous variable selection |
| title_short |
Multistep short-term wind speed prediction using nonlinear auto-regressive neural network with exogenous variable selection |
| title_full |
Multistep short-term wind speed prediction using nonlinear auto-regressive neural network with exogenous variable selection |
| title_fullStr |
Multistep short-term wind speed prediction using nonlinear auto-regressive neural network with exogenous variable selection |
| title_full_unstemmed |
Multistep short-term wind speed prediction using nonlinear auto-regressive neural network with exogenous variable selection |
| title_sort |
multistep short-term wind speed prediction using nonlinear auto-regressive neural network with exogenous variable selection |
| publisher |
Elsevier B.V. |
| publishDate |
2023 |
| _version_ |
1806427395717070848 |
| score |
13.222552 |