A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting
Curve fitting; Deep neural networks; Errors; Forecasting; Gas emissions; Gas plants; Global warming; Graphic methods; Mean square error; Recurrent neural networks; Time series; Time series analysis; Wind; Forecasting: applications; NARX neural network; Network-based approach; Neural network model; P...
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Springer Science and Business Media Deutschland GmbH
2023
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my.uniten.dspace-272742023-05-29T17:41:59Z A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting Rahman M.M. Shakeri M. Khatun F. Tiong S.K. Alkahtani A.A. Samsudin N.A. Amin N. Pasupuleti J. Hasan M.K. 57207730841 55433849200 57516189300 15128307800 55646765500 57190525429 7102424614 11340187300 55057479600 Curve fitting; Deep neural networks; Errors; Forecasting; Gas emissions; Gas plants; Global warming; Graphic methods; Mean square error; Recurrent neural networks; Time series; Time series analysis; Wind; Forecasting: applications; NARX neural network; Network-based approach; Neural network model; Performance; Prediction modelling; Renewable energies; Time series forecasting; Wind speed prediction; Wind time series; Greenhouse gases The increasing energy demand and expansion of power plants are provoking the effects of greenhouse gas emissions and global warming. To mitigate these issues, renewable energies (like solar, wind, and hydropower) are blessings for modern energy sectors. The study focuses on wind-speed prediction in energy forecasting applications. This paper is a comprehensive review of deep neural network based approaches, like the �nonlinear autoregressive exogenous inputs (NARX)�, �nonlinear input-output (NIO)� and �nonlinear autoregressive (NAR)� neural network models, in time-series forecasting applications. This study proposed NARX based prediction models in wind-speed forecasting for short-term scheme. The meteorological parameters related to wind time-series have been analyzed, and used for evaluating the performance of the proposed models. The experiments revealed the best performance of the prediction models in terms of �mean square error (MSE)�, �correlation-coefficient (R2)�, �auto-correlation�, �error-histogram�, and �input-error cross-correlation�. Comparing with the other neural network models, like �recurrent neural network (RNN)� and �curve fitting neural network (CFNN)� models, the NARX-based prediction model achieved better performance in regard to �auto-correlation�, �error-histogram�, �input-error cross-correlation�, and training time. The results also showed that the RNN and CFNN models performed better prediction accuracy with R2 and MSE values. While this performance index is slightly higher, it is negligible in forecasting applications and concluded that the proposed NARX-based model achieved the better prediction accuracy in terms of other performance evaluation measures. � 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG. Article in Press 2023-05-29T09:41:59Z 2023-05-29T09:41:59Z 2022 Article 10.1007/s40860-021-00166-x 2-s2.0-85122734862 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122734862&doi=10.1007%2fs40860-021-00166-x&partnerID=40&md5=b1dcf2a2de0af5ea06567d8c3bb5fafe https://irepository.uniten.edu.my/handle/123456789/27274 Springer Science and Business Media Deutschland GmbH Scopus |
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Curve fitting; Deep neural networks; Errors; Forecasting; Gas emissions; Gas plants; Global warming; Graphic methods; Mean square error; Recurrent neural networks; Time series; Time series analysis; Wind; Forecasting: applications; NARX neural network; Network-based approach; Neural network model; Performance; Prediction modelling; Renewable energies; Time series forecasting; Wind speed prediction; Wind time series; Greenhouse gases |
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57207730841 |
| author_facet |
57207730841 Rahman M.M. Shakeri M. Khatun F. Tiong S.K. Alkahtani A.A. Samsudin N.A. Amin N. Pasupuleti J. Hasan M.K. |
| format |
Article |
| author |
Rahman M.M. Shakeri M. Khatun F. Tiong S.K. Alkahtani A.A. Samsudin N.A. Amin N. Pasupuleti J. Hasan M.K. |
| spellingShingle |
Rahman M.M. Shakeri M. Khatun F. Tiong S.K. Alkahtani A.A. Samsudin N.A. Amin N. Pasupuleti J. Hasan M.K. A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting |
| author_sort |
Rahman M.M. |
| title |
A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting |
| title_short |
A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting |
| title_full |
A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting |
| title_fullStr |
A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting |
| title_full_unstemmed |
A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting |
| title_sort |
comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2023 |
| _version_ |
1806426253551468544 |
| score |
13.222552 |