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A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction

Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. NumericalWeather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon. Novel WS prediction models based on the multivaria...

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Main Authors:	Tao, H., Salih, S. Q., Saggi, M. K., Dodangeh, E., Voyant, C., Al-Ansari, N., Yaseen, Z. M., Shahid, S.
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2020
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/87661/1/ZaherMundherYaseen2020_ANewlyDevelopedIntegrativeBioInspiredArtificialIntelligence.pdf http://eprints.utm.my/id/eprint/87661/ http://www.dx.doi.org/10.1109/ACCESS.2020.2990439
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spelling	my.utm.876612020-11-30T13:08:20Z http://eprints.utm.my/id/eprint/87661/ A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction Tao, H. Salih, S. Q. Saggi, M. K. Dodangeh, E. Voyant, C. Al-Ansari, N. Yaseen, Z. M. Shahid, S. TA Engineering (General). Civil engineering (General) Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. NumericalWeather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon. Novel WS prediction models based on the multivariate empirical mode decomposition (MEMD), random forest (RF) and Kernel Ridge Regression (KRR) were constructed in this paper better accuracy in WS prediction. Particle swarm optimization algorithm (PSO) was employed to optimize the parameters of the hybridized MEMD model with RF (MEMD-PSO-RF) and KRR (MEMD-PSO-KRR) models. Obtained results were compared to those of the standalone RF and KRR models. The proposed methodology is applied for monthly WS prediction at meteorological stations of Iraq, Baghdad (Station1) and Mosul (Station2) for the period 1977-2013. Results showed higher accuracy of MEMD-PSO-RF model in predicting WS at both stations with a correlation coefficient (r) of 0.972 and r D 0.971 during testing phase at Station1 and Station2, respectively. The MEMD-PSO-KRR was found as the second most accurate model followed by Standalone RF and KRR, but all showed a competitive performance to the MEMD-PSO-RF model. The outcomes of this work indicated that the MEMD-PSO-RF model has a remarkable performance in predicting WS and can be considered for practical applications. Institute of Electrical and Electronics Engineers Inc. 2020 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/87661/1/ZaherMundherYaseen2020_ANewlyDevelopedIntegrativeBioInspiredArtificialIntelligence.pdf Tao, H. and Salih, S. Q. and Saggi, M. K. and Dodangeh, E. and Voyant, C. and Al-Ansari, N. and Yaseen, Z. M. and Shahid, S. (2020) A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction. IEEE Access, 8 . pp. 83347-83358. ISSN 2169-3536 http://www.dx.doi.org/10.1109/ACCESS.2020.2990439 DOI: 10.1109/ACCESS.2020.2990439
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Tao, H. Salih, S. Q. Saggi, M. K. Dodangeh, E. Voyant, C. Al-Ansari, N. Yaseen, Z. M. Shahid, S. A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction
description	Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. NumericalWeather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon. Novel WS prediction models based on the multivariate empirical mode decomposition (MEMD), random forest (RF) and Kernel Ridge Regression (KRR) were constructed in this paper better accuracy in WS prediction. Particle swarm optimization algorithm (PSO) was employed to optimize the parameters of the hybridized MEMD model with RF (MEMD-PSO-RF) and KRR (MEMD-PSO-KRR) models. Obtained results were compared to those of the standalone RF and KRR models. The proposed methodology is applied for monthly WS prediction at meteorological stations of Iraq, Baghdad (Station1) and Mosul (Station2) for the period 1977-2013. Results showed higher accuracy of MEMD-PSO-RF model in predicting WS at both stations with a correlation coefficient (r) of 0.972 and r D 0.971 during testing phase at Station1 and Station2, respectively. The MEMD-PSO-KRR was found as the second most accurate model followed by Standalone RF and KRR, but all showed a competitive performance to the MEMD-PSO-RF model. The outcomes of this work indicated that the MEMD-PSO-RF model has a remarkable performance in predicting WS and can be considered for practical applications.
format	Article
author	Tao, H. Salih, S. Q. Saggi, M. K. Dodangeh, E. Voyant, C. Al-Ansari, N. Yaseen, Z. M. Shahid, S.
author_facet	Tao, H. Salih, S. Q. Saggi, M. K. Dodangeh, E. Voyant, C. Al-Ansari, N. Yaseen, Z. M. Shahid, S.
author_sort	Tao, H.
title	A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction
title_short	A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction
title_full	A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction
title_fullStr	A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction
title_full_unstemmed	A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction
title_sort	newly developed integrative bio-inspired artificial intelligence model for wind speed prediction
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2020
url	http://eprints.utm.my/id/eprint/87661/1/ZaherMundherYaseen2020_ANewlyDevelopedIntegrativeBioInspiredArtificialIntelligence.pdf http://eprints.utm.my/id/eprint/87661/ http://www.dx.doi.org/10.1109/ACCESS.2020.2990439
_version_	1685578970209189888
score	13.211869

A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction

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