A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

• Published in: IEEE access Vol. 8; pp. 83347 - 83358
• Main Authors: Tao, Hai, Salih, Sinan Q., Saggi, Mandeep Kaur, Dodangeh, Esmaeel, Voyant, Cyril, Al-Ansari, Nadhir, Yaseen, Zaher Mundher, Shahid, Shamsuddin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Algorithms; Artificial intelligence; Atmospheric modeling; Atmospheric models; Biomimetics; Computational modeling; Computer Science; Correlation coefficients; Data models; Geoteknik; Information Retrieval; Iraq region; Kernel Ridge Regression; Model accuracy; multivariate empirical mode decomposition; Numerical models; Numerical weather forecasting; Particle swarm optimization; Performance prediction; Prediction algorithms; Prediction models; Predictive models; Random forest; Soil Mechanics; Swarm intelligence; Weather stations; Wind power; Wind speed; Wind Speed prediction
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2990439

Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. Numerical Weather 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 (Station 1 ) and Mosul (Station 2 ) 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 = 0.971 during testing phase at Station 1 and Station 2 , 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.