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

Mandeep Kaur Saggi,Cyril Voyant,Shamsuddin Shahid,Hai Tao,Esmaeel Dodangeh,Nadhir Al-Ansari,Zaher Mundher Yaseen,Sinan Q. Salih

doi:10.1109/access.2020.2990439

Abstract

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 (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 = 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.

Highlights

The importance of wind speed prediction in wind energy farm operation and maintenance has increased over the years [1], [2]
Novel wind speed (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
The MEMD-Particle swarm optimization algorithm (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

Summary

Introduction

The importance of wind speed prediction in wind energy farm operation and maintenance has increased over the years [1], [2]. The sustained increase in the rate of wind turbines erections demands the deployment of optimal dispatching strategy that will guarantee stable power generation by the wind turbines without having much influence on the power. The cost of wind farm operation may be affected by imperfect predictions due to the underlying uncertainties in WS [3], [4]. The availability of wind resources must be considered for a maintenance schedule to ensure optimal maintenance for reducing the turbines’ production loss [5], [6]. Accurate WS prediction has grasped research attention in recent years due to its great practical and academic values.

Methods

Results

Conclusion