Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability

Monowar Hossain; Saad Mekhilef; Firdaus Afifi; Laith M Halabi; Lanre Olatomiwa; Mehdi Seyedmahmoudian; Ben Horan; Alex Stojcevski

doi:10.1371/journal.pone.0193772

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability

PLoS One. 2018 Apr 27;13(4):e0193772. doi: 10.1371/journal.pone.0193772. eCollection 2018.

Authors

Monowar Hossain¹, Saad Mekhilef¹, Firdaus Afifi², Laith M Halabi¹, Lanre Olatomiwa³, Mehdi Seyedmahmoudian⁴, Ben Horan⁵, Alex Stojcevski⁴

Affiliations

¹ Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, University of Malaya, Kuala Lumpur, Malaysia.
² Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
³ Department of Electrical and Electronic Engineering, Federal University of Technology, PMB 65, Minna, Nigeria.
⁴ School of Software and Electrical Engineering, Swinburne University of Technology, Victoria, Australia.
⁵ School of Engineering, Deakin University, Victoria, Australia.

Abstract

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Fuzzy Logic
Malaysia
Models, Theoretical*
Renewable Energy*
Wind*

Grants and funding

The authors would like to acknowledge the financial support received from the University of Malaya, Malaysia, through Frontier Research Grant No. FG007-17AFR and Innovative Technology Grant No. RP043B-17AET.