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

Hossain, Monowar and Mekhilef, Saad and Afifi, Firdaus and Halabi, Laith M. and Olatomiwa, Lanre and Seyedmahmoudian, Mehdi and Horan, Ben and Stojcevski, Alex (2018) Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability. PLoS ONE, 13 (4). e0193772. ISSN 1932-6203

Full text not available from this repository.
Official URL: https://doi.org/10.1371/journal.pone.0193772

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 (R 2 ). 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.

Item Type: Article
Uncontrolled Keywords: Algorithms; Fuzzy Logic; Malaysia; Models, Theoretical; Renewable Energy; Wind
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Computer Science & Information Technology
Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 07 Aug 2019 08:08
Last Modified: 07 Aug 2019 08:08
URI: http://eprints.um.edu.my/id/eprint/21871

Actions (login required)

View Item View Item