Openly accessible

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

Hossain, Monowar, Mekhilef, Saad, Afifi, Firdaus, Halabi, Laith M., Olatomiwa, Lanre, Seyedmahmoudian, Mehdi, Horan, Ben and Stojcevski, Alex 2018, Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability, PLoS One, vol. 13, no. 4, pp. 1-31, doi: 10.1371/journal.pone.0193772.

Attached Files
Name Description MIMEType Size Downloads
seyedmahmoudian-applicationof-2018.pdf Published version application/pdf 17.01MB 1

Title Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability
Author(s) Hossain, Monowar
Mekhilef, Saad
Afifi, Firdaus
Halabi, Laith M.
Olatomiwa, Lanre
Seyedmahmoudian, Mehdi
Horan, BenORCID iD for Horan, Ben orcid.org/0000-0002-6723-259X
Stojcevski, Alex
Journal name PLoS One
Volume number 13
Issue number 4
Article ID e0193772
Start page 1
End page 31
Total pages 31
Publisher Public Library of Science (PLoS)
Place of publication San Francisco, Calif.
Publication date 2018
ISSN 1932-6203
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
EXTREME LEARNING-MACHINE
RENEWABLE ENERGY
SPEED PREDICTION
NEURAL-NETWORKS
SYSTEM
OPTIMIZATION
FEASIBILITY
IRAN
Summary 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.
Language eng
DOI 10.1371/journal.pone.0193772
Field of Research MD Multidisciplinary
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2018, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30108842

Document type: Journal Article
Collections: School of Engineering
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 22 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Thu, 31 May 2018, 11:08:59 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.