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Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability
journal contribution
posted on 2018-01-01, 00:00 authored by Monowar Hossain, S Mekhilef, F Afifi, L M Halabi, L Olatomiwa, Mohammadmehdi Seyedmahmoudian, Ben HoranBen Horan, A StojcevskiIn 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.
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Journal
PLoS OneVolume
13Issue
4Article number
e0193772Pagination
1 - 31Publisher
Public Library of Science (PLoS)Location
San Francisco, Calif.Publisher DOI
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eISSN
1932-6203Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2018, The AuthorsUsage metrics
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