Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability
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...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Published: |
Public Library of Science
2018
|
| Subjects: |
| _version_ | 1796961522355798016 |
|---|---|
| author | Hossain, Monowar Mekhilef, Saad Afifi, Firdaus Halabi, Laith M. Olatomiwa, Lanre Seyedmahmoudian, Mehdi Horan, Ben Stojcevski, Alex |
| author_facet | Hossain, Monowar Mekhilef, Saad Afifi, Firdaus Halabi, Laith M. Olatomiwa, Lanre Seyedmahmoudian, Mehdi Horan, Ben Stojcevski, Alex |
| author_sort | Hossain, Monowar |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T05:55:22Z |
| format | Article |
| id | um.eprints-21871 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:55:22Z |
| publishDate | 2018 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | um.eprints-218712019-08-07T08:08:51Z http://eprints.um.edu.my/21871/ 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 Stojcevski, Alex QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering 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. Public Library of Science 2018 Article PeerReviewed 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, DOI https://doi.org/10.1371/journal.pone.0193772 <https://doi.org/10.1371/journal.pone.0193772>. https://doi.org/10.1371/journal.pone.0193772 doi:10.1371/journal.pone.0193772 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Hossain, Monowar Mekhilef, Saad Afifi, Firdaus Halabi, Laith M. Olatomiwa, Lanre Seyedmahmoudian, Mehdi Horan, Ben Stojcevski, Alex Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability |
| title | Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability |
| title_full | Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability |
| title_fullStr | Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability |
| title_full_unstemmed | Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability |
| title_short | Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability |
| title_sort | application of the hybrid anfis models for long term wind power density prediction with extrapolation capability |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT hossainmonowar applicationofthehybridanfismodelsforlongtermwindpowerdensitypredictionwithextrapolationcapability AT mekhilefsaad applicationofthehybridanfismodelsforlongtermwindpowerdensitypredictionwithextrapolationcapability AT afififirdaus applicationofthehybridanfismodelsforlongtermwindpowerdensitypredictionwithextrapolationcapability AT halabilaithm applicationofthehybridanfismodelsforlongtermwindpowerdensitypredictionwithextrapolationcapability AT olatomiwalanre applicationofthehybridanfismodelsforlongtermwindpowerdensitypredictionwithextrapolationcapability AT seyedmahmoudianmehdi applicationofthehybridanfismodelsforlongtermwindpowerdensitypredictionwithextrapolationcapability AT horanben applicationofthehybridanfismodelsforlongtermwindpowerdensitypredictionwithextrapolationcapability AT stojcevskialex applicationofthehybridanfismodelsforlongtermwindpowerdensitypredictionwithextrapolationcapability |