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

Author

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

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.

Suggested Citation

  • Monowar Hossain & Saad Mekhilef & Firdaus Afifi & Laith M Halabi & Lanre Olatomiwa & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2018. "Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-31, April.
    • Handle: RePEc:plo:pone00:0193772
    DOI: 10.1371/journal.pone.0193772
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    References listed on IDEAS

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    1. Ray Huffaker & Marco Bittelli, 2015. "A Nonlinear Dynamics Approach for Incorporating Wind-Speed Patterns into Wind-Power Project Evaluation," PLOS ONE, Public Library of Science, vol. 10(1), pages 1-17, January.
    2. Osvaldo Rodríguez & Jesús A del Río & Oscar A Jaramillo & Manuel Martínez, 2015. "Wind Power Error Estimation in Resource Assessments," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
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    Cited by:

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    2. Mahdis sadat Jalaee & Amin GhasemiNejad & Sayyed Abdolmajid Jalaee & Naeeme Amani Zarin & Reza Derakhshani, 2022. "A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 15(7), pages 1-14, April.
    3. Kaloop, Mosbeh R. & Bardhan, Abidhan & Kardani, Navid & Samui, Pijush & Hu, Jong Wan & Ramzy, Ahmed, 2021. "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    4. Simeunović, Jelena & Schubnel, Baptiste & Alet, Pierre-Jean & Carrillo, Rafael E. & Frossard, Pascal, 2022. "Interpretable temporal-spatial graph attention network for multi-site PV power forecasting," Applied Energy, Elsevier, vol. 327(C).
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    6. Chen, Liang, 2020. "Impacts of climate change on wind resources over North America based on NA-CORDEX," Renewable Energy, Elsevier, vol. 153(C), pages 1428-1438.

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