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Interpolation of missing wind data based on ANFIS

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  • Yang, Zhiling
  • Liu, Yongqian
  • Li, Chengrong

Abstract

Measured wind data is one of the key input data for wind farm planning and design. There are always some missing and invalid data in wind measurement, which poses the main challenges for wind energy resources assessment. In this paper, the rules of integrity check and reasonableness check are introduced, then an adaptive neuro-fuzzy inference system (ANFIS) model is proposed, in which fuzzy inference algorithm are used to interpolate the missing and invalid wind data. A further comparison and analysis is given between the calculating result and measured data. Meanwhile Using methods of wind shear coefficient and ANFIS, 12 measured wind data sets from a wind farm in North China are interpolated and analyzed, respectively. The results proved the effectiveness of ANFIS.

Suggested Citation

  • Yang, Zhiling & Liu, Yongqian & Li, Chengrong, 2011. "Interpolation of missing wind data based on ANFIS," Renewable Energy, Elsevier, vol. 36(3), pages 993-998.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:3:p:993-998
    DOI: 10.1016/j.renene.2010.08.033
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    Cited by:

    1. Theo, Wai Lip & Lim, Jeng Shiun & Ho, Wai Shin & Hashim, Haslenda & Lee, Chew Tin, 2017. "Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 531-573.
    2. Isa Ebtehaj & Hossein Bonakdari, 2014. "Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4765-4779, October.
    3. Wang, Jianzhou & Dong, Yunxuan & Zhang, Kequan & Guo, Zhenhai, 2017. "A numerical model based on prior distribution fuzzy inference and neural networks," Renewable Energy, Elsevier, vol. 112(C), pages 486-497.
    4. Korprasertsak, Natapol & Leephakpreeda, Thananchai, 2018. "Nyquist-based adaptive sampling rate for wind measurement under varying wind conditions," Renewable Energy, Elsevier, vol. 119(C), pages 290-298.
    5. Mehrbakhsh Nilashi & Fausto Cavallaro & Abbas Mardani & Edmundas Kazimieras Zavadskas & Sarminah Samad & Othman Ibrahim, 2018. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    6. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    7. Mingzhe Zou & Sasa Z. Djokic, 2020. "A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements," Energies, MDPI, vol. 13(16), pages 1-30, August.
    8. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    9. Al-Ghandoor, Ahmed & Samhouri, Murad & Al-Hinti, Ismael & Jaber, Jamal & Al-Rawashdeh, Mohammad, 2012. "Projection of future transport energy demand of Jordan using adaptive neuro-fuzzy technique," Energy, Elsevier, vol. 38(1), pages 128-135.
    10. Asmaa Alazmi & Bader S. Al-Anzi, 2023. "Assessment of Machine Learning Algorithms for Predicting Air Entrainment Rates in a Confined Plunging Liquid Jet Reactor," Sustainability, MDPI, vol. 15(18), pages 1-13, September.
    11. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.

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