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A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data

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  • Sharifian, Amir
  • Ghadi, M. Jabbari
  • Ghavidel, Sahand
  • Li, Li
  • Zhang, Jiangfeng

Abstract

Nowadays, due to some environmental restrictions and decrease of fossil fuel sources, renewable energy sources and specifically wind power plants have a major part of energy generation in the industrial countries. To this end, the accurate forecasting of wind power is considered as an important and influential factor for the management and planning of power systems.

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  • Sharifian, Amir & Ghadi, M. Jabbari & Ghavidel, Sahand & Li, Li & Zhang, Jiangfeng, 2018. "A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data," Renewable Energy, Elsevier, vol. 120(C), pages 220-230.
    • Handle: RePEc:eee:renene:v:120:y:2018:i:c:p:220-230
    DOI: 10.1016/j.renene.2017.12.023
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    References listed on IDEAS

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    15. Khan, Zulfiqar Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2023. "Dual stream network with attention mechanism for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 338(C).
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    17. Zhao, Zhen-Yu & Chen, Yu-Long & Li, Heng, 2019. "What affects the development of renewable energy power generation projects in China: ISM analysis," Renewable Energy, Elsevier, vol. 131(C), pages 506-517.
    18. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    19. Xu, Xiaomin & Niu, Dongxiao & Xiao, Bowen & Guo, Xiaodan & Zhang, Lihui & Wang, Keke, 2020. "Policy analysis for grid parity of wind power generation in China," Energy Policy, Elsevier, vol. 138(C).
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