Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)

M. Mohandes*, S. Rehman, S. M. Rahman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

181 Scopus citations

Abstract

Wind energy has become a major competitor of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized wind turbines. However, wind with reasonable speed is not adequately sustainable everywhere to build an economical wind farm. The potential site has to be thoroughly investigated at least with respect to wind speed profile and air density. Wind speed increases with height, thus an increase of the height of turbine rotor leads to more generated power. Therefore, it is imperative to have a precise knowledge of wind speed profiles in order to assess the potential for a wind farm site. This paper proposes a clustering algorithm based neuro-fuzzy method to find wind speed profile up to height of 100. m based on knowledge of wind speed at heights 10, 20, 30, 40. m. The model estimated wind speed at 40. m based on measured data at 10, 20, and 30. m has 3% mean absolute percent error when compared with measured wind speed at height 40. m. This close agreement between estimated and measured wind speed at 40. m indicates the viability of the proposed method. The comparison with the 1/7th law and experimental wind shear method further proofs the suitability of the proposed method for generating wind speed profile based on knowledge of wind speed at lower heights.

Original languageEnglish
Pages (from-to)4024-4032
Number of pages9
JournalApplied Energy
Volume88
Issue number11
DOIs
StatePublished - Nov 2011

Keywords

  • ANFIS
  • Artificial neural networks
  • Fuzzy inference system
  • Wind profile
  • Wind speed

ASJC Scopus subject areas

  • Building and Construction
  • General Energy
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

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