Neural Computing and Applications

, Volume 18, Issue 3, pp 207–212 | Cite as

Missing wind data forecasting with adaptive neuro-fuzzy inference system

Original Article

Abstract

In any region, to begin generating electricity from wind energy, it is necessary to determine the 1-year distribution characteristics of wind speed. For this aim, a wind observation station must be constructed and 1-year wind speed and direction data must be collected. For determining the distribution characteristics, the collected data must be statistically analyzed. The continuity and reliability of the data are quite important for such studies on the days when possible faults can occur in any part of the observation unit or on days when, the system is on maintenance, it is not possible to record any data. In this study, it is assumed that the station had not worked at some randomly chosen days and that for these days no data could be recorded. The missing data are predicted using the data that were recorded before and after fault or maintenance by an adaptive neuro-fuzzy inference system (ANFIS). It is seen that ANFIS is successful for such a study.

Keywords

Wind energy Wind speed Forecasting Missing data ANFIS Back-propagation 

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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Fatih O. Hocaoglu
    • 1
  • Yusuf Oysal
    • 2
  • Mehmet Kurban
    • 1
  1. 1.Faculty of Engineering and Architecture, Department of Electrical and Electronics EngineeringAnadolu UniversityEskişehirTurkey
  2. 2.Faculty of Engineering and Architecture, Department of Computer EngineeringAnadolu UniversityEskişehirTurkey

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