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Short term wind power forcasting using adaptive neuro-fuzzy inreference systems

Conference Paper


Abstract


  • As the global political will to address climate change gains momentum, the issues associated with integrating an increasing penetration of wind power into power systems need to be addressed. This paper summarises the current trends in wind power and how it is accepted into electricity markets. The need for accurate short term wind power forecasting is highlighted with particular reference to the five minute dispatch interval for the proposed Australian Wind Energy Forecasting System. Results from a case study show that adaptive neuro-fuzzy inference system (ANFIS) models can be a useful tool for short term wind power forecasting providing a performance improvement over the industry standard "persistence" approach.

UOW Authors


  •   Johnson, Paul (external author)
  •   Negnevitsky, Michael (external author)
  •   Muttaqi, Kashem

Publication Date


  • 2007

Citation


  • P. Johnson, M. Negnevitsky & K. M. Muttaqi , "Short term wind power forcasting using adaptive neuro-fuzzy inreference systems," in Power Engineering Conference, 2007. AUPEC 2007. Australasian Universities, 2007, pp. 652-657.

Scopus Eid


  • 2-s2.0-51349095945

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4694

Start Page


  • 652

End Page


  • 657

Abstract


  • As the global political will to address climate change gains momentum, the issues associated with integrating an increasing penetration of wind power into power systems need to be addressed. This paper summarises the current trends in wind power and how it is accepted into electricity markets. The need for accurate short term wind power forecasting is highlighted with particular reference to the five minute dispatch interval for the proposed Australian Wind Energy Forecasting System. Results from a case study show that adaptive neuro-fuzzy inference system (ANFIS) models can be a useful tool for short term wind power forecasting providing a performance improvement over the industry standard "persistence" approach.

UOW Authors


  •   Johnson, Paul (external author)
  •   Negnevitsky, Michael (external author)
  •   Muttaqi, Kashem

Publication Date


  • 2007

Citation


  • P. Johnson, M. Negnevitsky & K. M. Muttaqi , "Short term wind power forcasting using adaptive neuro-fuzzy inreference systems," in Power Engineering Conference, 2007. AUPEC 2007. Australasian Universities, 2007, pp. 652-657.

Scopus Eid


  • 2-s2.0-51349095945

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4694

Start Page


  • 652

End Page


  • 657