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

Authors

Paul Johnson, University of Tasmania
Michael Negnevitsky, University of TasmaniaFollow
Kashem M. MuttaqiFollow

RIS ID

101253

Publication Details

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.

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.