Title :
Short term wind power forecasting using adaptive neuro-fuzzy inference systems
Author :
Johnson, Peter L. ; Negnevitsky, Michael ; Muttaqi, Kashem M.
Author_Institution :
Centre for Renewable Energy & Power Syst., Univ. of Tasmania, Hobart, TAS
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.
Keywords :
fuzzy systems; inference mechanisms; load forecasting; power markets; weather forecasting; wind power; Australian wind energy forecasting system; adaptive neuro-fuzzy inference system; climate change gains momentum; demand forecasting; electricity market; power system; short term wind power forecasting; Adaptive systems; Australia; Economic forecasting; Electricity supply industry; Load forecasting; Power system modeling; Power systems; Predictive models; Wind energy; Wind forecasting; Adaptive Neuro-Fuzzy Inference System (ANFIS); short term forecasting; wind power;
Conference_Titel :
Power Engineering Conference, 2007. AUPEC 2007. Australasian Universities
Conference_Location :
Perth, WA
Print_ISBN :
978-0-646-49488-3
Electronic_ISBN :
978-0-646-49499-1
DOI :
10.1109/AUPEC.2007.4548099