Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting
Joint Authors
Senu, Norazak
Faghihnia, E.
Ahmadian, Ali
Salahshour, Soheil
Source
Advances in Mathematical Physics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-16
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability.
Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems.
Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting.
In this paper, a local neurofuzzy (LNF) approach, trained by the polynomial model tree (POLYMOT) learning algorithm, is proposed for short-term wind power forecasting.
The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation.
Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach.
Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting.
American Psychological Association (APA)
Faghihnia, E.& Salahshour, Soheil& Ahmadian, Ali& Senu, Norazak. 2014. Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting. Advances in Mathematical Physics،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-487094
Modern Language Association (MLA)
Faghihnia, E.…[et al.]. Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting. Advances in Mathematical Physics No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-487094
American Medical Association (AMA)
Faghihnia, E.& Salahshour, Soheil& Ahmadian, Ali& Senu, Norazak. Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting. Advances in Mathematical Physics. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-487094
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-487094