Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: Case study of Aligoodarz, Iran. (June 2016)

• Record Type: Journal Article
• Authors: Shamshirband, Shahaboddin; Keivani, Afram; Mohammadi, Kasra; Lee, Malrey; Hamid, Siti Hafizah Abd; Petkovic, Dalibor
• Is Part Of: Renewable & sustainable energy reviews. Volume 59(2016:Jun.)
• Page Start: 429
• Page End: 435
• Publication Date: 2016-06
• Subjects: Wind power prediction -- ANFIS -- Weibull distribution -- Statistical indicators; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2015.12.269
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: The prime aim of this study is appraising the suitability of adaptive neuro-fuzzy inference framework (ANFIS) to compute the monthly wind power density. On this account, the extracted wind power from Weibull functions are utilized for training and testing the developed ANFIS model. The proficiency of the ANFIS model is certified by providing thorough statistical comparisons with artificial neural network (ANN) and genetic programming (GP) techniques. The computed wind power by all models are compared with those obtained using measured data. The study results clearly indicate that the proposed ANFIS model enjoys high capability and reliability to estimate wind power density so that it presents high superiority over the developed ANN and GP models. Based upon relative percentage error (RPE) values, all estimated wind power values via ANFIS model are within the acceptable range of −10% to 10%. Additionally, relative root mean square error (RRMSE) analysis shows that ANFIS model has an excellent performance for estimation of wind power density.