Predicting the wind power density based upon extreme learning machine. (15th June 2015)

• Record Type: Journal Article
• Authors: Mohammadi, Kasra; Shamshirband, Shahaboddin; Yee, Por Lip; Petković, Dalibor; Zamani, Mazdak; Ch, Sudheer
• Is Part Of: Energy. Volume 86(2015)
• Page Start: 232
• Page End: 239
• Publication Date: 2015-06-15
• Subjects: Wind power density -- ELM (extreme learning machine) -- Weibull method -- Prediction; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.03.111
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Precise predictions of wind power density play a substantial role in determining the viability of wind energy harnessing. In fact, reliable prediction is particularly useful for operators and investors to offer a secure situation with minimal economic risks. In this paper, a new model based upon ELM (extreme learning machine) is presented to estimate the wind power density. Generally, the two-parameter Weibull function has been normally used and recognized as a reliable method in wind energy estimations for most windy regions. Thus, the required data for training and testing were extracted from two accurate Weibull methods of standard deviation and power density. The validity of the ELM model is verified by comparing its predictions with SVM (Support Vector Machine), ANN (Artificial Neural Network) and GP (Genetic Programming) techniques. The wind powers predicted by all approaches are compared with those calculated using measured data. Based upon simulation results, it is demonstrated that ELM can be utilized effectively in applications of wind power predictions. In a nutshell, the survey results show that the proposed ELM model is suitable and precise to predict wind power density and has much higher performance than the other approaches examined in this study. Highlights: ELM (extreme learning machine) is applied to predict wind power density. Outlines of two Weibull methods are used as training and testing data. The capability of ELM is appraised compared to 3 different approaches. ELM can be utilized effectively in applications of wind power predictions.