Predicting the wind power density based upon extreme learning machine

• Main Authors: Mohammadi, Kasra, Shamshirband, Shahaboddin, Por, LipYee, Petkovic, Dalibor, Zamani, Mazdak, Ch., Sudheer
• Format: Article
• Published: Elsevier Limited 2015
• Subjects: T Technology (General)
• Online Access: http://eprints.utm.my/id/eprint/54985/; http://dx.doi.org/10.1016/j.energy.2015.03.111
• Institution: Universiti Teknologi Malaysia

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...