Efficient Modelling and Simulation Of Wind Power Using Online Sequential Learning Algorithm For Feed Forward Networks

Rashmi P. Shetty (), A. Sathyabhama and Srinivasa Pai P.
Additional contact information
Rashmi P. Shetty: Department of Mechanical engineering, National Institute of Technology Karnataka, 575025, India
A. Sathyabhama: Department of Mechanical engineering, National Institute of Technology Karnataka, 575025, India
Srinivasa Pai P.: Department of Mechanical engineering, NMAM Institute of Technology, Nitte, 574110, India

Journal of Mechanical Engineering Research & Developments (JMERD), 2019, vol. 42, issue 1, 109-115

Abstract: In this paper, an online sequential learning algorithm known as online sequential extreme learning machine (OS ELM) is applied to simulate the power output of a wind turbine. The OS ELM is used both in 1-by-1 and chunk-by-chunk mode and the results are compared with batch learning algorithms, namely Back Propagation (BP) and Extreme Learning Machine (ELM) algorithm. Different activation functions such as Sigmoidal, Sin, Radial Basis Function (RBF) and Hardlim have been used in OS ELM to decide upon most optimal function. It has been found that OS ELM with fixed chunk size of 50-by-50 and sigmoidal activation function with training time of 0.080s, Root Mean Square Error (RMSE) of 1.96%, prediction accuracies on training and test data of 100% and 99.95 % respectively, is best suited for wind power modelling and simulation applications, where the data arrives in a sequential manner.

Keywords: Extreme Learning Machine (ELM); Online Sequential ELM (OS ELM); Wind power modelling; Back Propagation (BP) (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://jmerd.org.my/download/3354/ (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zib:zjmerd:v:42:y:2019:i:1:p:109-115

DOI: 10.26480/jmerd.01.2019.109.115

Access Statistics for this article

Journal of Mechanical Engineering Research & Developments (JMERD) is currently edited by Distinguished Prof. T M Indra Mahlia

More articles in Journal of Mechanical Engineering Research & Developments (JMERD) from Zibeline International Publishing
Bibliographic data for series maintained by Zibeline International Publishing ( this e-mail address is bad, please contact ).