Forecasting of wind energy generation using Self-Organizing Maps and Extreme Learning Machines

Tan, K. H., Logenthiran, Thillainathan and Woo, Wai Lok (2017) Forecasting of wind energy generation using Self-Organizing Maps and Extreme Learning Machines. In: 2016 IEEE Region 10 Conference (TENCON). IEEE, pp. 451-454. ISBN 978-1-5090-2598-5

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/TENCON.2016.7848039

Abstract

This paper aims to forecast wind energy generation. With accurate forecasting of energy generation, it will aid the energy sector in managing of stability and grid planning for supplied energy. The main focus of this project is Artificial Neural Network (ANN) while the training algorithms used in this project is a combination of Self-Organizing Maps (SOM) and Extreme Learning Machines (ELM). Furthermore, the training algorithm is applied into MATLAB and simulated several times in order to obtain the optimal parameters setting so as to accurately forecast wind energy generation.

Item Type: Book Section
Uncontrolled Keywords: Wind energy Generation, Forecasting, Artificial neural network, Self-Organizing Maps, Extreme learning machine, MATLAB, Renewable energy resources
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 29 Mar 2019 15:46
Last Modified: 29 Mar 2019 15:46
URI: http://nrl.northumbria.ac.uk/id/eprint/38648

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence