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

K. H. Tan, T. Logenthiran, W. L. Woo

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

10 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationIEEE Region 10 Annual International Conference, Proceedings/TENCON
PublisherIEEE
Pages451-454
Number of pages4
ISBN (Electronic)978-1-5090-2597-8
ISBN (Print)978-1-5090-2598-5
DOIs
Publication statusPublished - 9 Feb 2017

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON

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