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

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

doi:10.1109/TENCON.2016.7848039

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

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

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

11 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 language	English
Title of host publication	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Publisher	IEEE
Pages	451-454
Number of pages	4
ISBN (Electronic)	978-1-5090-2597-8
ISBN (Print)	978-1-5090-2598-5
DOIs	https://doi.org/10.1109/TENCON.2016.7848039
Publication status	Published - 9 Feb 2017

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Artificial neural network
Extreme learning machine
Forecasting
MATLAB
Renewable energy resources
Self-Organizing Maps
Wind energy Generation

Access to Document

10.1109/TENCON.2016.7848039

Cite this

@inbook{cc4e8b20f946432a8aa859070d7b21f3,

title = "Forecasting of wind energy generation using Self-Organizing Maps and Extreme Learning Machines",

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.",

keywords = "Artificial neural network, Extreme learning machine, Forecasting, MATLAB, Renewable energy resources, Self-Organizing Maps, Wind energy Generation",

author = "Tan, \{K. H.\} and T. Logenthiran and Woo, \{W. L.\}",

year = "2017",

month = feb,

day = "9",

doi = "10.1109/TENCON.2016.7848039",

language = "English",

isbn = "978-1-5090-2598-5",

series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

publisher = "IEEE",

pages = "451--454",

booktitle = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

address = "United States",

}

Forecasting of wind energy generation using Self-Organizing Maps and Extreme Learning Machines. / Tan, K. H.; Logenthiran, T.; Woo, W. L.
IEEE Region 10 Annual International Conference, Proceedings/TENCON. IEEE, 2017. p. 451-454 (IEEE Region 10 Annual International Conference, Proceedings/TENCON).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Forecasting of wind energy generation using Self-Organizing Maps and Extreme Learning Machines

AU - Tan, K. H.

AU - Logenthiran, T.

AU - Woo, W. L.

PY - 2017/2/9

Y1 - 2017/2/9

N2 - 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.

AB - 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.

KW - Artificial neural network

KW - Extreme learning machine

KW - Forecasting

KW - MATLAB

KW - Renewable energy resources

KW - Self-Organizing Maps

KW - Wind energy Generation

UR - https://www.scopus.com/pages/publications/85015413904

U2 - 10.1109/TENCON.2016.7848039

DO - 10.1109/TENCON.2016.7848039

M3 - Chapter

SN - 978-1-5090-2598-5

T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON

SP - 451

EP - 454

BT - IEEE Region 10 Annual International Conference, Proceedings/TENCON

PB - IEEE

ER -

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

Abstract

Publication series

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this