@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",
}
