You are not logged in.

Forecasting the characteristics of renewable energy sources using machine learning techniques

Shafiullah, G. M., Oo, A., Jarvis, D., Ali, S. and Wolfs, P. 2010, Forecasting the characteristics of renewable energy sources using machine learning techniques, in IECHAR 2010 : Challenges, Technologies and Opportunities : Proceedings of the 2010 International Engineering Conference on Hot Arid Regions, King Faisal University, Al-Ahsa, Saudi Arabia, pp. 1-1.

Attached Files
Name Description MIMEType Size Downloads

Title Forecasting the characteristics of renewable energy sources using machine learning techniques
Author(s) Shafiullah, G. M.
Oo, A.
Jarvis, D.
Ali, S.
Wolfs, P.
Conference name International Engineering Conference on Hot Arid Regions (2010 : Al-Ahsa, Saudi Arabia)
Conference location Al-Ahsa, Saudi Arabia
Conference dates 1 - 2 May 2010
Title of proceedings IECHAR 2010 : Challenges, Technologies and Opportunities : Proceedings of the 2010 International Engineering Conference on Hot Arid Regions
Editor(s) [Unknown]
Publication date 2010
Conference series International Engineering Conference on Hot Arid Regions
Start page 1
End page 1
Total pages 1
Publisher King Faisal University
Place of publication Al-Ahsa, Saudi Arabia
Keyword(s) renewable energy sources
characteristics
forecasting
machine learning techniques
Language eng
Field of Research 090607 Power and Energy Systems Engineering (excl Renewable Power)
090608 Renewable Power and Energy Systems Engineering (excl Solar Cells)
090699 Electrical and Electronic Engineering not elsewhere classified
Socio Economic Objective 850604 Energy Transmission and Distribution (excl. Hydrogen)
HERDC Research category E1.1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30056461

Document type: Conference Paper
Collection: School of Engineering
Connect to link resolver
 
Link to Related Work
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 192 Abstract Views, 4 File Downloads  -  Detailed Statistics
Created: Thu, 03 Oct 2013, 11:13:29 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.