dc.contributor.author | Xinxing, Pan (Starry) | |
dc.date.accessioned | 2013-12-27T07:26:15Z | |
dc.date.available | 2013-12-27T07:26:15Z | |
dc.date.issued | 2012-06-18 | |
dc.identifier.citation | p. 1278 | en_US |
dc.identifier.isbn | 978-967-5760-11-2 | |
dc.identifier.uri | http://dspace.unimap.edu.my/123456789/30896 | |
dc.description | The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Postgraduates Student Association Ireland (MyPSI), 18th - 19th June 2012 at Putra World Trade Center (PWTC), Kuala Lumpur, Malaysia. | en_US |
dc.description.abstract | Load forecasting plays a very important role in building out the smart grid, and attracts
the attention of not only the researchers and engineers, but also governments. The classical method
for load forecasting is to use artificial neural networks (ANN). Recently the use of support vector
machines (SVM) has emerged as a hot research topic for load forecasting. Based on the results from
the experiments, a comparison between different internal ANN algorithms as well as the
comparison between ANN itself and SVM is discussed, and the merits of each approach described.
Also, how much effect the factors like weather and type of day have for the load prediction is
analyzed. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Universiti Malaysia Perlis (UniMAP) | en_US |
dc.relation.ispartofseries | The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012); | |
dc.subject | Support Vector Machines (SVMs) | en_US |
dc.subject | Artificial Neural Networks (ANNs) | en_US |
dc.title | Comparison of using SVMs and ANNs for smart grid load forecasting | en_US |
dc.type | Other | en_US |