New Zealand Journal of Agricultural Research abstracts
Application of a hybrid model on short-term load forecasting based
on support vector machines (SVM)
Limin Ao
Department of Information Engineering
Northeast Dianli University
Jilin, Jilin 132012
China
aolm@163.com
Yongchun Wang
Changchun Heating Group Company Limited
Changchun, Jilin 130022
China
Qian Zhang
Baishan Electric Power Company
Baishan, Jilin 134300
China
Abstract To overcome the shortcoming of
single train set of
support vector machines (SVM), a novel hybrid model based on dual
support vector machines (DSVM) is presented in this paper. The first
SVM takes the recent samples in the vicinity of the demand day as its
train samples. It can capture the most recent dynamic changing load.
The second one takes the same season’s load samples in historic years
that have the similar attributes with the demand days as its train
samples to reflect the season-period rule. Final results can be
archived by converging both SVM. The raw dataset related to
experiments was obtained from the EUNITE network. The experiments have
proved that the accuracy of proposed model is better than traditional
one in general and showed this model’s feasibility in practical
application.
Keywords daily load curve; empirical risk
minimisation;
kernel function; separating hyperplane; support vector machines
A07063; Online publication date 4 December 2007; Received and accepted
10 August 2007
New Zealand Journal of Agricultural Research, 2007, Vol. 50:
567–572
0028–8233/07/5005–0567 © The Royal Society of New Zealand 2007
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