Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)

Short-term electricity load forecasting based on particle swarm algorithm and SVM

Authors
Li-ping Wang1, Jing-min Wang, Dan Zhao
1North China Electric Power University
Corresponding Author
Li-ping Wang
Available Online October 2007.
DOI
https://doi.org/10.2991/iske.2007.41How to use a DOI?
Keywords
Particle Swarm Algorithm, Support Vector Machines, Load Forecasting
Abstract

In electricity industry, accurate load forecasting plays a key role in assuring the stability of power network and society. By far, there are many methods and models proposed to enhance the accuracy of forecasting results. On the basis of analyzing the performance of particle swarm algorithm (PSA) and SVM, the paper proposed a new forecasting model which is proved to be able to enhance the accuracy, improve the convergence ability and reduce operation time by numerical experiment. The proposed model is expected to offer a valid alternative for application in the load forecasting field.

Copyright
© 2007, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
Series
Advances in Intelligent Systems Research
Publication Date
October 2007
ISBN
978-90-78677-04-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/iske.2007.41How to use a DOI?
Copyright
© 2007, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Li-ping Wang
AU  - Jing-min Wang
AU  - Dan Zhao
PY  - 2007/10
DA  - 2007/10
TI  - Short-term electricity load forecasting based on particle swarm algorithm and SVM
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 237
EP  - 243
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.41
DO  - https://doi.org/10.2991/iske.2007.41
ID  - Wang2007/10
ER  -