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

DOI

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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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  - 10.2991/iske.2007.41
ID  - Wang2007/10
ER  -