Short-term electricity load forecasting based on particle swarm algorithm and SVM
Li-ping Wang1, Jing-min Wang, Dan Zhao
1North China Electric Power University
Available Online October 2007.
- 10.2991/iske.2007.41How to use a DOI?
- Particle Swarm Algorithm, Support Vector Machines, Load Forecasting
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.
- © 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 - 10.2991/iske.2007.41 ID - Wang2007/10 ER -