Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

Application of LS-SVM in the Short-term Power Load Forecasting Based on QPSO

Authors
Xiaohui Liao, Qian Ding
Corresponding Author
Xiaohui Liao
Available Online May 2014.
DOI
https://doi.org/10.2991/lemcs-14.2014.52How to use a DOI?
Keywords
load forecasting;least square support vector machine(LS-SVM);quantum-behaved particle swarm optimization(QPSO);weather factor; date factor
Abstract
Electricity power load forecasting is the basis of power system planning and construction. In order to improve the precision, this paper proposes a model, in which the parameters in least square support vector machine (LS-SVM) are optimized by Quantum-behaved Particle Swarm Optimization (QPSO) and considering the weather and date factors. In the quantum space, particles can be search in the whole feasible solution space. We can obtain the global optimal solution. Therefore, QPSO algorithm is a global guaranteed algorithm, which is better than the original PSO algorithm in search capability. The simulation results show that the adaptive particle swarm optimization-based SVM load forecasting model is more accurate than the neural networks model and traditional LS-SVM model.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2014)
Part of series
Advances in Intelligent Systems Research
Publication Date
May 2014
ISBN
978-94-6252-010-3
ISSN
1951-6851
DOI
https://doi.org/10.2991/lemcs-14.2014.52How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiaohui Liao
AU  - Qian Ding
PY  - 2014/05
DA  - 2014/05
TI  - Application of LS-SVM in the Short-term Power Load Forecasting Based on QPSO
BT  - International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2014)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-14.2014.52
DO  - https://doi.org/10.2991/lemcs-14.2014.52
ID  - Liao2014/05
ER  -