Application of LS-SVM in the Short-term Power Load Forecasting Based on QPSO
Xiaohui Liao, Qian Ding
Available Online May 2014.
- https://doi.org/10.2991/lemcs-14.2014.52How to use a DOI?
- load forecasting;least square support vector machine(LS-SVM);quantum-behaved particle swarm optimization(QPSO);weather factor; date factor
- 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.
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 -