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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 (LSSVM) 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. Keywords-short-term load forecasting;least square support vector machine(LS-SVM);quantum-behaved particle swarm optimization(QPSO);weather factor; date factor

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