A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization

作者：Wu Qi^*

来源：Expert Systems with Applications, 2010, 37(3): 2388-2394.

DOI：10.1016/j.eswa.2009.07.057

• Support vector machine
• Particle swarm optimization
• Embedded
• Chaotic mapping
• Load forecasting

Load forecasting is an important subject for power distribution systems and has been studied from different points of view. This paper aims at the Gaussian noise parts of load series the standard nu-support vector regression machine with epsilon-insensitive loss function that cannot deal with it effectively. The relation between Gaussian noises and loss function is built up. On this basis, a new nu-support vector machine (nu-SVM) with the Gaussian loss function technique named by g-SVM is proposed. To seek the optimal unknown parameters of g-SVM, a chaotic particle swarm optimization is also proposed. And then, a hybrid-load-forecasting model based on g-SVM and embedded chaotic particle swarm optimization (ECPSO) is put forward. The results of application of load forecasting indicate that the hybrid model is effective and feasible.

• 出版日期2010-3-15
• 单位东南大学