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

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 v-support vector regression machine with ε-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 v-support vector machine (v-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.