Short time load forecasting based on simulated annealing and genetic algorithm improved SVM

A support vector machines method (SVM) is presented for the hourly load forecasting of the coming days. In this approach, improved SVM based on simulated annealing algorithm and genetic algorithm. The new approach is used for the next day load forecast. These two methods are combined to find the improved parameters for Support Vector Machine. It proves that the combined method is useful in improve the SVM method. The load forecast results of this method are compared with the current neural network load forecast program (a conventional time series package). Both programs are utilized to predict the hourly load of one day ahead. Based on simulation results, the improved SVM approach provides a better performance than the neural network and the regular SVM algorithm.