Electric load forecasting using support vector machines optimized by genetic algorithm

Electric load forecasting has become an important research area for secure operation and management of the modern power systems. In this paper we have proposed a seven support vector machines model for daily peak load demand long range forecasting. One support vector machine for each day of the week is trained on the past data and then used for the forecasting. In tuning process of support vector machines there are few parameters to optimize. We have used genetic algorithm for optimization of these parameters. The proposed model is evaluated on the electric load data used in EUNITE load competition in 2001 arranged by East-Slovakia Power Distribution Company. A better result is found as compare to best result found in the competition.