%0 Journal Article %A Zhiyong Li and Zhigang Chen and Chao Fu and Shipeng Zhang %D 2010 %J International Journal of Energy and Power Engineering %B World Academy of Science, Engineering and Technology %I Open Science Index 47, 2010 %T Annual Power Load Forecasting Using Support Vector Regression Machines: A Study on Guangdong Province of China 1985-2008 %U https://publications.waset.org/pdf/516 %V 47 %X Load forecasting has always been the essential part of an efficient power system operation and planning. A novel approach based on support vector machines is proposed in this paper for annual power load forecasting. Different kernel functions are selected to construct a combinatorial algorithm. The performance of the new model is evaluated with a real-world dataset, and compared with two neural networks and some traditional forecasting techniques. The results show that the proposed method exhibits superior performance. %P 1670 - 1673