%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