@article{(Open Science Index):https://publications.waset.org/pdf/516,
	  title     = {Annual Power Load Forecasting Using Support Vector Regression Machines: A Study on Guangdong Province of China 1985-2008},
	  author    = {Zhiyong Li and  Zhigang Chen and  Chao Fu and  Shipeng Zhang},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Energy and Power Engineering},
	  volume    = {4},
	  number    = {11},
	  year      = {2010},
	  pages     = {1670 - 1673},
	  ee        = {https://publications.waset.org/pdf/516},
	  url   	= {https://publications.waset.org/vol/47},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 47, 2010},
	}