建立储煤中心的KPCA-SVRM选址模型研究

Research on KPCA-SVRM Site Selection Model of Coal Storage Centre for Power Generation Coal Supply

通过分析目前的电煤供应现状,提出建立储煤中心来解决电煤供应瓶颈问题。运用KPCA-SVRM(基于核函数的主成分分析与支持向量回归机结合)模型进行储煤中心选址决策,综合考虑各种因素,把社会专业化分工的优越性充分发挥出来,使之在实现电力行业可持续发展的同时尽量节约能源和成本、注重效益,保持电力行业的长期、健康、协调发展。在KPCA-SVRM模型中,首先是用KPCA对影响储煤中心选址决策的各种因素进行主成分提取,然后将提取后的主成分作为SVRM的输入,通过学习和训练最终输出决策结果,最后用相关实例来说明此过程。

This paper was established in electricity lines, and put forward the viewpoint that we can build coal storage center to settle the problem of power generation coal supply. The coal storage centre site selection was made by KPCA （kernel principal component analysis） -SVRM （support vector regression machine）, taking all factors into account and making the advantage of the social division of labor specialization fully played. In KPCA-SVRM, the first step was to apply KPCA to SVRM for feature extraction. KPCA first maps the original input into a high dimensional feature space using the kernel method and then calculated PCA in the high dimensional feature space. These new features were used as the inputs of SVRM to solve the site selection problem. By learning and training, the data of this subset to get the solution and find interrelationship of input and output by the SVRM. Practical examples are cited in this paper to illustrate the process.