文档介绍：重庆大学硕士学位论文 中文摘要
摘 要
本文研究了天然气消费需求量的中长期预测。首先简要介绍了天然气发展状
况和天然气预测的必要性；然后较为系统的阐述了预测理论、传统预测方法的分
类和传统预测方法向现代预测方法的转变；第三章、第四章和第五章分别仔细地
讨论了三种不同的天然气消费需求量中长期预测模型，即改进的 GM(1,1)预测模
型、遗传 BP 神经网络预测模型和参数优化的最小二乘支持向量机预测模型，在
此基础上分别得到了预测结果；接着本文讨论了组合预测的必要性，介绍了一种
组合预测算法，并得到最终预测结果。最后，文章总结了所作的工作和本文的不
足，并提出了以后继续研究的方向。
关键词：天然气消费需求量预测，GM(1,1)模型，三项 BP 神经网络，遗传算法，
最小二乘支持向量机
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重庆大学硕士学位论文 英文摘要
ABSTRACT
The natural gas mid- & long- term demand prediction is discussed in this
dissertation. This dissertation starts with the development of natural gas and the
indispensability of its demand prediction, and then it methodically introduces the
theories of prediction and the categories of traditional prediction methods in Section
Two. Moreover, the transition from the traditional ways to modern methods is also
articulated in this section. Three different models, the advanced GM(1,1) , the BP
Neural Networks (BPNN) optimized by Genetic Algorithms (GA) and the optimized
Least Square Support Vector Machines (LS-SVMs) are respectively discussed in
Section Three, Section Four and Section Five respectively, all of which could
effectively deal with the natural gas mid- & long- term demand forecasting. Next, in
order to use the data gained form diverse methods comprehensively, to prevent defects
of a single method that loses some useful information, to reduce the randomness and to
improve the prediction accuracy, the prediction with optimization combination is
applied in Section Six and the final prediction value of natural gas demand from
2005-2010 is obtained. In the last section, the conclusions are acquired and some
disadvantages are indicated. The future research aspect