基于SVM的中国地区进口LNG需求量预测

APPLICATION OF SVM IN PREDICTING LNG IMPORT DEMAND OF CHINA

随着我国能源结构转型不断清洁化,天然气在一次能源消费比重中持续上升。我国天然气液化和接收站项目不断上马,LNG在天然气消费市场占有比例持续上升。传统的项目分析中,使用固定天然气价格进行测算,不能很好反映消费市场变化。介绍了一个基于机器学习方法的动态预测模型,通过分析国内市场关键参数进行回归预测。在对过去10年的天然气进口量的分析中,利用该模型进行仿真预测其结果较好,对沿海地区LNG进口量的预测与实际进口量较吻合。

With the transformation of China's energy structure,the proportion of natural gas in primary energy consumption continues to rise.China's natural gas liquefaction and receiving station projects continue to launch,and the proportion of LNG in the natural gas consumer market keeps rising.In the traditional project analysis,the use of fixed natural gas prices for measurement does not reflect the changes in the consumer market.This paper provides a dynamic prediction model based on machine learning method.The regression prediction is carried out by analyzing the key parameters of the domestic market.In the analysis of natural gas imports in the past decade,the simulation prediction results are good,which can effectively predict the LNG imports in China's coastal areas.