中国天然气消费结构的模糊地域聚类
The Fuzzy Regional Clusters of China’s Natural Gas Consumption Structure
作者: 周仲兵 ：长江大学管理学院，湖北 荆州; 尹海彤 ：中国中元国际工程有限公司，北京;
关键词: 天然气； 消费结构； 模糊聚类； 传递闭包； 决策； 预测； Natural Gas； Consumption Structure； Fuzzy Clustering； Transitive Closure； Decision； Forecast
摘要:Abstract: In order to enhance the efficiency of natural gas policy, optimize the forecast of natural gas con-sumption and improve the plan of natural gas pipeline construction, an fuzzy clustering based on the fuzzy equivalence relation’s transitive closure is applied to the natural gas consumption structures of 30 provinces (excluding Tibet) of Mainland China in 2013. The results show that, among those provinces: 1) at the accuracy level of 0.9, there are 22 clusters, with Hebei and Hei-longjiang being one cluster, Shanxi and Hubei being another, Jiangxi, Guangdong, Guizhou and Shannxi being a third and each of the rest along being a single cluster; 2) at the accuracy level of 0.8, there are 12 clusters, with Beijing, Tianjin, Inner Mongolia, Shanghai, Fujian, Henan, Hainan, Chongqing, Qinghai and Xinjiang each being a single cluster, Jiangsu and Shandong being one, and all others being another; 3) at the accuracy level of 0.7, there are 4 clusters, with Chongqing along being a single, Beijing and Shanghai being one, Fujian and Henan being another, and all others be-ing the fourth; 4) at the accuracy level of 0.6, there are 2 clusters, with Chongqing along being a single, and all others being the other; and 5) at the accuracy level of 0.5, there is only 1 cluster. Three points can be concluded from the results. First, no evidence of regional characteristic has been found associated with the structures of natural gas consumption in those provinces. Second, it turns out to be rather difficult for Chongqing, Beijing, Shanghai, Fujian as well as Henan to be classified in one cluster, while Hubei & Shanxi and Jiangxi & Guangdong & Guizhou & Yunnan & Shannxi belong to one cluster rather significantly. And third, Chongqing appears the most unique. The in depth logics of those phenomena are worth further investigation.
文章引用: 周仲兵 , 尹海彤 (2016) 中国天然气消费结构的模糊地域聚类。 电力与能源进展， 4， 160-167. doi: 10.12677/AEPE.2016.45021
参考文献
[1]
Wang, T. and Lin, B.Q. (2014) China’s Natural Gas Consumption and Subsidies—From a Sector Perspective. Energy Policy, 65, 541-551.
http://dx.doi.org/10.1016/j.enpol.2013.10.065
[2]
Xu, G. and Wang, W.G. (2010) Forecasting China’s Natural Gas Consumption Based on a Combination Model. Journal of Natural Gas Chemistry, 19, 493-496.
http://dx.doi.org/10.1016/S1003-9953(09)60100-6
[3]
Li, J.C., Dong, X.C., Shangguan, J.X. and Hook, M. (2011) Forecasting the Growth of China’s Natural Gas Consumption. Energy, 36, 1380-1385.
http://dx.doi.org/10.1016/j.energy.2011.01.003
[4] 苏欣, 段康, 袁宗明, 张琳, 唐恂, 张鹏. 城市天然气负荷特点及其预测研究[J]. 油气储运, 2007, 26(1): 5-9.
[5] 何清. 模糊聚类分析理论与应用研究进展[J]. 模糊系统与数学, 1998, 12(2): 89-94.
[6] 余锦华, 汪源源, 施心陵. 基于空间邻域信息的二维模糊聚类图像分割[J]. 光电工程, 2007, 34(4): 114-119.
[7] 林开颜, 徐立鸿, 吴军辉. 快速模糊C均值聚类彩色图像分割方法[J]. 中国图象图形学报, 2004, 9(2): 159-163.
[8] 吴景社, 康绍忠, 王景雷, 黄修桥, 宋毅夫. 基于主成分分析和模糊聚类方法的全国节水灌溉分区研究[J]. 农业工程学报, 2004, 20(4): 64-68.
[9] 王志良, 邱林, 梁川, 付强. 基于模糊聚类分析的水资源分区研究[J]. 华北水利水电学院学报, 2001, 22(2): 7-10.
[10]
Yu, S., Wei, Y.-M., Fan, J., Zhang, X. and Wang, K. (2012) Exploring the Regional Characteristics of Inter-Provincial CO2 Emissions in China: An Improved Fuzzy Clustering Analysis Based on Particle Swarm Optimization. Applied Energy, 92, 552-562.
http://dx.doi.org/10.1016/j.apenergy.2011.11.068
[11] 冯国庆, 张烈辉, 沈勇伟, 陈忠强. 应用模糊聚类分析方法评价油藏质量[J]. 西南石油学院学报, 2004, 26(3): 33-36.
[12] 张美华, 陈宏. 几种模糊聚类法在环境质量综合评价中的应用[J]. 重庆环境科学, 1999, 21(3): 13-16.
[13] 李大全, 张鹏, 艾慕阳, 王玉彬, 陈利琼. 模糊聚类法在油气管道风险评价管段划分中的应用[J]. 天然气工业, 2012, 32(7): 63-67.
[14] 王文森. 产业结构相似系数在统计分析中的应用[J]. 中国统计, 2007(10): 47-48.
[15]
Wang, Z., Bovik, A.C., Sheikh, H.R. and Simoncelli, E.P. (2004) Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13, 600-612.
http://dx.doi.org/10.1109/TIP.2003.819861