基于灰色理论的安徽省能源需求预测分析

发布时间：2018-01-03 14:09

本文关键词：基于灰色理论的安徽省能源需求预测分析　出处：《安徽工业大学》2016年硕士论文　论文类型：学位论文

【摘要】：自国家实施“中部崛起、皖江城市带建设”等惠及安徽省经济发展的政策以来,安徽省经济正在快速发展的这条道路上加速前进,与此同时到来的还有安徽省对能源消费需求的快速增长。能源作为保障安徽省经济和社会生活稳定、健康快速发展的核心基石之一,对安徽省的能源需求进行准确预测,可以为能源安全有效的供给提供保证。论文运用灰色关联分析模型对安徽省能源需求影响因素进行分析;运用GM(1,1)模型预测安徽省的能源消费需求,同时利用马尔科夫过程模型对预测结果进行预估修正。具体取得如下几点进步:(1)系统地分析安徽省的能源供给、消费结构及消费状况。(2)运用灰色关联分析模型分析安徽省的能源需求影响因素。在综合分析后得出:在众多的影响因素中,第三产业产值对安徽省的能源需求影响程度最大,居民收入影响次之,生产总值GDP影响最小;就安徽省的产业结构,第三产业的能源消费需求对安徽省全省能源消费需求的影响最大,生活消费次之,第二产业影响最小。(3)运用GM(1,1)模型和马尔科夫过程对安徽省的能源需求进行预测。通过预测分析,发现在GM(1,1)预测的基础上运用马尔科夫过程进行修正,提高了预测的准确性;并运用此方法对2015-2019年安徽省的能源需求总量、煤炭、石油以及电力的日均需求量进行预测。(4)对安徽省的城市能源供给与需求进行比较分析。选取合肥与马鞍山市的能源消费数据,对两市的能源供给与需求进行分析;其次,从能源消费弹性系数、规模工业企业能源消费以及电力消费三个数据的角度出发,对合肥、马鞍山两市的能源供给与消费需求进行简单的定性分析,得出两市在能源供给与需求上是存在差异的。最后,定量分析两市的第一、第二、第三产业以及居民生活消费的能源量与能源消费总量间的关联度,得出:仅从能源消费关联度的结果上看两市的产业结构,合肥市的第二、第三产业的发展较为均衡,仍以第二产业为主;马鞍山市过度依赖第二产业,第三产业没有得到相应的发展。
[Abstract]:Since the implementation of the national policy of "the rise of the central region and the construction of the urban belt of Anhui River" and other policies that benefit the economic development of Anhui Province, the economy of Anhui Province has been speeding up along this road of rapid development. At the same time, there is also the rapid growth of energy consumption demand in Anhui Province. Energy as a guarantee of economic and social life in Anhui Province, healthy and rapid development of one of the core cornerstones. The accurate prediction of the energy demand in Anhui Province can guarantee the effective supply of energy security. The paper analyzes the influencing factors of the energy demand in Anhui Province by using the grey relational analysis model. The energy consumption demand of Anhui Province is predicted by GM1 / 1) model. At the same time, the Markov process model is used to revise the prediction results. The specific progress is as follows: 1) the energy supply in Anhui Province is analyzed systematically. Consumption structure and consumption status. 2) using the grey correlation analysis model to analyze the impact factors of energy demand in Anhui Province. After comprehensive analysis, it is concluded that: among the many influencing factors. The third industry's output value has the greatest influence on the energy demand of Anhui Province, the second is the resident income, and the least is the GDP. With regard to the industrial structure of Anhui Province, the energy consumption demand of the tertiary industry has the greatest impact on the energy consumption demand in Anhui Province, followed by the living consumption, and the second industry has the least impact on the energy consumption demand. 1) the model and Markov process are used to forecast the energy demand in Anhui Province. The accuracy of prediction is improved; This method is applied to the total energy demand of Anhui province from 2015 to 2019, coal. The daily average demand of oil and electricity is forecasted. 4) the energy supply and demand of Anhui Province are compared and analyzed. The energy consumption data of Hefei and Ma'anshan are selected. The energy supply and demand of the two cities are analyzed. Secondly, from the perspective of energy consumption elasticity coefficient, energy consumption of large-scale industrial enterprises and electricity consumption, this paper makes a simple qualitative analysis of the energy supply and consumption demand in Hefei and Ma'anshan. Finally, quantitative analysis of the two cities of the first, second, tertiary industry and residents living consumption of energy consumption and total energy consumption correlation degree. It is concluded that the industrial structure of the two cities is only seen from the results of the correlation degree of energy consumption. The development of the second and tertiary industries in Hefei is more balanced, and the secondary industry is still the main industry. Ma'anshan city relies on the second industry excessively, the tertiary industry does not get the corresponding development.
【学位授予单位】：安徽工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：F426.2

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