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Study On Simulation And Control Of Household Electricity Consumption In Western Chinese Cities Based On Family Size

Posted on:2019-09-28Degree:MasterType:Thesis
Country:ChinaCandidate:Y WangFull Text:PDF
GTID:2382330566981422Subject:Technical Economics and Management
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Energy is the basic driving force for social development.With the steady improvement of the global economic level and the increase in the population.the ever-increasing energy consumption has caused a crisis of energy shortage and huge environmental pollution,destroyed the ecological balance and the sustainable development of the global economy.China,as a big energy consumer,faces the dual pressure of energy supply and demand.With the acceleration of urbanization in China,urban housing has gradually become the key to energy conservation and emission reduction.The energy consumption in the residential sector is the main component of urban energy consumption,in which electricity is the main form of energy consumption.Because of its resource endowments,the backwardness of the economic development stage and the irrationality of the industrial structure,the power consumption of the western urban households has obvious regional characteristics.Therefore,it is significant to study the characteristics of the consumption characteristics of the western urban households,the influence factors and the changing laws of the power consumption.This study takes the urban household power consumption in the western region as the main research object.Based on the data from the 2014 China Family Panel Studies(CFPS)of Chinese Institute of Social Research of Peking University,analyze the monthly average power consumption characteristics of western urban households,its main influencing factors,the impact patterns and its marginal changes.The results found that:(1)Chinese Western urban household electricity consumption was at a relatively low level,the carbon emissions generated by electricity consumption was significantly lower than the eastern region.(2)The family household size,familyincome,net assets,living area and annual average temperature were the most significant influential factors for household electricity consumption in western cities.The family size was the main influencing factor,and the explanation for the total variance is 31.09%,which was significantly higher than other factors.We also found that the elasticity function of family power consumption on family size is nonlinear.(3)Grouping sample families according to family size,and found that with the different family sizes,the statistical distribution model of household power consumption and its parameter values were also The statistical distribution models for different families of 1-6 people are: Gamma and Log-logistic,Burr and Stable,Stable,Stable,Burr and Log-Logistics,Stable.According to the statistical model distribution function,the expected monthly average electricity consumption corresponding to each household size is obtained.It was found that the household electricity consumption changes as the family size changes to a smaller type,and the per capita electricity consumption showed a decreasing trend;in addition,according to statistics the inverse function of the cumulative distribution function(ICDF)of the distribution model can provide a theoretical reference for the development of the step power price.The power consumption characteristics of Chinese western household are clearly different from those in other parts of the country.Driven by policies such as “the Silk Road Economic Belt and the 21st-Century Maritime Silk Road” and Western Development,the process of urbanization and economic development in the western region will surely advance at a high speed,as well the level of energy consumption will also rise substantially.The research ideas and methods in this paper may provide references for research on household energy consumption and provide theoretical basis for the government to implement household energy regulation.
Keywords/Search Tags:Urban household energy consumption, Family size, Influencing factors, Marginal effect, Statistical distribution model
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