城市居住建筑能耗影响因素与预测模型构建研究
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摘要
建筑与工业、交通并列为能源消耗的三大领域，也是温室气体排放的重要来源。随着我国国民经济的发展，导致建筑能耗所占能源消耗的比重逐年提升，近年来人们物质生活的改善和对于生活品质的追求，居住建筑能耗的增长十分显著，其节能减排的空间更为广阔。因此开展居住建筑的能耗调研、研究居住建筑能耗的影响因素，并据此建立居住建筑的能耗预测模型，预测未来的发展趋势，并未雨绸缪制定相应能源规划和发展战略以及节能减排的政策措施就显得十分紧迫。
但是居住建筑因为其鲜明的个体性，影响居住建筑能耗的因素不但与建筑本身有关，更与居住的人的行为、意识、经济社会发展和电器使用密切相关。因此其影响因素复杂、建立科学合理的预测模型预测难度较大。
本研究首先综述了国内外关于建筑节能和建筑能耗影响因素的研究，在文献研究的基础上，采用问卷调查法、开放式访谈法和德尔菲法编制调研问卷。问卷涉及影响城市居住建筑能耗的人口特征、建筑特征、生活方式、节能意识、设备特征等5个方面29项指标；问卷经过实测分析，进行信度、信度和半结构分析，最终筛选出独立性强的有效题目，最后再经过专家分析讨论，形成了信度和效度较好的正式调研问卷。组织人员以典型抽样的方式对重庆市主城区的1500户家庭进行了抽样调查，剔除无效样本后得到的632户有效样本。同时，从供电部门采集到被调查住户一一匹配的实际电耗数据。在客观数据与主观问卷两两匹配的基础上，调查了影响居住建筑能耗的29个可能的影响因素，数据处理采用社会科学统计软件SPSS17.0处理。
调研表明重庆市居住建筑的用能特点与现状为：住户80%为2—4口人，80%的家庭收入在4—8万元，60%的家庭居住面积在50—90m~2，建筑年代基本集中在2000年以后；户均拥有的空调、电脑、电视等生活必需的家用电器在一台以上，夏季降温以空调为主，冬季降温以取暖器为主，节能意识总体较好。重庆市居住建筑户均单位面积能耗26.4kWh/m2，总体而言老建筑比新建筑能耗要略微高一些，中间层的住宅的平均总能耗要低于位于建筑底层和顶层的住宅的平均总能耗，夏季开启空调的行为方式对于能耗的影响较大，夏季使用空调的数量对于能耗的影响十分显著。总体而言，夏季重庆室内居住舒适评价为比较凉爽的38%，60%的住户认为比较热和基本可以接受；冬季认为暖和的只有23%，77%的住户认为冬季的室内温度很冷和基本可以接受，说明重庆居住环境的室内环境并不乐观，居住品质并不理想。
在调研的基础上,对重庆市居住建筑能耗进行相关分析和偏相关分析,从29个影响年能耗的调查变量中筛选出了具有显著线性关系的常住人口、建筑面积、建筑类型、夏季空调降温方式、夏季制冷空调台数、冬季采暖空调台数、冬季采暖其它设备台数、电脑台数、电视台数、电磁炉台数、其它信息类设备台数、电视每日开机时间等12个变量。根据基础调查数据初选简单相关变量和派生变量与年能耗的简单线性关系确定出影响年能耗的最终简单相关变量6个,包括：常住人口、人均建筑面积、建筑类型、夏季空调降温方式、制冷空调和电脑台数和、空调电脑电视日均使用时间和。由于简单相关分析有一定的局限性,主要是在计算相关系数时,不能消除其它变量的影响,因此有必要对年能耗简单相关变量进行偏相关分析。最后,偏相关分析结果表明,与年能耗有显著相关因素有4个,包括：常住人口、人均建筑面积、制冷空调和电脑台数和、夏季空调降温方式。年能耗与各变量的偏相关系数从大到小依次为：常住人口(0.307)、人均建筑面积(0.290)、制冷空调和电脑台数和(0.125)、夏季空调降温方式(0.124)。
在分析和筛选出上述四个影响因素的基础上,建立了基于相关分析的多元线性回归预测模型：Y=-817.445+380.434X1+30.699X2+87.376X3+226.667X4。该模型与传统的线性多元回归分析建模不同,没有把可能影响建筑能耗的所有因素变量纳入模型,而是在通过相关分析和偏相关分析后剔除模型冗余变量,消除了共线性因素变量的影响,从而能在模型中更好反映建筑能耗的重要影响因素及影响程度,预测更为可靠和准确。为了与线性回归建模进行比较和检验,同时,选择了拟合精度更高、但拟合计算更为复杂的非线性回归分析建立模型。本文采用非线性最优化算法L—M(Levenberh—Marquardt)算法进行极值计算,进行了多参数非线性模拟多元回归分析,确定重庆市居住建筑能耗的非线性函数的拟合回归模型,多项式函数模型y=-10468.60+7148.6x10.14+1556.7x20.27+96x31.48+6.48x4215和指数函数模型
y=3706.15-3471.9e-0.32x1-3181.5e-0.028x2+509.4e0238x3+192.76e0153x4。最后利用2008-2009年重庆市城市实际居住建筑能耗数据对建立的线性和非线性回归模型拟合效果进行检验,模型拟合数据与实际统计数据符合度为94%以上,模型拟合度较好。
根据重庆市居住建筑能耗影响因素模型,结合重庆经济、社会、人口发展发展趋势,对重庆市“十二五”期间各能耗影响因素进行了情景分析,建立了重庆市居住建筑情景分布预测矩阵：E=∑E退休住户+∑E年轻住户+∑E基本家庭住户+∑E复合家庭住户=(c1j)*(bi1)T+(c2j)*(bi2)T+(c3j)*(bi3)T+(c4j)*(bi4)T(i,j=1,2,3)。根据该情景分析预测，重庆城市居住建筑能耗预测结果重庆市2012年总能耗为414527.7万kWh，2015年为507214.7万kWh。
结合重庆市的实际情况，以及居民的可接受程度，针对性提出了制定可持续的市能源发展规划、建立城市居住建筑能耗管理体系、完善基于低碳的居民用能引导措施和健全基于低碳的居住建筑节能激励机制，为重庆市解决能源矛盾和建筑节能提供了系列对策措施和政策建议。
Construction, tied with the industrial and transportation for three areas of energyconsumption, is also an important source of greenhouse gas emissions. With thedevelopment of China's national economy in recent years, it results to the proportion ofbuilding energy consumption share of energy consumption increase year by year. Theimprovement of people's material life and the pursuit of quality of life, leads to thegrowth of energy consumption of residential building significantly, which provide muchwider energy saving space. Therefore, carrying out the investigation of the energyconsumption of residential buildings, researching the influencing factors for residentialbuildings energy consumption, and establishing the forecasting model for residentialbuilding energy consumption accordingly to predict the future development trends, andplan ahead to develop the appropriate energy planning and development strategies andenergy saving policy measures is extremely urgent.
But because of residential building’s lively individuality, factors affecting theenergy consumption of residential buildings relate not only with architecture itself, butalso more closely relate to the behavior of people’s living and awareness, developmentof economic and social, and the use of electrical appliances. Owing to its impact factors,it is difficult to establish a scientific and rational prediction model.
This study reviews the research on the factors of the impact of building energyefficiency and building energy consumption at home and abroad firstly. On the basis ofthe literature study, questionnaire, open interviews and the Delphi method were used toprepare the survey questionnaire. The questionnaire involve five aspects which arearchitectural features, lifestyle, awareness of energy conservation, equipmentcharacteristics, up to29indicators having impacts on the urban residential buildingenergy consumption. After measuring the questionnaire through the analysis ofreliability, reliability, and semi-structured analysis, the final screening effectiveindependent subject was selected. Finally it was analyzed and discussed by experts toform the formal survey questionnaire with reliability and validity. Organization staffsampling typically1500families of Chongqing city for sampling survey,632validsamples from families was gotten after eliminating invalid samples. At the same time,the actual consumption data matched with investigated households was collected fromthe power supply departments. Based on comparing both objective data and subjective questionnaire, the research do the investigation of29factor which may have influenceof residential building energy consumption, and use social science statistical softwareSPSS17.0for data processing.
Research shows that the characteristics and present situation energy consumptionof residential building of Chongqing is that,80%of the residents are2-4people,80%of household income between4000and80000Yuan,60%of households’ area between50and90m2, and the age of the buildings basic focus on being late than2000. Thenecessaries living appliances every household has such as air conditioning, computer,TV and so on is more than1. Air conditioning gives priority to cooling in summer, andheater gives priority to a higher temperature in winter, the overall energy savingconsciousness is good. Per unit m~2area of Chongqing residential building’s energyconsumption is26.4kWh. On the whole, the old building’s energy consumption shouldbe a little higher than the new ones’, while the average total energy consumption of themiddle residence is lower than in the bottom’s average total energy consumption andtop’s, the behavior of open air conditioning in summer having bigger influence of theenergy consumption, and the number of using air conditioning for the influence of theenergy consumption in summer is very significant. Overall,38%of the household’sindoor comfortable living evaluation of Chongqing in summer is cool, and60%ofhouseholds think it is hot and can be accepted basically. Only23%of households thinkit is warm in winter, and77%of households think indoor temperature in winter is verycold and can be accepted basically. That also shows the indoor living environment ofChongqing is not optimistic, and the living quality is not ideal.
On the basis of investigation, do the related analysis and partial correlation analysisof the energy consumption of the residential building. From29variables of the affectenergy consumption in the investigation,12variable having significant linearrelationship were selected, which are the resident population, the construction area,building types, summer air conditioning cooling mode, summer refrigeration and airconditioning sets, winter heating air conditioning sets, heating in winter otherequipment sets, computers, television sets, the number of induction cooker sets, otherinformation equipment sets, using time of TV every day. According to the basic surveydata primary related variables and simple derived variables between years of simplelinear relationship energy consumption to determine6final simple relevant variableseffects the yearly energy consumption,6final simple relevant variables including theresident population, per capita consumption of building area, building type, the summer air-conditioning cooling method, number of refrigeration and air conditioning and computer tables, air conditioning, the total number of air-conditioning, refrigeration and the computer, the average daily use time of air-conditioning, refrigeration and the computer. Due to simple correlation analysis has certain limitation, mainly when calculating correlation coefficient it cannot eliminate the influence of other variables. Therefore, it is necessary to do partial correlation analysis of simple related variables of energy consumption. After excluding irrelevant variable, four factors with significant linear correlation was gotten finally, and they are the resident population, and per capita floor space, the total number of air-conditioning, refrigeration and the computer, and the cooling air-conditioning way in summer. The partial correlation coefficient of the energy consumption and the variables is as follows sorting from big to little:the resident population (0.307), per capita building area (0.290), the total number of air-conditioning, refrigeration and the computer (0.125), and the cooling air-conditioning way in summer (0.124).
Based on the analysis and filter out the four factors above, multiple regression analysis prediction models
Y=-817.445+380.434X1+30.699X2+87.376X3+226.667X4was built. Different from the traditional multiple regression analysis modeling building energy consumption, the model do not bring into all factors that may affect building energy consumption variables, but excluding redundant variables of the model after correlation analysis and partial correlation analysis, which eliminates a total of line factors variables in the model so that it can better reflect the important influence factors and the impact of building energy consumption, making the model more reliable and accurate prediction. In order to compare and inspection with linear regression model, meanwhile, the higher fitting precision nonlinear regression model but with more complex calculation also be chosen. This article use the L—M(Levenberg—Marquardt) model to identify the parameter, and do the multiple regression analysis, then ascertain Index nonlinear function fitting regression model of residential building energy consumption of Chongqing, polynomial function model
y=-10468.60+7148.6x10.14+1556.7x20.27+96x21.48+6.48x42.15and index function model y=3706.15-3471.9e-0.32X1-3181.5e-0.028x2+509.4e0.238x3+192.76e0.153x4Finally, comparing2008-2009Chongqing cities actual residential building energy consumption data with the data forecasted by using the linear and nonlinear regression model, resultshows that model fitting data and practical statistical data conformity degree is above94%, and means model fitting degree is better.
After obtaining the residential building energy consumption factor model ofChongqing, combined with the economic, social, demographic situation anddevelopment trend of Chongqing to conduct scenario analysis on the base of energyconsumption influencing factors in the "12th Five-Year" period,(including the numberof urban households; the structure of urban households; value distribution of factorsaffecting energy consumption various types of households, etc.). According to theresults of analysis of Chongqing urban residents established distribution matrix and theenergy consumption factors distribution matrix, energy consumption was predicted. Thetotal energy consumption forecasting results of Chongqing urban residential building is414527.7kWh in2012and507214.7kWh in2015.
Combined with the actual situation of Chongqing city and the residents’ acceptabledegree, authors put forward the sustainable development of the city for energy plan,establishing city residential building energy consumption management system, perfectthe residents energy consumption of low carbon based on guide measures and improvethe residential building based on low carbon energy saving excitation mechanism, andprovide a series of building energy efficiency measures and suggestions to solve theenergy contradiction of Chongqing.
但是居住建筑因为其鲜明的个体性，影响居住建筑能耗的因素不但与建筑本身有关，更与居住的人的行为、意识、经济社会发展和电器使用密切相关。因此其影响因素复杂、建立科学合理的预测模型预测难度较大。
本研究首先综述了国内外关于建筑节能和建筑能耗影响因素的研究，在文献研究的基础上，采用问卷调查法、开放式访谈法和德尔菲法编制调研问卷。问卷涉及影响城市居住建筑能耗的人口特征、建筑特征、生活方式、节能意识、设备特征等5个方面29项指标；问卷经过实测分析，进行信度、信度和半结构分析，最终筛选出独立性强的有效题目，最后再经过专家分析讨论，形成了信度和效度较好的正式调研问卷。组织人员以典型抽样的方式对重庆市主城区的1500户家庭进行了抽样调查，剔除无效样本后得到的632户有效样本。同时，从供电部门采集到被调查住户一一匹配的实际电耗数据。在客观数据与主观问卷两两匹配的基础上，调查了影响居住建筑能耗的29个可能的影响因素，数据处理采用社会科学统计软件SPSS17.0处理。
调研表明重庆市居住建筑的用能特点与现状为：住户80%为2—4口人，80%的家庭收入在4—8万元，60%的家庭居住面积在50—90m~2，建筑年代基本集中在2000年以后；户均拥有的空调、电脑、电视等生活必需的家用电器在一台以上，夏季降温以空调为主，冬季降温以取暖器为主，节能意识总体较好。重庆市居住建筑户均单位面积能耗26.4kWh/m2，总体而言老建筑比新建筑能耗要略微高一些，中间层的住宅的平均总能耗要低于位于建筑底层和顶层的住宅的平均总能耗，夏季开启空调的行为方式对于能耗的影响较大，夏季使用空调的数量对于能耗的影响十分显著。总体而言，夏季重庆室内居住舒适评价为比较凉爽的38%，60%的住户认为比较热和基本可以接受；冬季认为暖和的只有23%，77%的住户认为冬季的室内温度很冷和基本可以接受，说明重庆居住环境的室内环境并不乐观，居住品质并不理想。
在调研的基础上,对重庆市居住建筑能耗进行相关分析和偏相关分析,从29个影响年能耗的调查变量中筛选出了具有显著线性关系的常住人口、建筑面积、建筑类型、夏季空调降温方式、夏季制冷空调台数、冬季采暖空调台数、冬季采暖其它设备台数、电脑台数、电视台数、电磁炉台数、其它信息类设备台数、电视每日开机时间等12个变量。根据基础调查数据初选简单相关变量和派生变量与年能耗的简单线性关系确定出影响年能耗的最终简单相关变量6个,包括：常住人口、人均建筑面积、建筑类型、夏季空调降温方式、制冷空调和电脑台数和、空调电脑电视日均使用时间和。由于简单相关分析有一定的局限性,主要是在计算相关系数时,不能消除其它变量的影响,因此有必要对年能耗简单相关变量进行偏相关分析。最后,偏相关分析结果表明,与年能耗有显著相关因素有4个,包括：常住人口、人均建筑面积、制冷空调和电脑台数和、夏季空调降温方式。年能耗与各变量的偏相关系数从大到小依次为：常住人口(0.307)、人均建筑面积(0.290)、制冷空调和电脑台数和(0.125)、夏季空调降温方式(0.124)。
在分析和筛选出上述四个影响因素的基础上,建立了基于相关分析的多元线性回归预测模型：Y=-817.445+380.434X1+30.699X2+87.376X3+226.667X4。该模型与传统的线性多元回归分析建模不同,没有把可能影响建筑能耗的所有因素变量纳入模型,而是在通过相关分析和偏相关分析后剔除模型冗余变量,消除了共线性因素变量的影响,从而能在模型中更好反映建筑能耗的重要影响因素及影响程度,预测更为可靠和准确。为了与线性回归建模进行比较和检验,同时,选择了拟合精度更高、但拟合计算更为复杂的非线性回归分析建立模型。本文采用非线性最优化算法L—M(Levenberh—Marquardt)算法进行极值计算,进行了多参数非线性模拟多元回归分析,确定重庆市居住建筑能耗的非线性函数的拟合回归模型,多项式函数模型y=-10468.60+7148.6x10.14+1556.7x20.27+96x31.48+6.48x4215和指数函数模型
y=3706.15-3471.9e-0.32x1-3181.5e-0.028x2+509.4e0238x3+192.76e0153x4。最后利用2008-2009年重庆市城市实际居住建筑能耗数据对建立的线性和非线性回归模型拟合效果进行检验,模型拟合数据与实际统计数据符合度为94%以上,模型拟合度较好。
根据重庆市居住建筑能耗影响因素模型,结合重庆经济、社会、人口发展发展趋势,对重庆市“十二五”期间各能耗影响因素进行了情景分析,建立了重庆市居住建筑情景分布预测矩阵：E=∑E退休住户+∑E年轻住户+∑E基本家庭住户+∑E复合家庭住户=(c1j)*(bi1)T+(c2j)*(bi2)T+(c3j)*(bi3)T+(c4j)*(bi4)T(i,j=1,2,3)。根据该情景分析预测，重庆城市居住建筑能耗预测结果重庆市2012年总能耗为414527.7万kWh，2015年为507214.7万kWh。
结合重庆市的实际情况，以及居民的可接受程度，针对性提出了制定可持续的市能源发展规划、建立城市居住建筑能耗管理体系、完善基于低碳的居民用能引导措施和健全基于低碳的居住建筑节能激励机制，为重庆市解决能源矛盾和建筑节能提供了系列对策措施和政策建议。
Construction, tied with the industrial and transportation for three areas of energyconsumption, is also an important source of greenhouse gas emissions. With thedevelopment of China's national economy in recent years, it results to the proportion ofbuilding energy consumption share of energy consumption increase year by year. Theimprovement of people's material life and the pursuit of quality of life, leads to thegrowth of energy consumption of residential building significantly, which provide muchwider energy saving space. Therefore, carrying out the investigation of the energyconsumption of residential buildings, researching the influencing factors for residentialbuildings energy consumption, and establishing the forecasting model for residentialbuilding energy consumption accordingly to predict the future development trends, andplan ahead to develop the appropriate energy planning and development strategies andenergy saving policy measures is extremely urgent.
But because of residential building’s lively individuality, factors affecting theenergy consumption of residential buildings relate not only with architecture itself, butalso more closely relate to the behavior of people’s living and awareness, developmentof economic and social, and the use of electrical appliances. Owing to its impact factors,it is difficult to establish a scientific and rational prediction model.
This study reviews the research on the factors of the impact of building energyefficiency and building energy consumption at home and abroad firstly. On the basis ofthe literature study, questionnaire, open interviews and the Delphi method were used toprepare the survey questionnaire. The questionnaire involve five aspects which arearchitectural features, lifestyle, awareness of energy conservation, equipmentcharacteristics, up to29indicators having impacts on the urban residential buildingenergy consumption. After measuring the questionnaire through the analysis ofreliability, reliability, and semi-structured analysis, the final screening effectiveindependent subject was selected. Finally it was analyzed and discussed by experts toform the formal survey questionnaire with reliability and validity. Organization staffsampling typically1500families of Chongqing city for sampling survey,632validsamples from families was gotten after eliminating invalid samples. At the same time,the actual consumption data matched with investigated households was collected fromthe power supply departments. Based on comparing both objective data and subjective questionnaire, the research do the investigation of29factor which may have influenceof residential building energy consumption, and use social science statistical softwareSPSS17.0for data processing.
Research shows that the characteristics and present situation energy consumptionof residential building of Chongqing is that,80%of the residents are2-4people,80%of household income between4000and80000Yuan,60%of households’ area between50and90m2, and the age of the buildings basic focus on being late than2000. Thenecessaries living appliances every household has such as air conditioning, computer,TV and so on is more than1. Air conditioning gives priority to cooling in summer, andheater gives priority to a higher temperature in winter, the overall energy savingconsciousness is good. Per unit m~2area of Chongqing residential building’s energyconsumption is26.4kWh. On the whole, the old building’s energy consumption shouldbe a little higher than the new ones’, while the average total energy consumption of themiddle residence is lower than in the bottom’s average total energy consumption andtop’s, the behavior of open air conditioning in summer having bigger influence of theenergy consumption, and the number of using air conditioning for the influence of theenergy consumption in summer is very significant. Overall,38%of the household’sindoor comfortable living evaluation of Chongqing in summer is cool, and60%ofhouseholds think it is hot and can be accepted basically. Only23%of households thinkit is warm in winter, and77%of households think indoor temperature in winter is verycold and can be accepted basically. That also shows the indoor living environment ofChongqing is not optimistic, and the living quality is not ideal.
On the basis of investigation, do the related analysis and partial correlation analysisof the energy consumption of the residential building. From29variables of the affectenergy consumption in the investigation,12variable having significant linearrelationship were selected, which are the resident population, the construction area,building types, summer air conditioning cooling mode, summer refrigeration and airconditioning sets, winter heating air conditioning sets, heating in winter otherequipment sets, computers, television sets, the number of induction cooker sets, otherinformation equipment sets, using time of TV every day. According to the basic surveydata primary related variables and simple derived variables between years of simplelinear relationship energy consumption to determine6final simple relevant variableseffects the yearly energy consumption,6final simple relevant variables including theresident population, per capita consumption of building area, building type, the summer air-conditioning cooling method, number of refrigeration and air conditioning and computer tables, air conditioning, the total number of air-conditioning, refrigeration and the computer, the average daily use time of air-conditioning, refrigeration and the computer. Due to simple correlation analysis has certain limitation, mainly when calculating correlation coefficient it cannot eliminate the influence of other variables. Therefore, it is necessary to do partial correlation analysis of simple related variables of energy consumption. After excluding irrelevant variable, four factors with significant linear correlation was gotten finally, and they are the resident population, and per capita floor space, the total number of air-conditioning, refrigeration and the computer, and the cooling air-conditioning way in summer. The partial correlation coefficient of the energy consumption and the variables is as follows sorting from big to little:the resident population (0.307), per capita building area (0.290), the total number of air-conditioning, refrigeration and the computer (0.125), and the cooling air-conditioning way in summer (0.124).
Based on the analysis and filter out the four factors above, multiple regression analysis prediction models
Y=-817.445+380.434X1+30.699X2+87.376X3+226.667X4was built. Different from the traditional multiple regression analysis modeling building energy consumption, the model do not bring into all factors that may affect building energy consumption variables, but excluding redundant variables of the model after correlation analysis and partial correlation analysis, which eliminates a total of line factors variables in the model so that it can better reflect the important influence factors and the impact of building energy consumption, making the model more reliable and accurate prediction. In order to compare and inspection with linear regression model, meanwhile, the higher fitting precision nonlinear regression model but with more complex calculation also be chosen. This article use the L—M(Levenberg—Marquardt) model to identify the parameter, and do the multiple regression analysis, then ascertain Index nonlinear function fitting regression model of residential building energy consumption of Chongqing, polynomial function model
y=-10468.60+7148.6x10.14+1556.7x20.27+96x21.48+6.48x42.15and index function model y=3706.15-3471.9e-0.32X1-3181.5e-0.028x2+509.4e0.238x3+192.76e0.153x4Finally, comparing2008-2009Chongqing cities actual residential building energy consumption data with the data forecasted by using the linear and nonlinear regression model, resultshows that model fitting data and practical statistical data conformity degree is above94%, and means model fitting degree is better.
After obtaining the residential building energy consumption factor model ofChongqing, combined with the economic, social, demographic situation anddevelopment trend of Chongqing to conduct scenario analysis on the base of energyconsumption influencing factors in the "12th Five-Year" period,(including the numberof urban households; the structure of urban households; value distribution of factorsaffecting energy consumption various types of households, etc.). According to theresults of analysis of Chongqing urban residents established distribution matrix and theenergy consumption factors distribution matrix, energy consumption was predicted. Thetotal energy consumption forecasting results of Chongqing urban residential building is414527.7kWh in2012and507214.7kWh in2015.
Combined with the actual situation of Chongqing city and the residents’ acceptabledegree, authors put forward the sustainable development of the city for energy plan,establishing city residential building energy consumption management system, perfectthe residents energy consumption of low carbon based on guide measures and improvethe residential building based on low carbon energy saving excitation mechanism, andprovide a series of building energy efficiency measures and suggestions to solve theenergy contradiction of Chongqing.
引文
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