我国城镇公共建筑能耗预测及能效提升路径研究

发布时间：2017-12-27 22:27

  本文关键词：我国城镇公共建筑能耗预测及能效提升路径研究　出处：《北京交通大学》2017年博士论文　论文类型：学位论文

  更多相关文章： 城镇公共建筑 能耗预测 能效提升路径 绩效影响因素 节能保障措施

【摘要】：为了有效缓解我国经济社会发展与能源环境容量之间的矛盾,寻求可持续发展,国家能源消费强度和消费总量"双控制"的新机制已渐上日程。公共建筑部门是影响整个建筑部门乃至国家层面能耗总量控制目标实现的关键领域,准确分析其能耗现状并预测其增长趋势、探索其能效提升路径,对于指导公共建筑部门进行能效提升、控制能耗增长、实施节能减排具有重要意义。本文以我国城镇化快速发展阶段为重要背景,以我国城镇公共建筑为研究对象,综合运用一系列定性与定量研究方法,对其宏观运行能耗与能效问题进行了深入研究。由于国家统计体系尚不完善,公共建筑的能耗总量与用能强度等基本现状都不明确。因此,论文首先基于指标法和统计年鉴数据拆分法系统分析了我国城镇公共建筑宏观运行能耗总量及整体能耗强度现状。在之基础上,综合运用回归分析法、趋势外推法、系统校核法进行了我国城镇公共建筑能耗预测模型构建,并预测了我国城镇公共建筑能耗的增长趋势。之后,通过愿景牵引法、机构问卷调查法和改进的德尔菲法,设计了我国城镇公共建筑能效提升中长期路径,并基于路径参数构建TAYLOR级数BP神经网络模型测算了节能量。最后,运用EFA和SEM等方法探究了我国城镇公共建筑能效提升绩效影响因素的内部结构和路径分布,在之基础上提出了我国城镇公共建筑能效提升的保障措施建议。论文的创新之处主要体现在以下3个方面:(1)应用曲线回归分析法构建了我国城镇公共建筑能耗预测模型,并预测了我国城镇公共建筑能耗的增长趋势。在对我国城镇公共建筑能耗总量和能耗强度现状进行系统测算的基础上,通过理论分析构建了我国城镇公共建筑能耗预测理论模型,运用MATLAB和DATAFIT进行曲线回归模型方程组的建立和模型检验后,分别运用趋势外推法和系统校核法进行自变量的预测赋值和中介变量的校核判定,预测了我国城镇公共建筑能耗2015～2030年的增长趋势。(2)采用愿景牵引法设计了我国城镇公共建筑能效提升路径,并测算了路径的节能量。采用愿景牵引法,通过机构问卷调查和改进的德尔菲法,设计了包括能效提升梯度和描述参数体系的我国城镇公共建筑能效提升中长期(2016～2030年)路径。并基于路径参数,构建了 TAYLOR级数BP神经网络预测模型,测算了路径的节能量。(3)综合运用EFA和SEM探究了我国城镇公共建筑能效提升绩效影响因素的内部结构和路径分布。结合文献研究与专家访谈识别了公共建筑能效提升绩效的影响因素,构建了影响因素清单;基于EFA和SEM方法,应用SPSS和AMOS软件工具,探究了影响因素的内部结构和路径分布;在之基础上提出了我国城镇公共建筑能效提升的保障措施建议。
[Abstract]:In order to effectively alleviate the contradiction between China's economic and social development and the capacity of energy and environment, and seek sustainable development, the new mechanism of "dual control" of national energy consumption intensity and total consumption has been on the agenda. Department of public buildings is affecting the entire construction sector in key areas to realize the goal of the national level and the total energy consumption control, accurate analysis of the current situation of the energy consumption and to explore the path of enhancing energy efficiency of its growth trend, forecast, to guide the public building department to improve energy efficiency, energy consumption control plays an important role in growth, the implementation of energy-saving emission reduction. Taking the rapid development stage of urbanization in China as an important background, this paper takes China's urban public buildings as the research object, and applies a series of qualitative and quantitative research methods to conduct in-depth research on its macro operation energy consumption and energy efficiency. Because the national statistical system is not perfect, the total energy consumption of public buildings and the basic status of energy use are not clear. Therefore, based on the index method and the statistical yearbook data splitting method, this paper systematically analyzes the total energy consumption and the overall energy consumption intensity of urban public buildings in China. On the basis of it, we use the regression analysis method, trend extrapolation method and system check method to build the prediction model of urban public building energy consumption in China, and predict the growth trend of urban public building energy consumption in China. Then, through the vision traction method, the institutional questionnaire survey and the improved Delphy method, the mid long term path of urban public building energy efficiency improvement is designed. Based on the path parameters, TAYLOR series BP neural network model is built to calculate the energy saving. Finally, we use EFA and SEM to explore the internal structure and path distribution of the influencing factors of urban public building energy efficiency performance in China. On the basis of that, we put forward some suggestions for improving the energy efficiency of urban public buildings in China. The innovation of the paper is mainly reflected in the following 3 aspects: (1) the prediction model of urban public building energy consumption is constructed by curvilinear regression analysis, and the growth trend of urban public building energy consumption in China is forecasted. Based on systematically estimates of China's urban public building energy consumption and energy intensity on the status quo, through theoretical analysis, the construction of public building energy consumption of China's urban forecast theory model, model and test by using MATLAB and DATAFIT regression model equations, determine the variables were used to check the check method of trend extrapolation method and system the prediction of assignment and the intermediary variables, predict the growth trend of China's urban public building energy consumption for 2015~2030 years. (2) the way of improving the energy efficiency of urban public buildings in China is designed by using the vision traction method, and the energy of the path is calculated. By adopting the method of vision traction, through the institutional questionnaire survey and the improved Delphy method, we have designed the medium and long term (2016~2030 years) path of energy efficiency enhancement of urban public buildings in China, including the gradient of energy efficiency and the description of parameter system. Based on the path parameters, the TAYLOR series BP neural network prediction model is constructed, and the node energy of the path is calculated. (3) comprehensive use of EFA and SEM to explore the internal structure and path distribution of the impact factors of energy efficiency improvement in urban public buildings in China. According to the interviews identified public building energy efficiency factors influencing the performance of literature study and experts, constructs the influence factors of the list; EFA and SEM based on the method of using SPSS and AMOS software tools, explores the influence of internal structure and path distribution factors; on the basis of our proposed measures to improve the energy efficiency of urban public buildings.
【学位授予单位】：北京交通大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：F206
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本文编号：1343463