本文關(guān)鍵詞：我國城鎮(zhèn)公共建筑能耗預(yù)測及能效提升路徑研究　出處：《北京交通大學(xué)》2017年博士論文　論文類型：學(xué)位論文

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【摘要】：為了有效緩解我國經(jīng)濟(jì)社會發(fā)展與能源環(huán)境容量之間的矛盾,尋求可持續(xù)發(fā)展,國家能源消費強(qiáng)度和消費總量"雙控制"的新機(jī)制已漸上日程。公共建筑部門是影響整個建筑部門乃至國家層面能耗總量控制目標(biāo)實現(xiàn)的關(guān)鍵領(lǐng)域,準(zhǔn)確分析其能耗現(xiàn)狀并預(yù)測其增長趨勢、探索其能效提升路徑,對于指導(dǎo)公共建筑部門進(jìn)行能效提升、控制能耗增長、實施節(jié)能減排具有重要意義。本文以我國城鎮(zhèn)化快速發(fā)展階段為重要背景,以我國城鎮(zhèn)公共建筑為研究對象,綜合運用一系列定性與定量研究方法,對其宏觀運行能耗與能效問題進(jìn)行了深入研究。由于國家統(tǒng)計體系尚不完善,公共建筑的能耗總量與用能強(qiáng)度等基本現(xiàn)狀都不明確。因此,論文首先基于指標(biāo)法和統(tǒng)計年鑒數(shù)據(jù)拆分法系統(tǒng)分析了我國城鎮(zhèn)公共建筑宏觀運行能耗總量及整體能耗強(qiáng)度現(xiàn)狀。在之基礎(chǔ)上,綜合運用回歸分析法、趨勢外推法、系統(tǒng)校核法進(jìn)行了我國城鎮(zhèn)公共建筑能耗預(yù)測模型構(gòu)建,并預(yù)測了我國城鎮(zhèn)公共建筑能耗的增長趨勢。之后,通過愿景牽引法、機(jī)構(gòu)問卷調(diào)查法和改進(jìn)的德爾菲法,設(shè)計了我國城鎮(zhèn)公共建筑能效提升中長期路徑,并基于路徑參數(shù)構(gòu)建TAYLOR級數(shù)BP神經(jīng)網(wǎng)絡(luò)模型測算了節(jié)能量。最后,運用EFA和SEM等方法探究了我國城鎮(zhèn)公共建筑能效提升績效影響因素的內(nèi)部結(jié)構(gòu)和路徑分布,在之基礎(chǔ)上提出了我國城鎮(zhèn)公共建筑能效提升的保障措施建議。論文的創(chuàng)新之處主要體現(xiàn)在以下3個方面:(1)應(yīng)用曲線回歸分析法構(gòu)建了我國城鎮(zhèn)公共建筑能耗預(yù)測模型,并預(yù)測了我國城鎮(zhèn)公共建筑能耗的增長趨勢。在對我國城鎮(zhèn)公共建筑能耗總量和能耗強(qiáng)度現(xiàn)狀進(jìn)行系統(tǒng)測算的基礎(chǔ)上,通過理論分析構(gòu)建了我國城鎮(zhèn)公共建筑能耗預(yù)測理論模型,運用MATLAB和DATAFIT進(jìn)行曲線回歸模型方程組的建立和模型檢驗后,分別運用趨勢外推法和系統(tǒng)校核法進(jìn)行自變量的預(yù)測賦值和中介變量的校核判定,預(yù)測了我國城鎮(zhèn)公共建筑能耗2015～2030年的增長趨勢。(2)采用愿景牽引法設(shè)計了我國城鎮(zhèn)公共建筑能效提升路徑,并測算了路徑的節(jié)能量。采用愿景牽引法,通過機(jī)構(gòu)問卷調(diào)查和改進(jìn)的德爾菲法,設(shè)計了包括能效提升梯度和描述參數(shù)體系的我國城鎮(zhèn)公共建筑能效提升中長期(2016～2030年)路徑。并基于路徑參數(shù),構(gòu)建了 TAYLOR級數(shù)BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型,測算了路徑的節(jié)能量。(3)綜合運用EFA和SEM探究了我國城鎮(zhèn)公共建筑能效提升績效影響因素的內(nèi)部結(jié)構(gòu)和路徑分布。結(jié)合文獻(xiàn)研究與專家訪談識別了公共建筑能效提升績效的影響因素,構(gòu)建了影響因素清單;基于EFA和SEM方法,應(yīng)用SPSS和AMOS軟件工具,探究了影響因素的內(nèi)部結(jié)構(gòu)和路徑分布;在之基礎(chǔ)上提出了我國城鎮(zhèn)公共建筑能效提升的保障措施建議。
[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.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：F206
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本文編號：1343463