本文選題：公共建筑　切入點：能耗定額　出處：《重慶大學》2014年博士論文　論文類型：學位論文

【摘要】：全球氣候變化是人類迄今面臨的最重大環(huán)境問題，隨著科學發(fā)展觀在我國的深入貫徹，節(jié)約資源和保護環(huán)境成為我國的基本國策，如何加快轉變城鄉(xiāng)建設模式和建筑業(yè)發(fā)展方式，提高人民生活質量，成為一項亟待解決的問題。目前，建筑已經與工業(yè)、交通并列為能源消耗的三大領域，也是溫室氣體排放的重要來源。建筑領域能耗高、比重大，長期增長趨勢明顯，同時具備較大的節(jié)能潛力，減排成本相對較低。 為有效提高公共建筑能源利用效率，建立促進公共建筑節(jié)能的長效機制，中國政府建立了國家機關辦公建筑和大型公共建筑節(jié)能監(jiān)管體系。該政策體系的工作目標是逐步建立起全國聯(lián)網的國家機關辦公建筑和大型公共建筑能耗監(jiān)測平臺，對全國重點城市重點建筑能耗進行實時監(jiān)測，并通過能耗統(tǒng)計、能源審計、用能定額和超定額加價等制度，促使國家機關辦公建筑和大型公共建筑提高節(jié)能運行管理水平，培育建筑節(jié)能服務市場，為高能耗建筑的進一步節(jié)能改造準備條件。在“十一五”期間，全國共完成國家機關辦公建筑和大型公共建筑能耗統(tǒng)計33000棟，完成能源審計4850棟，公示了近6000棟建筑的能耗狀況，已對1500余棟建筑的能耗進行了動態(tài)監(jiān)測。同時，北京、天津、深圳、江蘇、重慶、內蒙古、上海、浙江、貴州等九個省市已開展能耗動態(tài)監(jiān)測平臺建設試點工作，部分省市也已出臺了試運行的公共建筑用能定額。用能定額是整個公共建筑節(jié)能監(jiān)管體系當中的一個重要環(huán)節(jié)，它為制定超定額加價和確定節(jié)能改造標準提供依據。但是，由于各地氣候、建筑用電特點以及經濟發(fā)展水平等差異，選取何種能耗定額分析方法存在較大爭議，致使能耗定額政策遲遲未能得到推廣。為了給住房和城鄉(xiāng)建設部制定用能定額和超定額加價政策提供參考，本文以重慶地區(qū)為例，通過調查研究重慶市公共建筑的能耗現狀和用能水平，從建筑運行階段的負荷率變化來進行分類，探索制定了一種較為科學合理的用能定額方法。 首先，通過調研隨機抽取重慶市公共建筑監(jiān)測平臺的207棟公共建筑的基本信息，以及它們在2012年全年各小時的用電數據。通過對用電數據進行整理，對主要存在的三種類型錯誤數據（突發(fā)錯誤、數據延遲和數據中斷）進行處理，最終篩選出145棟公共建筑進行詳細研究。通過對145棟公共建筑的總體用電現狀進行統(tǒng)計分析，發(fā)現不同使用功能建筑的用電水平差異很大，相同功能的公共建筑之間年能耗的差異也很大。再分別對重慶市的政府辦公建筑、商場建筑、一般辦公建筑、酒店建筑這四類公共建筑的用電分布進行統(tǒng)計分析，得到其用電水平、電耗特征和電耗分布情況，發(fā)現建筑電耗分布規(guī)律服從對數正態(tài)分布。 而后對不同功能公共建筑的月、周用電變化進行研究。通過對樣本建筑的周用電變化、月用電變化進行分析，獲得不同建筑的用電特征。研究發(fā)現，總能耗的波動主要是受到空調系統(tǒng)用電的影響，全年月能耗有兩個波峰和兩個波谷。由于不同功能建筑的運行時間不同，導致各類建筑逐時負荷率存在很大差異。負荷率變化導致了用電變化。不同功能建筑的日用電變化在一定程度上表征該類建筑的用電特點。論證了從建筑運行階段的負荷率變化出發(fā)建立公共建筑分類方法的合理性。 本文篩選了影響公共建筑用電的各主要因素，對影響因素與建筑能耗之間進行相關性分析和影響權重排序。分析發(fā)現，在α=0.01顯著性水平上，單位面積的照明插座能耗、單位面積空調能耗、單位面積動力能耗和單位面積特殊系統(tǒng)能耗與單位面積年能耗顯著性正相關；空調形式與單位面積空調能耗顯著性正相關。通過實際能耗監(jiān)測得到某一政府辦公樣本建筑2012年全年日用電能耗，從中國氣象局獲取2012年全年的氣象數據，再通過對該建筑進行能耗模擬得到典型年全年逐日用電能耗。采用一元線性回歸的方法，將典型年的日平均氣溫與2012年實測的日平均氣溫的差值設為自變量x，實際日用電能耗與模擬用電能耗的差值設為因變量y，得到室外日空氣溫度變化對建筑總能耗影響的一元線性回歸方程式為 0.6690.0055x和室外日空氣溫度變化對建筑空調能耗影響的一元線性回歸方程式為0.5820.00145x，且通過顯著性檢驗，發(fā)現在置信概率為0.95的水平上，y和x顯著相關。 基于上述分析，將公共建筑進行兩級分類。第一級按照公共建筑的使用功能進行分類，分為了政府辦公建筑、一般辦公建筑、商場建筑、酒店建筑、學校類建筑、醫(yī)院建筑的六類。在第一級分類的基礎上，運用層次聚類分析法進行二級分類，以日負荷率變化為標準將建筑分為ABC三類，A類日總負荷率最高，其次是B類，，C類日總負荷率最低。主要步驟是首先得到建筑在四季的典型日負荷率變化矩陣，再采用層次聚類方法，按照日總負荷率的高中低水平分為ABC三類。最后采用多項式擬合，得到每類建筑的典型部分負荷率曲線，得到擬合方程。并取顯著性水平0.05，對擬合曲線進行顯著性檢驗，且從R-Square都很接近于1，表示各擬合方程的擬合程度都較高。證明該分類方法科學有效。 并且運用層次聚類分析方法，對照明及插座系統(tǒng)和空調系統(tǒng)的用電使用分布特征曲線進行快速分類。根據聚類步驟之間系數變化率來判斷最佳聚類個數，從大量樣本建筑進行快速分類，且快速篩選并提取出用電使用率特征曲線。通過對分類結果的分析，發(fā)現該方法應用于對大量公共建筑日用電特征進行快速篩選是非常有效的。 而對于未納入公共建筑監(jiān)測平臺的建筑需要通過預測其日負荷率來進行二級分類判別。因此利用時間序列ARIMA模型建立建筑用電負荷率預測模型。建筑用電負荷率受到建筑使用者行為的影響，具有隨機性特點。而時間序列分析模型應用于對建筑用電負荷率的預測可將各種復雜因素的總和效應統(tǒng)一包含于時間序列之中。通過對建筑負荷率建立隨機過程模型，并通過使用重慶市69棟政府辦公建筑在2012年的用電負荷率數據進行時間序列模型的建立、識別和擬合，得到預測模型ARIMA (1,0,8)(2,1,1)，并對模型的適應性進行了驗證。為了進一步驗證得到的ARIMA (1,0,8)(2,1,1)的適用性，使用該模型預測兩棟政府辦公建筑的日用電使用率，發(fā)現預測效果與實際檢測到的結果差異不大，且實際值基本都落入置信區(qū)間之內。該方法也可以推廣使用到其他類型建筑的用電負荷率的預測。 最后，在探索公共建筑合理分類的基礎上，分別從統(tǒng)計定額和技術定額兩個方向制定重慶地區(qū)的公共建筑能耗定額。統(tǒng)計定額的服務對象是政府部門，為政府部門制定政策提供參考。技術定額主要服務對象是公共建筑管理人員以及技術人員，為下一步對建筑進行節(jié)能改造或節(jié)能運行提供參考。并且，建立待評建筑快速判斷分類的方法。選取一個在線監(jiān)測的政府辦公建筑為案例，通過比較該建筑的統(tǒng)計定額值、技術定額值和實際監(jiān)測總用能，檢驗定額的合理性和有效性。
[Abstract]:Global climate change is the most serious environmental problems humanity has ever faced, with Scientific Outlook on Development in China thoroughly, conserve resources and protect the environment has become China's basic national policy, how to accelerate the transformation of urban and rural construction mode and construction development, improve people's quality of life, become a problem to be solved. At present, the building has been with the industry, traffic is one of three major areas of energy consumption, but also an important source of greenhouse gas emissions. Building energy consumption is high, a significant, long-term growth trend is obvious, and has great potential of energy saving and emission reduction, the cost is relatively low.
In order to effectively improve the utilization efficiency of public building energy, establish a long-term mechanism to promote the energy efficiency of public buildings, China government established the state organ office buildings and large public building energy monitoring system. The policy system goal is to gradually establish a national network of state machine off office buildings and large public building energy monitoring platform, real-time monitoring on the construction of national key energy city, and through the energy consumption statistics, energy audit, energy consumption and fixed price system, the state organ office buildings and large public buildings to improve the level of operation and management of energy conservation, cultivation of building energy saving service market, for further energy-saving high energy consumption building conditions. In the "11th Five-Year" period, China total energy consumption of office buildings and large public building statistics of 33000 buildings, 4850 buildings completed the energy audit, publicity nearly The situation of energy consumption of 6000 buildings, has more than 1500 buildings on energy consumption was monitored. At the same time, Beijing, Tianjin, Shenzhen, Jiangsu, Chongqing, Inner Mongolia, Shanghai, Zhejiang, Guizhou and other nine provinces and cities have carried out energy dynamic monitoring platform construction of pilot work, some provinces and cities have also introduced the trial operation of public buildings energy consumption. Energy consumption is an important part of the public building energy monitoring system, it make the over quota increase and determine the energy saving standard. However, due to the climate, the electrical characteristics and the level of economic development and the difference in construction, selection of analysis method of energy consumption quota which there is considerable controversy, resulting in energy consumption the quota policy failed to get a promotion. In order to give the Ministry of housing and urban development to provide reference quota and fixed price policy, taking Chongqing area as an example, through the investigation and study The current situation and level of energy consumption of public buildings in Chongqing are classified from the load rate of building operation stage, and a more scientific and reasonable energy quota method is developed.
First of all, the basic information of 207 public buildings in Chongqing city were randomly selected to research public building monitoring platform, and in 2012 each year hours of electricity data. Based on the data of electricity, three main types of errors (data burst errors, data delay and data processing, the final interrupt) selected 145 public buildings in detail. Based on the 145 public buildings electricity situation carries on the statistical analysis, found that the different use function of building electricity level difference is very big, is also a great difference between the same function of public buildings. The annual energy consumption respectively of the Chongqing city government office buildings, shopping malls, General Office buildings, public buildings, the four types of hotel building electricity distribution statistical analysis, the consumption level, consumption characteristics and consumption distribution, building energy consumption distribution found The law obeys the lognormal distribution.
Then the different functions of public buildings, Zhou power change was studied. Through the power change of sample building week, analysis power change month, different building electrical characteristics. The study found that the total energy consumption volatility is mainly affected by the electric air conditioning system, the energy consumption of two months two peaks and troughs. The running time of the different functions of the building, leading to all kinds of hourly load rate differences. Load rate changes lead to changes of electricity. Electrical changes in the different functions of the building. To a certain extent in the construction of the electrical characteristics. The rationality from the building operation load the rate of change of the establishment of public building classification.
In this paper, the effect of screening by various main factors of electric public buildings, to carry out correlation analysis and influence weights between influence factors and building energy consumption. The analysis found that the alpha =0.01 level, lighting energy consumption per unit area, per unit area of air conditioning energy consumption, energy consumption and energy consumption per unit area per unit area and unit area of special system the annual energy consumption significantly positive correlation; the energy consumption of air conditioning air conditioning unit area form and a significant positive correlation. The actual energy consumption monitoring of a government office building annual energy consumption in 2012 sample daily meteorological data obtained from the year 2012, Chinese Meteorological Bureau, and then through the simulation of the typical annual daily electricity consumption of the building energy consumption. Using the method of linear regression, the difference between the daily average temperature of typical year daily average temperature in 2012 and measured the set as independent variable x, the actual The difference between daily energy consumption and simulated electricity consumption is set as dependent variable y, and a linear regression equation is obtained for the influence of outdoor air temperature change on total energy consumption of buildings.
The linear regression equation of 0.6690.0055x and outdoor day air temperature change on building air conditioning energy consumption is 0.5820.00145x, and by significance test, it is found that y and X are significantly correlated on the level of confidence probability of 0.95.
Based on the above analysis, public buildings will be two categories. The first level in accordance with the public building use function classification, divided into government office buildings, office buildings, shopping malls, hotel buildings, school buildings, six hospital buildings. Based on the first level classification, using hierarchical clustering analysis method two classification of daily load rate changes as the standard to divide the building into ABC class three, class a total daily load rate is the highest, followed by the B class, C class, the total load rate is the lowest. The main step is to first get the construction rate of change of matrix in the typical daily load seasons, using hierarchical clustering method, according to the daily total load rate of the high school low level ABC is divided into three categories. The polynomial fitting, each kind of building typical part load rate curve fitting equation. And the significant level of 0.05, a significant test of the fitting curve, and from R-Square It is very close to 1, which indicates that the fitting degree of each fitting equation is high. It is proved that the classification method is scientific and effective.
And the use of hierarchical clustering analysis method of lighting and socket system and air conditioning system of electricity distribution curve for fast classification. According to the clustering steps between the coefficient change rate to determine the best number of clusters was used to classify samples from a large number of buildings, and rapid screening and extract the usage of electricity. By analyzing the characteristic curve the results of the classification, the method is applied to the large public building electrical characteristics of rapid screening is very effective.
For not included in the public building monitoring platform construction through the forecast to the two level classification to judge its daily load rate. Therefore the establishment of building electric load forecasting model using the rate of time series ARIMA model. With the influence of electric load rate by building the user behavior building, with random characteristics. And the time series analysis model is applied to the building electricity load forecast rate can be unified summation effect of various complex factors contained in the time series. A stochastic process model based on the building load rate, and through the use of 69 Chongqing city government office building built in 2012 is used to set the load factor data of time series model, identification and fitting, prediction model ARIMA (1,0,8) (2,1,1), and the adaptability of the model is verified. In order to further verify the obtained ARIMA (1,0,8) (2,1,1) and the applicability of using the model of pre The daily electricity consumption rate of two government office buildings is measured. It is found that the prediction effect is not very different from the actual detection results, and the actual value is basically within the confidence interval. This method can also be extended to the prediction of the electricity load rate of other types of buildings.
Finally, based on the exploration of public buildings on the reasonable classification, respectively from the statistical quota and quota technology two direction developed in Chongqing area of public building energy consumption quota. Statistical quota service object of government departments, to provide reference for government departments to formulate policies. Technical quota is the main target of public building management personnel and technical personnel, to provide reference for the next step for energy saving or energy-saving operation of the building. The building and establishment of rapid judgment classification method. Select an on-line monitoring of the government office building as a case, through the construction of the statistical quota value, technical quota value and the actual total energy monitoring, inspection quota is reasonable and effective.

【學位授予單位】：重慶大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TU242;TU111.195

【參考文獻】

相關期刊論文 前10條

1 丁力行,陳季芬,郭卉;全年干球溫度的截尾正態(tài)分布模型及其應用[J];長沙鐵道學院學報;2001年04期

2 梁珍,趙加寧,路軍;公共建筑能耗主要影響因素的分析[J];低溫建筑技術;2001年03期

3 徐斌;蔣平;羅立新;董文博;;個人行為對校園能耗和節(jié)能減排的影響分析——復旦大學案例分析[J];復旦學報(自然科學版);2011年05期

4 嚴智勇;許巧玲;;福州地區(qū)大型辦公建筑能耗的多元線性回歸分析[J];能源與環(huán)境;2009年01期

5 廖深瓶;陶尚儒;朱惠英;馬俊;;廣西大型公共建筑用能定額的研究[J];建筑節(jié)能;2012年10期

6 李志生;張國強;李冬梅;梅勝;劉旭紅;李利新;;廣州地區(qū)大型辦公類公共建筑能耗調查與分析[J];重慶建筑大學學報;2008年05期

7 梁珍,趙加寧,郭駿;高層辦公建筑能耗調查與節(jié)能潛力分析[J];節(jié)能技術;2001年01期

8 張甫仁;;基于氣象熱舒適度的建筑能耗灰色神經網絡預測[J];建筑科學;2007年10期

9 曹勇;劉益民;于丹;魏崢;劉輝;宋業(yè)輝;;德國與美國能耗基準確定方法在北京地區(qū)辦公建筑空調系統(tǒng)能耗定額確定方面的應用及對比[J];建筑科學;2012年04期

10 曹勇;魏崢;劉輝;孟沖;宋業(yè)輝;劉益民;;德國VDI3807標準對我國能耗定額的啟示[J];建設科技;2011年22期

相關博士學位論文 前4條

1 王鑫;公共建筑用能分項計量綜合關鍵技術研究[D];清華大學;2010年

2 梁傳志;夏熱冬暖地區(qū)辦公建筑能耗特性研究[D];天津大學;2011年

3 萬蓉;基于氣候的采暖空調耗能及室外計算參數研究[D];西安建筑科技大學;2008年

4 周智勇;建筑能耗定額的理論與實證研究[D];重慶大學;2010年

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