發(fā)布時間：2018-05-26 12:43

  本文選題：公共建筑 + 冷負荷��； 參考：《湖南大學》2015年碩士論文

【摘要】：能源是制約人類社會的快速發(fā)展的瓶頸,節(jié)約能源是每一個公民應盡的義務。在我國,由于經濟快速生長,能源需求量逐年增加。作為社會總能耗的主要組成部分,建筑能耗的上升速度較快,所以在建筑領域,其節(jié)能潛力很大,對進一步實現我國的能源戰(zhàn)略目標以及可持續(xù)發(fā)展有重要意義�？照{系統(tǒng)作為影響建筑能耗最重要的因素,實現建筑節(jié)能應從準確計算建筑冷熱負荷開始,尤其是冷負荷。本文首先分析了影響建筑冷負荷的影響因素以及現有的負荷預測方法,得知設計人員在計算冷負荷值時傾向使用計算機模擬法和估算法。然而由于計算機模擬方法需要輸入的參數過多且耗時長,人們不易掌握,估算法由于估算指標值在現有規(guī)范和設計中僅僅只根據建筑功能粗略的進行分類,往往易使計算的冷負荷偏大,造成能源的浪費。因此在不失計算精度的基礎上簡化計算方法,分析負荷影響因素,是現階段預測建筑冷負荷的主要研究內容。通過對冷負荷影響因素定性分析,選出影響建筑冷負荷比較顯著的因素：建筑物所在地理位置、建筑功能、體形系數、窗墻比、綜合傳熱系數、室內人均密度等作為分類指標,對現有公共建筑進行分類,建立一系列基準建筑模型,同時借用能耗模擬軟件DesignBuilder對每一類模型進行全年冷負荷特性分析,最終選取全年第51大單位面積冷負荷值作為冷負荷指標計算值,建立公共建筑單位面積冷負荷指標值數據庫。最后,以長沙市所調研的公共建筑為實例驗證本文所建數據庫的適用性。通過分析辦公建筑、商業(yè)建筑、賓館類建筑以及教育類建筑實際運行值、數據庫選取值以及設計階段通過估算法計算的設計值三者之間的大小關系得知,本文所建數據庫在預測公共建筑冷負荷時,其預測精度能夠滿足要求,其誤差大小在工程允許范圍內。而且,只要知道一定的建筑參數,本文所建數據庫同樣適用于城市規(guī)劃階段冷負荷的預測。由實例驗證可得知,數據庫選取值和實際運行值之間的誤差較小,表明本文所建的公共建筑單位面積冷負荷指標數據庫具有較好的有效性和較高的預測精確度�？梢哉J為,本文所建數據庫對公共建筑冷負荷的預測能夠提供一定的理論依據和工程應用價值。
[Abstract]:Energy is the bottleneck restricting the rapid development of human society. Saving energy is the duty of every citizen. In China, due to the rapid growth of the economy, energy demand is increasing year by year. As the main component of the total energy consumption of the society, the building energy consumption is rising rapidly, so in the field of construction, its energy saving potential is very large, which is of great significance to the further realization of our country's energy strategic goal and sustainable development. As the most important factor affecting building energy consumption, air conditioning system should begin with accurate calculation of building cooling and heat load, especially cold load. In this paper, the factors affecting the cooling load of buildings and the existing load forecasting methods are analyzed. It is found that the designers tend to use computer simulation and estimation methods in the calculation of the cooling load. However, because the computer simulation method needs to input too many parameters and takes a long time, it is difficult for people to grasp the estimation method, because the estimation index value is only roughly classified according to the building function in the existing specification and design. Often easy to make the calculation of the cooling load is too large, resulting in a waste of energy. Therefore, simplifying the calculation method and analyzing the factors affecting the load on the basis of not losing the calculation precision are the main research contents of forecasting the building cooling load at the present stage. Through the qualitative analysis of the factors affecting the cooling load, the factors that influence the building cooling load are selected as the classification indexes: the location of the building, the function of the building, the coefficient of shape, the ratio of window to wall, the comprehensive heat transfer coefficient, the per capita density of the room, etc. The existing public buildings are classified and a series of benchmark building models are established. At the same time, energy consumption simulation software DesignBuilder is used to analyze the annual cooling load characteristics of each model. Finally, the 51st unit area cold load value is selected as the calculated value of the cooling load index, and the database of the common building unit area cold load index value is established. Finally, taking the public buildings investigated in Changsha as an example, the applicability of the database is verified. By analyzing the actual operation values of office buildings, commercial buildings, hotel buildings and educational buildings, the value of database selection and the design value calculated by the method of estimation in the design stage, the size relationship among them is obtained. The prediction accuracy of the database in this paper can meet the requirements when forecasting the cooling load of public buildings, and the error is within the allowable range of the project. Moreover, as long as certain building parameters are known, the database can also be used to predict the cooling load in urban planning stage. It can be seen that the error between the selected value and the actual operating value of the database is small, which indicates that the database of unit area cooling load index built in this paper is effective and accurate. It can be concluded that the database built in this paper can provide certain theoretical basis and engineering application value for forecasting the cooling load of public buildings.
【學位授予單位】：湖南大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TU242;TU831.2

【參考文獻】

相關期刊論文 前10條

1 趙亞楠;刁乃仁;韓明坤;蓋玉剛;;窗墻比和體形系數對建筑全年動態(tài)負荷的影響[J];山東建筑大學學報;2012年06期

2 端木琳;王振江;李祥立;王仁瑾;;區(qū)域建筑形狀對圍護結構冷負荷的影響分析[J];土木建筑與環(huán)境工程;2011年S1期

3 苑翔;龍惟定;張潔;;建筑體形參數與外擾因素影響下冷負荷的相關性分析[J];中南大學學報(自然科學版);2010年05期

4 王金奎;史慧芳;;窗墻比在公共建筑節(jié)能設計中的應用[J];低溫建筑技術;2010年09期

5 王飛;胡文斌;;廣州地區(qū)辦公建筑空調負荷特性及分布規(guī)律探討[J];科學技術與工程;2010年23期

6 王軍;張旭;;建筑室內人員密度對新風量指標的影響特征分析[J];流體機械;2010年02期

7 苑祥;龍惟定;;區(qū)域建筑冷負荷預測中的朝向因素分析及轉換[J];土木建筑與環(huán)境工程;2009年06期

8 張思思;董重成;王陸廷;;我國村鎮(zhèn)住宅采暖熱負荷指標計算分析[J];低溫建筑技術;2009年11期

9 瞿燕;潘毅群;黃治鐘;;上海世博園區(qū)空調動態(tài)負荷預測與研究[J];暖通空調;2008年10期

10 李瓊;孟慶林;吉野博;持田燈;;基于支持向量機的建筑物空調負荷預測模型[J];暖通空調;2008年01期

相關碩士學位論文 前6條

1 張春亮;千米級摩天大樓空調動態(tài)負荷研究[D];哈爾濱工業(yè)大學;2013年

2 李國帥;夏熱冬暖地區(qū)公共建筑能耗定額分類模型及標準建筑的研究[D];重慶大學;2010年

3 馬明明;公共建筑空調系統(tǒng)改造與節(jié)能潛力的研究[D];重慶大學;2007年

4 宋瑋;廣州地區(qū)公共建筑能耗調查與研究[D];廣州大學;2006年

5 吳曉艷;公共建筑空調系統(tǒng)的節(jié)能設計與優(yōu)化管理[D];湖南大學;2006年

6 侯余波;夏熱冬暖地區(qū)建筑師用建筑能耗計算方法的研究[D];重慶大學;2001年

，

本文編號：1937273