公共建筑分类方法及冷负荷预测的研究

发布时间：2018-05-26 12:43

本文选题：公共建筑 + 冷负荷　；参考：《湖南大学》2015年硕士论文

【摘要】：能源是制约人类社会的快速发展的瓶颈,节约能源是每一个公民应尽的义务。在我国,由于经济快速生长,能源需求量逐年增加。作为社会总能耗的主要组成部分,建筑能耗的上升速度较快,所以在建筑领域,其节能潜力很大,对进一步实现我国的能源战略目标以及可持续发展有重要意义。空调系统作为影响建筑能耗最重要的因素,实现建筑节能应从准确计算建筑冷热负荷开始,尤其是冷负荷。本文首先分析了影响建筑冷负荷的影响因素以及现有的负荷预测方法,得知设计人员在计算冷负荷值时倾向使用计算机模拟法和估算法。然而由于计算机模拟方法需要输入的参数过多且耗时长,人们不易掌握,估算法由于估算指标值在现有规范和设计中仅仅只根据建筑功能粗略的进行分类,往往易使计算的冷负荷偏大,造成能源的浪费。因此在不失计算精度的基础上简化计算方法,分析负荷影响因素,是现阶段预测建筑冷负荷的主要研究内容。通过对冷负荷影响因素定性分析,选出影响建筑冷负荷比较显著的因素：建筑物所在地理位置、建筑功能、体形系数、窗墙比、综合传热系数、室内人均密度等作为分类指标,对现有公共建筑进行分类,建立一系列基准建筑模型,同时借用能耗模拟软件DesignBuilder对每一类模型进行全年冷负荷特性分析,最终选取全年第51大单位面积冷负荷值作为冷负荷指标计算值,建立公共建筑单位面积冷负荷指标值数据库。最后,以长沙市所调研的公共建筑为实例验证本文所建数据库的适用性。通过分析办公建筑、商业建筑、宾馆类建筑以及教育类建筑实际运行值、数据库选取值以及设计阶段通过估算法计算的设计值三者之间的大小关系得知,本文所建数据库在预测公共建筑冷负荷时,其预测精度能够满足要求,其误差大小在工程允许范围内。而且,只要知道一定的建筑参数,本文所建数据库同样适用于城市规划阶段冷负荷的预测。由实例验证可得知,数据库选取值和实际运行值之间的误差较小,表明本文所建的公共建筑单位面积冷负荷指标数据库具有较好的有效性和较高的预测精确度。可以认为,本文所建数据库对公共建筑冷负荷的预测能够提供一定的理论依据和工程应用价值。
[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.
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TU242;TU831.2

【参考文献】