寒区农宅供热能耗统计及评价研究

发布时间：2019-05-10 02:56

【摘要】：伴随我国建筑节能工作的深入开展和广大村镇地区的经济发展，村镇地区住宅的节能降耗问题日益引起业界关注。寒区农宅的供热能耗更是占据了建筑总能耗的大部分比例，是我国建筑节能的重中之重。充分了解寒区农宅的能耗现状和影响因素，进行科学的统计分析和评价，是国家制定农村建筑节能政策、制定并实施有效建筑节能技术的重要依据。首先针对寒区农宅建筑用能特点，建立包含家庭基本情况、建筑基本信息、建筑围护结构基本信息、冬季室内环境信息、农宅用能信息的五大块统计指标体系。初步建立寒区农宅供热能耗数据库。针对寒区农宅的现状，进行了全面的基本信息描述统计分析。深入调查分析了东北三省村镇农宅的建筑总用能、供热商品用能和非商品用能的使用情况，得到户年供热总能耗若干统计量，并与实测数据进行对照分析。应用平均数差异检验分析的方法对寒区农宅供热能耗的主要影响因素进行遴选和关联强度分析。采用独立样本t检验针对具有两个水平的各影响因素进行分析，得到4个在0.05水平上显著的因素。采用单因素方差分析的方法针对具有三个及以上水平的各影响因素进行分析，得到11个在0.05水平显著的因素，并进行事后比较分析发掘各组之间深层的差异。为研究因素间交互作用的影响，以建筑供热面积和其他各因素的交互分析为例继续做双因素方差分析，旨在寻找那些与供热面积交互后对农宅户年供热能耗有显著影响的因素，在此基础上做剔除供热面积影响下的各因素单纯主效应分析。分别采用多元线性回归分析和逻辑斯回归分析两种方法，将影响村镇农宅供热能耗的显著因素纳入回归分析，得到适宜的多元回归模型并加以验证，，以期实现利用最少变量对农宅供热能耗的描述、解释和预测。在多元线性回归分析中，通过多个模型对比研究，最终选定了拟合优度高、预测误差比率低、自变量个数较少的包含交互的指数模型1，应用此模型可以对寒区农宅供热能耗进行预测，得到农宅单位度日数单位供热面积年供热能耗的预测值。逻辑斯回归分析可以从另外一个角度，对寒区农宅供热能耗高、中、低进行分类概率预测，逻辑斯回归分析结果对于供热能耗评价有借鉴意义。依据前述统计分析结果，主要从用户基本信息指标、建筑围护结构热工指标、供热能源与系统指标、供热环境指标和居民用热行为指标五个方面合计20个分项指标，对寒区农宅供热能耗进行评价。采用层次分析法，依据统计分析与推断的结果构造比较判断矩阵，计算确定各层级指标权重，通过指标评价值的计算可以做出寒区农宅供热能耗评价。
[Abstract]:With the in-depth development of building energy conservation in China and the economic development of villages and towns, the problem of energy saving and consumption reduction of residential buildings in villages and towns has attracted more and more attention of the industry. The heating energy consumption of agricultural houses in cold region accounts for most of the total building energy consumption, and it is the most important part of building energy saving in our country. It is an important basis for the state to formulate rural building energy conservation policy and to formulate and implement effective building energy saving technology to fully understand the present situation and influencing factors of energy consumption of agricultural houses in cold region and to carry out scientific statistical analysis and evaluation. Firstly, according to the characteristics of energy consumption of agricultural houses in cold region, five statistical index systems are established, which include the basic situation of family, the basic information of architecture, the basic information of building envelope, the information of indoor environment in winter and the information of energy consumption of agricultural houses. The database of heating energy consumption of agricultural houses in cold region is established. According to the present situation of rural houses in cold region, a comprehensive statistical analysis of basic information is carried out. The total building energy consumption, heating commodity energy consumption and non-commodity energy consumption of rural houses in the three provinces of Northeast China are investigated and analyzed deeply, and some statistics of total household heating energy consumption are obtained and compared with the measured data. The main influencing factors of heating energy consumption of agricultural houses in cold region are selected and analyzed by means of average difference test and analysis. The independent sample t test was used to analyze the influencing factors with two levels, and four significant factors at 0.05 level were obtained. The single factor variance analysis method was used to analyze the influencing factors with three or more levels, and 11 significant factors at 0.05 level were obtained, and the deep differences among the three groups were compared and analyzed afterwards. In order to study the influence of interaction among factors, taking the interactive analysis of building heating area and other factors as an example, the two-factor variance analysis was continued in order to find out those factors that had significant influence on the annual heating energy consumption of farm and homestead households after interacting with heating area. On this basis, the simple main effect analysis of each factor under the influence of eliminating heating area is made. By using multiple linear regression analysis and logic regression analysis, the significant factors affecting the heating energy consumption of rural houses in villages and towns are brought into the regression analysis, and the suitable multiple regression model is obtained and verified. In order to realize the description, explanation and prediction of the heating energy consumption of agricultural houses by using the minimum variables. In the multivariate linear regression analysis, through the comparative study of multiple models, the exponential model 1, which has high goodness of fit, low prediction error ratio and small number of independent variables, is selected. This model can be used to predict the heating energy consumption of agricultural houses in cold region, and the predicted value of annual heating energy consumption per unit heating area of agricultural houses can be obtained. Logic regression analysis can predict the classification probability of high, medium and low heating energy consumption in cold region from another point of view. The results of logic regression analysis can be used for reference in the evaluation of heating energy consumption. According to the above statistical analysis results, there are 20 sub-indexes from five aspects: the basic information index of users, the thermal index of building enclosure structure, the index of heating energy and system, the index of heating environment and the index of thermal behavior of residents. The energy consumption of heating in rural houses in cold region is evaluated. By using the analytic hierarchy process (AHP), the comparative judgment matrix is constructed according to the results of statistical analysis and inference, and the index weight of each level is calculated and determined. Through the calculation of the evaluation value of the index, the energy consumption evaluation of agricultural house heating in cold region can be made.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TU833

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