重庆市公共建筑能耗定额方法研究

发布时间：2018-03-14 19:04

本文选题：公共建筑　切入点：能耗定额　出处：《重庆大学》2014年博士论文　论文类型：学位论文

【摘要】：全球气候变化是人类迄今面临的最重大环境问题，随着科学发展观在我国的深入贯彻，节约资源和保护环境成为我国的基本国策，如何加快转变城乡建设模式和建筑业发展方式，提高人民生活质量，成为一项亟待解决的问题。目前，建筑已经与工业、交通并列为能源消耗的三大领域，也是温室气体排放的重要来源。建筑领域能耗高、比重大，长期增长趋势明显，同时具备较大的节能潜力，减排成本相对较低。为有效提高公共建筑能源利用效率，建立促进公共建筑节能的长效机制，中国政府建立了国家机关办公建筑和大型公共建筑节能监管体系。该政策体系的工作目标是逐步建立起全国联网的国家机关办公建筑和大型公共建筑能耗监测平台，对全国重点城市重点建筑能耗进行实时监测，并通过能耗统计、能源审计、用能定额和超定额加价等制度，促使国家机关办公建筑和大型公共建筑提高节能运行管理水平，培育建筑节能服务市场，为高能耗建筑的进一步节能改造准备条件。在“十一五”期间，全国共完成国家机关办公建筑和大型公共建筑能耗统计33000栋，完成能源审计4850栋，公示了近6000栋建筑的能耗状况，已对1500余栋建筑的能耗进行了动态监测。同时，北京、天津、深圳、江苏、重庆、内蒙古、上海、浙江、贵州等九个省市已开展能耗动态监测平台建设试点工作，部分省市也已出台了试运行的公共建筑用能定额。用能定额是整个公共建筑节能监管体系当中的一个重要环节，它为制定超定额加价和确定节能改造标准提供依据。但是，由于各地气候、建筑用电特点以及经济发展水平等差异，选取何种能耗定额分析方法存在较大争议，致使能耗定额政策迟迟未能得到推广。为了给住房和城乡建设部制定用能定额和超定额加价政策提供参考，本文以重庆地区为例，通过调查研究重庆市公共建筑的能耗现状和用能水平，从建筑运行阶段的负荷率变化来进行分类，探索制定了一种较为科学合理的用能定额方法。首先，通过调研随机抽取重庆市公共建筑监测平台的207栋公共建筑的基本信息，以及它们在2012年全年各小时的用电数据。通过对用电数据进行整理，对主要存在的三种类型错误数据（突发错误、数据延迟和数据中断）进行处理，最终筛选出145栋公共建筑进行详细研究。通过对145栋公共建筑的总体用电现状进行统计分析，发现不同使用功能建筑的用电水平差异很大，相同功能的公共建筑之间年能耗的差异也很大。再分别对重庆市的政府办公建筑、商场建筑、一般办公建筑、酒店建筑这四类公共建筑的用电分布进行统计分析，得到其用电水平、电耗特征和电耗分布情况，发现建筑电耗分布规律服从对数正态分布。而后对不同功能公共建筑的月、周用电变化进行研究。通过对样本建筑的周用电变化、月用电变化进行分析，获得不同建筑的用电特征。研究发现，总能耗的波动主要是受到空调系统用电的影响，全年月能耗有两个波峰和两个波谷。由于不同功能建筑的运行时间不同，导致各类建筑逐时负荷率存在很大差异。负荷率变化导致了用电变化。不同功能建筑的日用电变化在一定程度上表征该类建筑的用电特点。论证了从建筑运行阶段的负荷率变化出发建立公共建筑分类方法的合理性。本文筛选了影响公共建筑用电的各主要因素，对影响因素与建筑能耗之间进行相关性分析和影响权重排序。分析发现，在α=0.01显著性水平上，单位面积的照明插座能耗、单位面积空调能耗、单位面积动力能耗和单位面积特殊系统能耗与单位面积年能耗显著性正相关；空调形式与单位面积空调能耗显著性正相关。通过实际能耗监测得到某一政府办公样本建筑2012年全年日用电能耗，从中国气象局获取2012年全年的气象数据，再通过对该建筑进行能耗模拟得到典型年全年逐日用电能耗。采用一元线性回归的方法，将典型年的日平均气温与2012年实测的日平均气温的差值设为自变量x，实际日用电能耗与模拟用电能耗的差值设为因变量y，得到室外日空气温度变化对建筑总能耗影响的一元线性回归方程式为 0.6690.0055x和室外日空气温度变化对建筑空调能耗影响的一元线性回归方程式为0.5820.00145x，且通过显著性检验，发现在置信概率为0.95的水平上，y和x显著相关。基于上述分析，将公共建筑进行两级分类。第一级按照公共建筑的使用功能进行分类，分为了政府办公建筑、一般办公建筑、商场建筑、酒店建筑、学校类建筑、医院建筑的六类。在第一级分类的基础上，运用层次聚类分析法进行二级分类，以日负荷率变化为标准将建筑分为ABC三类，A类日总负荷率最高，其次是B类，，C类日总负荷率最低。主要步骤是首先得到建筑在四季的典型日负荷率变化矩阵，再采用层次聚类方法，按照日总负荷率的高中低水平分为ABC三类。最后采用多项式拟合，得到每类建筑的典型部分负荷率曲线，得到拟合方程。并取显著性水平0.05，对拟合曲线进行显著性检验，且从R-Square都很接近于1，表示各拟合方程的拟合程度都较高。证明该分类方法科学有效。并且运用层次聚类分析方法，对照明及插座系统和空调系统的用电使用分布特征曲线进行快速分类。根据聚类步骤之间系数变化率来判断最佳聚类个数，从大量样本建筑进行快速分类，且快速筛选并提取出用电使用率特征曲线。通过对分类结果的分析，发现该方法应用于对大量公共建筑日用电特征进行快速筛选是非常有效的。而对于未纳入公共建筑监测平台的建筑需要通过预测其日负荷率来进行二级分类判别。因此利用时间序列ARIMA模型建立建筑用电负荷率预测模型。建筑用电负荷率受到建筑使用者行为的影响，具有随机性特点。而时间序列分析模型应用于对建筑用电负荷率的预测可将各种复杂因素的总和效应统一包含于时间序列之中。通过对建筑负荷率建立随机过程模型，并通过使用重庆市69栋政府办公建筑在2012年的用电负荷率数据进行时间序列模型的建立、识别和拟合，得到预测模型ARIMA (1,0,8)(2,1,1)，并对模型的适应性进行了验证。为了进一步验证得到的ARIMA (1,0,8)(2,1,1)的适用性，使用该模型预测两栋政府办公建筑的日用电使用率，发现预测效果与实际检测到的结果差异不大，且实际值基本都落入置信区间之内。该方法也可以推广使用到其他类型建筑的用电负荷率的预测。最后，在探索公共建筑合理分类的基础上，分别从统计定额和技术定额两个方向制定重庆地区的公共建筑能耗定额。统计定额的服务对象是政府部门，为政府部门制定政策提供参考。技术定额主要服务对象是公共建筑管理人员以及技术人员，为下一步对建筑进行节能改造或节能运行提供参考。并且，建立待评建筑快速判断分类的方法。选取一个在线监测的政府办公建筑为案例，通过比较该建筑的统计定额值、技术定额值和实际监测总用能，检验定额的合理性和有效性。
[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.

【学位授予单位】：重庆大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TU242;TU111.195

【参考文献】