基于数据挖掘的医院建筑用能评价及分析
发布时间：2019-06-03 21:14
【摘要】：在医院建筑的用能计量中,大量的能耗数据被收集并存储,在其中往往隐含着许多有价值的信息。本课题在医院用能统计工作已完成的前提下,从所收集的原始数据出发,通过数据挖掘方法,针对医院建筑自身用能特点,提出合适的用能评价及分析方法。首先,建立了适用于医院建筑末端用户的用能评价方法。该方法由综合用能评价、分类分项用能评价组成,这两部分评价都是以k-means聚类算法作为基本方法。通过综合评价,不仅可以评价用户总能耗量的高低,还可发现用户用能行为的差异,从而为医院开展用能评比工作提供依据;通过分类分项评价,可发现用户的节能潜力项,从而为用户提供行为节能方向。其次,提出了医院建筑用能分析的具体方法。在用能规律分析中,总结了昼夜能耗比、能耗均衡率等8个用能特性参数,以反映能耗曲线的基本情况。在节能潜力分析中,提出了两种计算方法:一种是与自身历史情况纵向比较的节能潜力,另一种是与最优用户横向比较的节能潜力。在用能异常情况分析中,提出了两种分析方法:第一种方法是通过聚类方法发现用能异常模式;第二种方法是借助神经网络对历史经验进行学习,从而掌握对异常原因的判断能力。最后,将所提出的用能评价及分析方法,应用于某医院的实际案例分析中。对该医院2014年2月的科室能耗进行评价,发现了综合用能偏高的23个科室,并为其提供了具体的行为节能方向;此外,基于用能评价结果,计算了各个科室该月份的节电潜力。对医院三号消毒站2013年11月27日的蒸汽用量进行能耗规律分析,并发现其存在峰谷调节潜力。通过基于聚类的异常分析方法,挖掘出6种典型的用能异常模式;通过基于神经网络的异常分析方法,给出了对用能异常原因的正确判断结果。
[Abstract]:In the energy consumption measurement of hospital buildings, a large number of energy consumption data are collected and stored, in which there is often a lot of valuable information. On the premise that the statistics of hospital energy consumption has been completed, starting from the collected original data, through the data mining method, according to the characteristics of hospital building energy consumption, this paper puts forward a suitable energy use evaluation and analysis method. First of all, an energy use evaluation method is established, which is suitable for the end users of hospital buildings. This method is composed of comprehensive energy use evaluation and classification sub-energy evaluation. K-means clustering algorithm is used as the basic method in these two parts of the evaluation. Through comprehensive evaluation, we can not only evaluate the total energy consumption of users, but also find the difference of energy consumption behavior of users, thus providing the basis for the hospital to carry out the evaluation of energy use. Through the classification and sub-evaluation, the energy saving potential items of users can be found, so as to provide users with behavioral energy saving direction. Secondly, the concrete method of energy consumption analysis in hospital building is put forward. In the analysis of energy consumption law, eight energy consumption characteristic parameters, such as diurnal energy consumption ratio and energy consumption balance rate, are summarized to reflect the basic situation of energy consumption curve. In the analysis of energy saving potential, two calculation methods are put forward: one is the energy saving potential compared vertically with the historical situation, the other is the energy saving potential compared with the optimal user horizontally. In the analysis of energy anomaly, two analysis methods are put forward: the first method is to find the abnormal pattern of energy use by clustering method; The second method is to learn the historical experience with the help of neural network, so as to master the ability to judge the abnormal causes. Finally, the proposed energy evaluation and analysis method is applied to the actual case analysis of a hospital. The energy consumption of the department of the hospital in February 2014 was evaluated, and 23 departments with high comprehensive energy consumption were found, and the specific direction of behavior energy saving was provided. In addition, based on the results of the energy use evaluation, the power saving potential of each department in that month was calculated. The energy consumption of steam consumption in No. 3 disinfection station of hospital on November 27, 2013 was analyzed, and it was found that there was peak and valley regulating potential. Through the anomaly analysis method based on clustering, six typical abnormal patterns of energy use are excavated, and the correct judgment results of the causes of abnormal energy use are given by the anomaly analysis method based on neural network.
【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】：2015
【分类号】：TU111.195
本文编号：2492223
[Abstract]:In the energy consumption measurement of hospital buildings, a large number of energy consumption data are collected and stored, in which there is often a lot of valuable information. On the premise that the statistics of hospital energy consumption has been completed, starting from the collected original data, through the data mining method, according to the characteristics of hospital building energy consumption, this paper puts forward a suitable energy use evaluation and analysis method. First of all, an energy use evaluation method is established, which is suitable for the end users of hospital buildings. This method is composed of comprehensive energy use evaluation and classification sub-energy evaluation. K-means clustering algorithm is used as the basic method in these two parts of the evaluation. Through comprehensive evaluation, we can not only evaluate the total energy consumption of users, but also find the difference of energy consumption behavior of users, thus providing the basis for the hospital to carry out the evaluation of energy use. Through the classification and sub-evaluation, the energy saving potential items of users can be found, so as to provide users with behavioral energy saving direction. Secondly, the concrete method of energy consumption analysis in hospital building is put forward. In the analysis of energy consumption law, eight energy consumption characteristic parameters, such as diurnal energy consumption ratio and energy consumption balance rate, are summarized to reflect the basic situation of energy consumption curve. In the analysis of energy saving potential, two calculation methods are put forward: one is the energy saving potential compared vertically with the historical situation, the other is the energy saving potential compared with the optimal user horizontally. In the analysis of energy anomaly, two analysis methods are put forward: the first method is to find the abnormal pattern of energy use by clustering method; The second method is to learn the historical experience with the help of neural network, so as to master the ability to judge the abnormal causes. Finally, the proposed energy evaluation and analysis method is applied to the actual case analysis of a hospital. The energy consumption of the department of the hospital in February 2014 was evaluated, and 23 departments with high comprehensive energy consumption were found, and the specific direction of behavior energy saving was provided. In addition, based on the results of the energy use evaluation, the power saving potential of each department in that month was calculated. The energy consumption of steam consumption in No. 3 disinfection station of hospital on November 27, 2013 was analyzed, and it was found that there was peak and valley regulating potential. Through the anomaly analysis method based on clustering, six typical abnormal patterns of energy use are excavated, and the correct judgment results of the causes of abnormal energy use are given by the anomaly analysis method based on neural network.
【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】：2015
【分类号】：TU111.195
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相关硕士学位论文 前2条
1 张奎;山东省医院建筑能源审计与节能措施研究[D];山东大学;2011年
2 原兆亮;医院建筑节能潜力分析与节能途径研究[D];长安大学;2012年
，本文编号：2492223
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