基于最大信息系数的区域供冷能效敏感性分析*!
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摘要
随着我国经济的快速发展,大型建筑群越来越多,相应的关于区域供冷的研究也日益增多。分析区域供冷系统中多变量和多输出量之间的关系能够有效提高系统的能效。本文根据北方某地源热泵系统在夏季时间段内采集到的数据,采用最大信息系数(MIC)法选取供/回水温度、机组功率、综合室外温度等8个特征变量,并将数据分为工作日与非工作日2类,分别与系统能效进行敏感性分析。研究结果表明,在工作日中根据室内温度调节系统有助于提高系统能效,而在非工作日中根据综合室外温度和辐射进行调节能够有效提升系统能效。
With the rapid development of economy in China,there are more and more large buildings,and the corresponding research on the district cooling system is increasing.The analysis on the relationship between the multiple variables and multiple outputs in the district cooling system can effectively improve the energy efficiency of the system.According to the data collected in the summer time period of one ground source heat pump system in the north,the maximum information coefficient(MIC)method is used to select eight characteristic variables,such as water supply/return temperature,power of the assembling unit and comprehensive outdoor temperature,etc..Using the data divided into two categories,namely working days and weekend,the sensitivity analyses of the system energy efficiency are carried out respectively.The research results show that during the working days,the energy efficiency of system can be improved by adjusting the system according to the indoor temperature;in the weekend,the energy efficiency of system can be effectively improved by adjusting the system according to the comprehensive outdoor temperature and radiation.
With the rapid development of economy in China,there are more and more large buildings,and the corresponding research on the district cooling system is increasing.The analysis on the relationship between the multiple variables and multiple outputs in the district cooling system can effectively improve the energy efficiency of the system.According to the data collected in the summer time period of one ground source heat pump system in the north,the maximum information coefficient(MIC)method is used to select eight characteristic variables,such as water supply/return temperature,power of the assembling unit and comprehensive outdoor temperature,etc..Using the data divided into two categories,namely working days and weekend,the sensitivity analyses of the system energy efficiency are carried out respectively.The research results show that during the working days,the energy efficiency of system can be improved by adjusting the system according to the indoor temperature;in the weekend,the energy efficiency of system can be effectively improved by adjusting the system according to the comprehensive outdoor temperature and radiation.
引文
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[19]王浩.某区域供冷系统的能耗与经济性分析[D].哈尔滨工业大学,2016:9-14.
[2]黄建恩,冯伟.区域供冷系统经济性分析[J].流体机械,2007,35(1):82-86.
[3]杨际淼.济南高新技术产业开发区集中供冷暖站的设计及综合分析[J].制冷学报,1996(1):53-57.
[4]惠荣娜,徐奇,李德英,等.我国区域供冷的现状及发展[J].建筑节能,2007,35(3):47-50.
[5]樊瑛,龙惟定.区域供冷系统的动态逐时能分析与分析[J].制冷学报,2013,34(3):69-77.
[6]CHOW T T,AU W H,YAU R,et al.Applying district-cooling technology in Hong Kong[J].Applied Energy,2004,79(3):275-289.
[7]ZHEN L,LIN D M,SHU H W,et al.District cooling and heating with seawater as heat source and sink in Dalian,China[J].Renewable Energy,2007,32(15):2603-2616.
[8]闫军威.区域供冷系统节能优化运行与控制方法研究及系统实现[D].广州:华南理工大学,2012:12-25.
[9]谭福太.广州大学城区域供冷系统的节能优化[J].节能技术,2009,27(4):371-374.
[10]RESHEF D N,RESHEF Y A,FINUCANE H K,et al.Detecting novel associations in large data sets[J].Science,2011,334(6062):1518-1524.
[11]WANG S,ZHAO Y,SHU Y,et al.Fast search local extremum for maximal information coefficient(MIC)[J].Journal of Computational and Applied Mathematics,2018,327(Supplement C):372-387.
[12]张亚红,李玉鑑,张婷.检测多元相关关系的最大信息熵方法[J].电子与信息学报,2015,37(1):123-129.
[13]战泉茹.基于最大信息系数的人脸特征选择[D].长春:东北师范大学,2013:13-19.
[14]王鹏,张善从.基于最大信息系数的时延数据相关性分析方法[J].电子测量技术,2015(9):112-115.
[15]蔡毅,邢岩,胡丹.敏感性分析综述[J].北京师范大学学报(自然科学版),2008,44(1):9-16.
[16]ARCUCCI R,D′AMORE L,PISTOIA J,et al.On the variational data assimilation problem solving and sensitivity analysis[J].Journal of Computational Ph-y sics,2017,335:311-326.
[17]LEONELLI M,G9RGEN C,SMITH J Q.Sensitivity analysis in multilinear probabilistic models[J].Information Sciences,2017,411:84-97.
[18]ALAVI H M,NOBAKHTIAN S.Sensitivity analysis of the value function for nonsmooth optimization problems[J].Operations Research Letters,2017,45(4):348-352.
[19]王浩.某区域供冷系统的能耗与经济性分析[D].哈尔滨工业大学,2016:9-14.