Mining building performance data for energy-efficient operation

被引:68
作者
Ahmed, Ammar [1 ]
Korres, Nicholas E. [2 ]
Ploennigs, Joern [3 ]
Elhadi, Haithum [4 ]
Menzel, Karsten [1 ]
机构
[1] Univ Coll Cork, Dept Civil & Environm Engn, IRUSE, Cork, Ireland
[2] Univ Coll Cork, Environm Res Inst, Cork, Ireland
[3] Tech Univ Dresden, Inst Appl Comp Sci, Dresden, Germany
[4] IIT, Chicago, IL 60616 USA
基金
爱尔兰科学基金会;
关键词
Data mining; Intelligent building; Decision support; Energy efficiency; Occupants' thermal comfort; Indoor daylight; THERMAL COMFORT; OFFICE BUILDINGS; CONSUMPTION; SYSTEMS; WEATHER; DEMAND;
D O I
10.1016/j.aei.2010.10.002
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This research investigates the impact of connecting building characteristics and designs with its performance by data mining techniques, hence the appropriateness of a room in relation to energy efficiency. Mining models are developed by the use of comparable analytical methods. Performance of prediction models is estimated by cross validation consisting of holding a fraction of observations out as a test set. The derived results show the high accuracy and reliability of these techniques in predicting low-energy comfortable rooms. The results are extended to show the benefits of these techniques in optimizing a building's four basic elements (structure, systems, services and management) and the interrelationships between them. These techniques extend and enhance, current methodologies, to simplify modeling interior daylight and thermal comfort, to further assist building energy management decision-making. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:341 / 354
页数:14
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