Mining building performance data for energy-efficient operation

作者：Ahmed Ammar^*; Korres Nicholas E; Ploennigs Joern; Elhadi Haithum; Menzel Karsten

来源：Advanced Engineering Informatics, 2011, 25(2): 341-354.

DOI：10.1016/j.aei.2010.10.002

• Data mining
• Intelligent building
• Decision support
• Energy efficiency
• Occupants' thermal comfort
• Indoor daylight

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.

• 出版日期2011-4