Mining building performance data for energy-efficient operation

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.

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► Found correlations between weather state, buildings’ specifications and performance.
► Supervised buildings’ operational processes using its physical specifications.
► Potentials to facilitate short-term and long-term buildings’ operational processes.
► Optimized the trade-off between energy consumption and occupants’ comfort.
► Simplified modeling internal natural lighting and occupants’ thermal comfort.