Mining Hidden Knowledge from Measured Data for Improving Building Energy Performance.
PhD thesis, Concordia University.
- Accepted Version
Nowadays, building automation and energy management systems provide an opportunity to collect vast amounts of building-related data (e.g., climatic data, building operational data, etc.). The data can provide abundant useful knowledge about the interactions between building energy consumption and its influencing factors. Such interactions play a crucial role in developing and implementing control strategies to improve building energy performance. However, the data is rarely analyzed and this useful knowledge is seldom extracted due to a lack of effective data analysis techniques.
In this research, data mining (classification analysis, cluster analysis, and association rule mining) is proposed to extract hidden useful knowledge from building-related data. Moreover, a data analysis process and a data mining framework are proposed, enabling building-related data to be analyzed more efficiently. The applications of the process and framework to two sets of collected data demonstrate their applicability. Based on the process and framework, four data analysis methodologies were developed and applied to the collected data.
Classification analysis was applied to develop a methodology for establishing building energy demand predictive models. To demonstrate its applicability, the methodology was applied to estimate residential building energy performance indexes by modeling building energy use intensity (EUI) levels (either high or low). The results demonstrate that the methodology can classify and predict the building energy demand levels with an accuracy of 93% for training data and 92% for test data, and identify and rank significant factors of building EUI automatically.
Cluster analysis was used to develop a methodology for examining the influences of occupant behavior on building energy consumption. The results show that the methodology facilitates the evaluation of building energy-saving potential by improving the behavior of building occupants, and provides multifaceted insights into building energy end-use patterns associated with the occupant behavior.
Association rule mining was employed to develop a methodology for examining all associations and correlations between building operational data, thereby discovering useful knowledge about energy conservation. The results show there are possibilities for saving energy by modifying the operation of mechanical ventilation systems and by repairing equipment.
Cluster analysis, classification analysis, and association rule mining were combined to formulate a methodology for identifying and improving occupant behavior in buildings. The results show that the methodology was able to identify the behavior which needs to be modified, and provide occupants with feasible recommendations so that they can make required decisions to modify their behavior.
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