Conclusions

In this dissertation, data mining are proposed to analyze measured building-related data. Furthermore, a data analysis process and a data mining framework are proposed to extract useful knowledge from building-related data, so as to help reduce building energy consumption. The process consists of eight steps: (1) problem definition and objective setting; (2) data source selection; (3) data collection; (4) data preprocessing/preparation; (5) data warehouses/marts construction; (6) data mining and model construction; (7) results analysis and evaluation; (8) knowledge discovery and presentation. The framework is composed of measured building-related data and data mining algorithms. It provides useful knowledge about the total building energy performance. In particular, three main data mining techniques, namely classification analysis, cluster analysis, and association rule mining are employed in this framework.

The applicability of the proposed process and framework was demonstrated through their applications to two sets of data collected from 80 residential buildings and a mechanically ventilated building. The applications have suggested that the process and framework can effectively help develop data analysis methodologies for extracting hidden useful knowledge from building-related data, in order to account for interactions between building energy consumption and its influencing factors. A clear and thorough

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understanding of such interactions could provide essential guidance in reducing building energy consumption. In this study, four data analysis methodologies were developed and applied to the collected data, and are summarized as follows:

(1) Classification analysis was applied to develop a methodology for establishing building energy demand predictive models.. The developed model estimates the building energy performance indexes in a rapid and easy way. This methodology is appropriate to classify and predict categorical variables: its competitive advantage over other widely used modeling techniques, such as regression methods and ANN methods, lies in the ability to generate accurate predictive models with interpretable flowchart-like tree structures that enable users to quickly extract useful information. 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 decision tree method can classify and predict 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. The method can provide the combination of significant factors as well as the threshold values that will lead to high building energy performance. Moreover, the average EUI in each classified data subsets can be used as reference when performing prediction. The outcomes of this methodology could benefit architects, building designers and owners greatly in the building design and

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operation stage. One crucial benefit is improving building energy performance mainly due to the fact that designers can optimize their building design plans based on the combination of significant factors as well as the threshold values. Another advantage of this methodology is that it can be utilized by users without requiring much computation knowledge.

(2) Cluster analysis was used to develop a methodology for examining the influences of occupant behavior on building energy consumption. To deal with data inconsistencies, min-max normalization is performed as a data preprocessing step before clustering. Grey relational grades, a measure of relevancy between two factors, are used as weighted coefficients of different attributes in cluster analysis. To demonstrate the applicability of the proposed methodology, it was applied to a set of residential buildings’ measurement data. 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. The results obtained could help occupants to prioritize efforts at the modification of their behavior in order to reduce building energy consumption.

(3) 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. To provide information for building

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owners and operators to reduce energy consumption, both daily and annual data are mined. Moreover, data from two different years is mined, and the obtained associations and correlations in the two years are compared. In order to demonstrate the applicability of the proposed methodology, it was applied to the operational data of an air-conditioned building. The results show there are possibilities for saving energy by modifying the operation of mechanical ventilation systems and by repairing equipment. The results obtained from this methodology could help to better understand building operation and provide opportunities for energy conservation. (4) Cluster analysis, classification analysis, and association rule mining were combined

to formulate a methodology for identifying and improving occupant behavior in buildings. In order to demonstrate its applicability, the methodology was applied to a group of residential buildings, and one building with the most comprehensive household appliances was selected as the case building. The results show that, for the case building, 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. Also, a reference building can be identified for the case building to evaluate its energy-saving potential due to occupant behavior modification. Considering the diversity of specific occupant behavior, the determination of energy-inefficient general occupant behavior can narrow down the scope of identification of energy-inefficient specific occupant behavior, and thus can

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help occupants to quickly find the generated association rules, as well as specific behavior, which deserve more attention. Also, such information is extracted from the measured data and covers almost all energy-related behavior. With such information, building occupants can then better understand their behavior patterns, and easily focus on the energy-inefficient behaviour that needs to be modified. Therefore, the main advantage of the proposed methodology lies in its high efficiency of occupant behavior improvement. Moreover, the identification of energy-inefficient general behavior in this study is mainly based on the comparison with other similar buildings; this can help building owners to be aware of avoidable energy waste caused by their behavior, and motivate them to modify their activities accordingly.