X.Liang et al.Building and Environment 102(2016)179-192 187 ◆Mon叠TueWed←Thu米Fri 20 00 80 60 40 20 0 1 234567891011121314151617181920212223 Time Fig.9.Mean of hourly occupants presence of weekdays. ◆-Euclidean Distance --Correlation Similarity Dynamic Time Wrap Q.12 a.11 Q1 0,09 0.08 豆 0.07 a.06 0.05 0.04 0.03 3 4 5 6 Number of k Fig.10.Performance of k and distance metrics evaluated by BDL information can be transformed from the existing data set (time 1 happened on Friday and there is no Pattern 4 happened in winter step column).to simplify the proposed method,seasons are It means it is possible to induce the underlying rules of patterns selected as an analysis factor.As shown in Fig.12,these three fac- from these factors. tors have strong relations with patterns.For example,most Pattern Fig.13 shows the decision tree for classification of the patterns ◆Pattern1 -Pattern2 Pattern3←Pattern4 120 100 40 0 891011121314151617181920212223 Time Fig.11.Patterns of occupant presence.information can be transformed from the existing data set (time step column), to simplify the proposed method, seasons are selected as an analysis factor. As shown in Fig. 12, these three fac￾tors have strong relations with patterns. For example, most Pattern 1 happened on Friday and there is no Pattern 4 happened in winter. It means it is possible to induce the underlying rules of patterns from these factors. Fig. 13 shows the decision tree for classification of the patterns Fig. 9. Mean of hourly occupants presence of weekdays. Fig. 10. Performance of k and distance metrics evaluated by BDI. Fig. 11. Patterns of occupant presence. X. Liang et al. / Building and Environment 102 (2016) 179e192 187