186 X Liang et aL Building and Environment 102 (2016)179-192 Monday Tuesday Wednesday Thursday Friday 200 180 60 40 80 60 40 20 0 6 12 18 0 6 12 18 0 6 12 18 0 6 12 18 0 6 12 1823 Time Fig.8.Hourly occupant presence from Monday to Friday. occupancy features are different in each weekday.the averages of distance metrics are compared among Euclidean distance,corre- hourly occupant presence in each weekday are very similar except lation similarity and dynamic time wrap.The results indicate that Friday.It indicates that traditional method,which only uses mean k=4 with Euclidean distance metric is the optimal parameter in k- value to describe occupant presence (Fig.9),loses granularity of means algorithm for this data set,shown in Fig.10. information. The four clusters of occupant presence data are shown in Fig.11. Fig.9 shows occupant presence in Building 101 has dual-peak From the visualization of the clusters,four patterns of occupant feature(mainly due to occupants going out for lunch).which is presence are highlighted as following,and the characteristics of similar to occupant schedules used in ASHRAE standard 90.1 [40].It patterns are shown in Table 3: verifies the occupancy data in this case is not abnormal and has general adaption.But the peak in the afternoon is a bit lower than Pattern 1 represents the lowest occupancy rate and shortest that in the morning (the peaks in morning and afternoon are the working time.The occupants go to work latest and go home late same in ASHRAE standard).In addition,the drop at noon is not as in this pattern.The occupancy rate rises to 50%around early sharp as that in ASHRAE standard 90.1,and the slopes are likewise 10 am.In addition,there is no obvious noon-break drop of the different.Therefore,ASHRAE standard schedule is not adaptable to curve in this pattern,since the occupant number decreases variable buildings,it is necessary to adjust occupancy factor ac- continuously since 11 am. cording to the data of a particular building. Pattern 2 represents the highest occupancy rate and longest The occupant presence curve can be divided into six periods: working time.The occupants go to work earliest and go home late in this pattern.The occupancy rate rises to 50%around early The night period (7 pm-6 am):Few occupants are in the 8 am and decreases to 50%around 5 pm.The noon-break is building.typically no occupant.The occupancy rate is normally around 12 pm. less than 10%of the max value. Pattern 3 represents the medium occupancy rate,medium The going-to-work period(7 am-9 am):Occupants are arriving working time,going-to-work later and going-home later.The successively in this period.The occupancy rate is growing from occupancy rate rises to 50%around 9 am and decreases to 50% 10%to70%. before 6 pm.The noon-break is around 2 pm The morning period (10 am-12 pm):Occupants are working in Pattern 4 is similar to Pattern 3,which likewise represents the the building and the occupancy rate stays around 80%. medium occupancy rate and medium working time.But the The noon-break period (12 pm-1 pm):some occupants go out main difference is that the going-to-work time and going-home for lunch and the occupancy rate drops slightly to lower than time are about 1 h earlier than that in Pattern 3.The occupancy 80%. rate rises to 50%around 8 am and decreases to 50%before 5 pm The afternoon period(2 pm-3 pm):Occupants are back to work The noon-break is around 1 pm. in the building.The occupancy rate rises slightly higher than 80%.but is lower than that in the morning period. The going-home period (4 pm-6 pm):Occupants are leaving 3.3.Rules of patterns office successively in this period.The occupancy rate is decreasing from 70%to 10%. Based on the recognized patterns of occupant presence,the rules of these patterns are induced in this step.According to data analysis,three influencing factors are used in the decision tree 3.2.Patterns of occupant presence generation:the patterns are related to(1)seasons(temperatures): (2)weekdays:and(3)daylight saving time(DST).Since the tem- This step is to discover the pattern of occupant presence during perature information needs other data input but season weekdays.The data mining software RapidMiner 6 is applied to disaggregate presence data to several clusters.In this study,BDI is used to find the optimal different k value in the k-means algorithm 1 Daylight saving time in USA starts on the second Sunday in March and ends on and distance metric.The k values are evaluated from 2 to 8 and the the first Sunday in November.occupancy features are different in each weekday, the averages of hourly occupant presence in each weekday are very similar except Friday. It indicates that traditional method, which only uses mean value to describe occupant presence (Fig. 9), loses granularity of information. Fig. 9 shows occupant presence in Building 101 has dual-peak feature (mainly due to occupants going out for lunch), which is similar to occupant schedules used in ASHRAE standard 90.1 [40]. It verifies the occupancy data in this case is not abnormal and has general adaption. But the peak in the afternoon is a bit lower than that in the morning (the peaks in morning and afternoon are the same in ASHRAE standard). In addition, the drop at noon is not as sharp as that in ASHRAE standard 90.1, and the slopes are likewise different. Therefore, ASHRAE standard schedule is not adaptable to variable buildings, it is necessary to adjust occupancy factor ac￾cording to the data of a particular building. The occupant presence curve can be divided into six periods: The night period (7 pme6 am): Few occupants are in the building, typically no occupant. The occupancy rate is normally less than 10% of the max value. The going-to-work period (7 ame9 am): Occupants are arriving successively in this period. The occupancy rate is growing from 10% to 70%. The morning period (10 ame12 pm): Occupants are working in the building and the occupancy rate stays around 80%. The noon-break period (12 pme1 pm): some occupants go out for lunch and the occupancy rate drops slightly to lower than 80%. The afternoon period (2 pme3 pm): Occupants are back to work in the building. The occupancy rate rises slightly higher than 80%, but is lower than that in the morning period. The going-home period (4 pme6 pm): Occupants are leaving office successively in this period. The occupancy rate is decreasing from 70% to 10%. 3.2. Patterns of occupant presence This step is to discover the pattern of occupant presence during weekdays. The data mining software RapidMiner 6 is applied to disaggregate presence data to several clusters. In this study, BDI is used to find the optimal different k value in the k-means algorithm and distance metric. The k values are evaluated from 2 to 8 and the distance metrics are compared among Euclidean distance, corre￾lation similarity and dynamic time wrap. The results indicate that k ¼ 4 with Euclidean distance metric is the optimal parameter in k￾means algorithm for this data set, shown in Fig. 10. The four clusters of occupant presence data are shown in Fig. 11. From the visualization of the clusters, four patterns of occupant presence are highlighted as following, and the characteristics of patterns are shown in Table 3: Pattern 1 represents the lowest occupancy rate and shortest working time. The occupants go to work latest and go home late in this pattern. The occupancy rate rises to 50% around early 10 am. In addition, there is no obvious noon-break drop of the curve in this pattern, since the occupant number decreases continuously since 11 am. Pattern 2 represents the highest occupancy rate and longest working time. The occupants go to work earliest and go home late in this pattern. The occupancy rate rises to 50% around early 8 am and decreases to 50% around 5 pm. The noon-break is around 12 pm. Pattern 3 represents the medium occupancy rate, medium working time, going-to-work later and going-home later. The occupancy rate rises to 50% around 9 am and decreases to 50% before 6 pm. The noon-break is around 2 pm. Pattern 4 is similar to Pattern 3, which likewise represents the medium occupancy rate and medium working time. But the main difference is that the going-to-work time and going-home time are about 1 h earlier than that in Pattern 3. The occupancy rate rises to 50% around 8 am and decreases to 50% before 5 pm. The noon-break is around 1 pm. 3.3. Rules of patterns Based on the recognized patterns of occupant presence, the rules of these patterns are induced in this step. According to data analysis, three influencing factors are used in the decision tree generation: the patterns are related to (1) seasons (temperatures); (2) weekdays; and (3) daylight saving time (DST)1 . Since the tem￾perature information needs other data input but season Fig. 8. Hourly occupant presence from Monday to Friday. 1 Daylight saving time in USA starts on the second Sunday in March and ends on the first Sunday in November. 186 X. Liang et al. / Building and Environment 102 (2016) 179e192