# An Automated IoT Visualization BIM Platform for Decision Support in Facilities Management
## 3 Case Demonstration  
This section uses an actual case to demonstrate the use of the proposed IoT visualization platform, which includes multiple temperature and humidity sensors and an Arduino microcontroller, and shows how a facility manager may adjust the layout of a space based on the visualized PMV comfort values. The case involved a laboratory located in the Engineering II Building on the campus of National Chiao Tung University, Hsinchu, Taiwan. Figure 14 shows the laboratory's spatial layout and the location of the sensors. Figures 15 and 16 show sample temperature and humidity data for each two-minute interval during the two-hour period from 5:39 p.m. to 7:09 p.m. on 30 December 2017. Note that the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PMV Index** & \(\mathbf{-3}\) & \(\mathbf{-2}\) & \(\mathbf{-1}\) & \(\mathbf{0}\) & \(\mathbf{1}\) & \(\mathbf{2}\) & \(\mathbf{3}\) \\ \hline
**Comfort feeling** & Cold & Cool & Slightly cool & Neutral & Slightly warm & Warm & Hot \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PMV index and comfort table. Figure 13: PMV analysis results in the BIM model. windows were opened from 5:39 p.m. to 6:09 p.m., and closed from 6:11 p.m. to 6:39 p.m. The air conditioner was turned on with the windows closed from 6:41 p.m. to 7:09 p.m.  
Figure 14: (a) Spatial layout of the research laboratory; (b) sensor locations. Figure 15: Cont. Figure 17 show snapshots of the visualized comfort level during different conditions, such as when the windows are open, when the windows are closed, and when the air conditioning is on, with the corresponding comfort level and improvement suggestions made by the platform developed in this study shown by the side. The PMV value did not change significantly during the entire duration of the experiment, except when the air conditioning was turned on, which resulted in a lower PMV value (\(-0.676\)) because the room became slightly colder. Nevertheless, small changes occurred in the PMV value when the room went from the open-window (\(-0.309\)) to the closed-window state (\(-0.24\)). The greatest difference in PMV value occurred during the shift between the air-conditioned and closed-window states; both temperature and humidity apparently decreased, as shown in Figure 16, and the PMV value also decreased, as can be seen from the change in visualized color in Figure 17.  
Figure 16: Humidity data for sensors 1–9. Figure 15: Temperature data for sensors 1–9. The experiment was conducted during the winter in Taiwan, and the results showed that differences in temperature and humidity were not apparent during a short period of time in this semitropical area. When the air conditioner was turned on, both the temperature and humidity appeared to decrease, but according to the PMV value, the room remained comfortable during all three states. In some cases, such as in a large space with limited number of air conditioners, the visualized comfort level may show an uneven distribution; in these cases the facility manager may choose to change spatial usage by assigning the most comfortable area for use by activities and the less comfortable area for use for storage, which will maximize comfort for most occupants. Regardless of the sensors' locations, whether the windows were opened or not did not affect the temperature and humidity data collected by the sensors. Although it can be seen from Figures 14-16 that sensors 1, 2 and 3 were the three sensors closest to the windows, these sensors had almost the same temperature and humidity readings after the windows were closed. It can be seen from Figure 16 that the deviation in humidity in the air-conditioned state was much greater than in either the open-window or closed-window states. This is because the air conditioners mechanically blew cool air with greater force than the natural breeze, and thus created greater deviations in humidity distribution.