**3.4 Deployment of AI solution in airside system of the chiller plant located at SIT**

Implementation of measurement of the performance of AI-oriented proactive control solution comprises the following stages:


**Figure 14.**

*Dynamic chilled water balancing control process.*

**Figure 15.** *A schematic diagram of dynamic airflow balancing.*


After completing the installation of the required instrumentation, sensors, and IoT devices and commissioning, which includes fine-tuning the parameters, the testbed was operated in two phases: baseline mode and smart mode. Each mode was operated for 10 days, excluding weekends. The baseline mode represents the operation of

**Figure 16.** *A schematic diagram of dynamic chilled water balancing.*

existing conditions, isolating the proactive AI control, whereas the smart mode enables the proactive AI control, including dynamic air balancing, dynamic chilled water balancing, and fresh air optimization. The AI control overwrites the set point of BMS for existing operating conditions. Outdoor temperature and relative humidity were also recorded in the cloud during the testing of both phases to ensure that the impact of the weather conditions on the airside system's performance was considered. During both testing phases, data are recorded every minute using the instrumentation and sensor installed during the retrofitting stage, as tabulated in **Table 2**. During weekdays, AHUs are scheduled to start at 6:30 am, and the chiller and pumps are staged to turn on from 7 progressively for pre-conditioning. Since the building operates from 8:30 am to 6:00 pm, the data analysis only includes this period of the day. Key parameters, such as the temperature and relative humidity of all 43 zones, were recorded every minute in both baseline and smart modes. While AHU 2-1 supplies the cooling requirements to zone 1–23, AHU2-2 serves zones 24–43. The set point for all spaces was maintained during the tests at 24°C.

**Figure 18** depicts the temperature profiles of supply air measured during the baseline and smart mode tests. It is indicated that supply air temperature fluctuated between 13.6°C and 21.7°C, whereas it was maintained between 15.3°C and 19.3°C. The median temperature of supply air for baseline test and smart modes were 16.3°C and 17.1°C, respectively. Despite maintaining the close median supply air temperature between baseline mode and smart mode, the differential temperature in the interquartile for baseline mode was 1.5°C, and that of smart mode was about 1°C. It is also concluded from the box plot that most of the supply air temperature during the

#### **Figure 17.**

*A schematic diagram of uutside air optimization comfort range.*


#### **Table 2.**

*List of measured parameters and locations of measurement.*

smart mode test fall outside the interquartile, and outliers are beyond 1.5 times the interquartile (upper whisker) due to inefficient air distribution systems and control strategy.

On the other hand, no outliers were discovered beyond the upper and lower whiskers during the smart mode testing. It is also worth noting that the temperature

#### **Figure 18.**

*Supply air temperature profiles during the baseline and smart test.*

difference between the minimum and maximum supply air temperature was less than 4°C, assuring that smart mode control performs significantly better than baseline mode in terms of air distribution effectiveness. The space temperature with respect to time during baseline mode and smart mode is presented in **Figure 19**. During the test period of both modes, the set point temperature was maintained at 24°C, and the results were analyzed by comparing the baseline and smart mode tests. From the temperature and relative humidity profiles during the baseline test, it was observed that the space temperature during the smart mode test fluctuated from 21 to 25°C, while relative humidity in the space varied between 67% and 48%. Furthermore, the difference between space temperature and the set point was found to be considerably huge in some cases; it was as high as 3°C, resulting in cold spots and hotspots in space. However,

#### **Figure 19.**

*Average temperature and relative humidity profiles of the space during the baseline and smart mode tests.*

**Figure 20.** *Temperature distributions with 2°C range.*

during the smart mode test, the space temperature varied between 23 and 24.5°C, while relative humidity ranged from 52 to 65%, which falls well within the thermal.

In order to analyze further details of the temperature distribution in the space, a temperature bin is created with 2°C range with a total of 551,872 data points, and the results are illustrated in **Figure 20**. Seventeen percent of the data points that falls under undercooled regions (19–22°C) during the baseline test were shifted to 22–26°C when the smart mode was activated. Moreover, the smart mode delivered 99.97 percent of the events within the bin range of 20–23.9°C, highlighting that proactive AI control works perfectly fine to optimize the airside performance compared to the baseline mode. Therefore, proactive AI control not only achieves a better thermal comfort condition in the space but also improves the efficiency of the airside system, because AI control optimizes the cooling load prediction by adapting the characteristics and activity ongoing in the space, along with the dynamic airflow balancing strategy. Energy consumption should not be overlooked despite improving the thermal performance of the airside. Therefore, energy data, such as electricity consumption and cooling supplied to the building, were monitored and recorded throughout both baseline and smart modes. All energy data were recorded using the Kamstrup BTU (cooling energy) and the Schneider Energy Meter (electrical energy). Data during the weekends of the testing period were excluded from the analysis in both modes. Two AHUs (AHU 2-1 and AHU 2-2) were assigned to supply cooling to the space, and the rated power of AHU 2-1 and AHU 2-2 at the full load are 5.7 kW and 3.7 kW, respectively. During different test modes, weather conditions were normalized to ensure that the deviation in the weather conditions was not affected. The pairs of the daily average ambient temperatures during both modes for comparative analysis are presented in **Figure 21**.

Daily electricity consumption of both AHU 2-1 and AHU 2-2 is illustrated in **Figure 22**. During the baseline test, it is observed that the daily electricity consumption of AHU 2-2 ranges between 44 kWh and 70.50 kWh, while the electricity consumption of AHU 2-1 varies between 22.6 kWh and 8 kWh. While conducting the test in the smart mode, as indicated in **Figure 22**, electricity consumption of AHU 2-2
