**3.3 Details of the testbed located at the Singapore institute of technology**

The pilot tests were conducted on one floor of the Singapore Institute of Technology (SIT) campus located at *Dover Drive*. The *testbed occupying* 11,000 square feet is located at *level two*, compr*isi*ng open offices, meeting rooms, a pantry, an AHU room, and washrooms. The space is fully air-conditioned except for the washrooms. The details of the testbed are tabulated in **Table 1**. **Figure 8** illustrates the layout of the


**Table 1.** *Details of the pilot in the case study.*

#### **Figure 8.**

*Plan view of the office space at level two of the University Service Centre at the Singapore Institute of Technology.*

office located at the Singapore Institute of Technology, and the spaces are segregated into two zones, namely, *block B and block* C. While block B's cooling requirements are supplied by air handling unit 2-1 (AHU 2-1), block C is served by AHU 2-2. The temperature set point of the space was 24°C throughout the day.

The key issue with the air conditioning system is the thermal comfort of the occupants stationed in the space. From the occupant's feedback, it is discovered that there are areas with hotspots and overcooling within the office. On occasion, occupants feel uncomfortably hot or extremely cold in the office. It is observed that some of the diffusers are covered with masking tape to restrict airflow. The AHU VFD and actuator set points are changed manually based on complaints from the occupants. In addition, due to the work nature of the academic staff, they are frequently required to leave and return to their desks for lectures and classes, resulting in a dynamic heat map. Therefore, there is a need to resolve the issue without compromising the energy efficiency of the air conditioning system. The primary objective of this study is to develop an intelligent solution to resolve thermal comfort issues without compromising energy efficiency while eliminating the conventional reactive approach to control systems.

The proactive solution would account for the varying occupant numbers throughout the day while creating an optimal condition for their staff. Despite the abundant availability of smart sensors, which work on room levels, an AI algorithm was developed and tested at SIT staff office, along with the collaboration between SIT and Singapore Digital Pte. Ltd., a sole distributor of 75F smart innovation solutions in Singapore.

Dynamic air balancing and chilled water balancing, along with proactive AI predictive control, are the essential components of this study to achieve energy savings while maintaining the thermal comfort of the occupants. In order to optimize the air distribution efficiency, two big spaces, as indicated in **Figure 8**, are divided into 43 micro-zones, as indicated in **Figure 9**. Each meeting room is treated as a micro-zone. Each micro-zone in the open office is equipped with an IoT smart sensor that measures the key parameters, such as temperature, relative humidity, CO2 concentration, and occupancy status, using a passive infrared sensor (PIR), enabling an accurate representation of the local heat load. Moreover, as illustrated in **Figure 10**, smart

**Figure 9.** *Two zones are split into 43 micro-zones.*

**Figure 10.** *Architecture of proactive AI control system.*

#### *Application of Artificial Intelligence in Air Conditioning Systems DOI: http://dx.doi.org/10.5772/intechopen.107379*

dampers are retrofitted between each supply air diffuser/VAV duct and flexible air duct to modulate the amount of airflow based on the actual heat load. This facilitates micro-zonal control, allowing better comfort and energy savings. In addition, the opening of the smart damper is controlled based on the local heat load. The IoT smart sensors and dampers are wirelessly connected smart nodes which communicate wirelessly with central control units (CCUs). A cloud-based proactive AI control powers the algorithm behind the control units, and the architecture of the proactive AI control system is shown in **Figure 10**. The CCU sends minute-by-minute data regarding temperatures in various building parts to cloud servers. Every night, these servers run proprietary algorithms to crunch the historical data and develop a thermal model of the building. They then predict the thermal load in each part of the building for the next day based on the forecasted weather.

### *3.3.1 Dynamic airflow balancing*

**Figure 11** illustrates the dynamic airflow balancing, which optimizes the cold air supply to the most required space. The smart dampers' opening at micro-zones with cold spots is adjusted to accommodate the cooling needs in that micro-zone. Due to the changes in the opening of the smart dampers, the static pressure in the duct increases. However, cold air is circulated to space (hotspots), which requires more cooling, restoring the static pressure. Therefore, supply air fan speed is not ramped up to supply more cooling to the hot space; instead, air balancing between cold and hotspots progresses, resulting in energy savings in AHUs. This means that the AI control is able to identify which zones require more cooling by deploying dynamics zone *priority* (DP). Since the system enables minute-*by-minute data* collection, real-time DP is performed prior to executing the next control phase. Air balance is performed using a weighted average of the local heat load, as shown in Eq. (1) (**Figure 12**).

$$
\dot{Q}\_{weighted} = \frac{\sum\_{i=x\_1}^{x\_n} \left( \dot{Q}\_{i,j} \ge DP\_i - \dot{Q}\_{i,k} \ge DP\_i \right)}{\sum\_{i=x\_1}^{x\_n} DP\_i} \tag{1}
$$

**Figure 11.** *Illustration of dynamics of airflow balancing.*

where *Q*\_ *weighted* and *Q*\_ denote the weighted average of heat load and local heat load in the space, respectively, DP refers to dynamics zone priority, i denote the number of zones in the space, j represents the zones with overcooling, and k is for the zones requiring more cooling. Then, air balancing for the micro-zones is carried out based on the DP value, which identifies how far the current temperature is away from the set point. The AI algorithm identifies and optimizes the air balancing, resulting in the evenly distributed cold air supply to each of the micro-zones, and AHUs can still be operated at a lower speed as compared to the conventional control system because the speed of the AHUs is adjusted, as shown in **Figure 13**, based on the weighted average of micro-zones after air balancing is carried out. In addition, fresh air optimization is enabled by incorporating a modulating damper in the fresh air duct. The bandwidth of the opening of the fresh air damper ranges from 20 to 100% based on CO2 concentration in the space, enabling minimal fresh air usage when the indoor CO2 level is about 900 ppm.

**Figure 13.** *Block diagram that shows smart damper and AHU VFD relational control.*
