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

**Figure 22.** *Daily electricity consumption of AHU2-1 and AHU2-2 during the baseline and smart modes.*

fluctuates between 37.2 kWh and 18.6 kW and that of AHU 2-1 peaks at 14.2 kWh, and its minimum value is 7 kWh. The average electricity consumption of AHU2-1 and AHU 2-1 during the baseline mode was 54.67 kWh and 16.07 kWh, respectively. However, the average electricity consumption of both AHUs during the smart mode was 24.5 kWh [AHU2-1] and 10.43 kWh [AHU 2-2]. Therefore, the total electricity consumption of AHU 2-2 is cut from 492 kWh in the baseline test to 220.5 kWh in the smart test, whereas the electricity consumption of AHU 2-1 is lowered by 50.7kWh from 144.6 kWh to 93.9 kWh, as demonstrated in **Figure 23**. The results also highlight that electrical energy savings in AHU 2-2 are about 55%, while AHU 2-1 saves approximately 35% of electricity usage when the smart mode is activated. While

**Figure 23.** *Electrical energy savings at different AHUs between baseline and smart modes.*

presenting electricity consumption analysis, cooling energy consumption is also investigated in this case study. Due to some constraints in the installation of BTU meters for each AHU to measure the cooling energy, only one BTU meter was installed at the common chilled water header to log the chilled water flow rates. Therefore, the cooling energy consumption (kWh) is calculated as follows:

$$
\dot{Q}\_{coiling} = \dot{m}\_{chw} \mathbf{C} p\_{chw} (T\_R - T\_S) \mathbf{x} \mathbf{N}\_{op} \tag{2}
$$

In Eq. (2), the first parameter *Q*\_ *cooling* represents cooling energy consumption in kWh; the second parameter *m*\_ *chw* denotes mass flow rates of chilled water in kg/s, the third parameter *Cpchw* is the specific heat capacity of chilled water in kJ/kg�K, T represents temperature in °C, and *Nop* is the operation time in hours. The subscript R and S represent return and supply, respectively. **Figure 24** illustrates the accumulative cooling energy consumption for the baseline and smart mode tests. The smart mode is observed to consume 29% less cooling than the baseline test while maintaining thermal comfort in the space, because the cooling requirements in the office are significantly reduced by optimizing the supply airflow rates to facilitate the cooling load in each micro-zone. The results show that airside energy consumption can be reduced by as high as 50% of electricity consumption in AHUs, while the reduction in cooling supply to the office was also approximately 29%. The results also assure that reduction in the cooling supply and electrical energy consumption do not compromise the thermal comfort of the office.

This case study demonstrated the application of AI-oriented control in airside air conditioning systems to resolve typical issues, such as thermal comfort and high energy consumption due to overcooling and undercooling, in open offices. It also highlights that the improvement on the airside also contributes to the reduction of electricity consumption of the fans, resulting in minimizing the waste energy as compared to the baseline control system while cooling required in the offices is also optimized.

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

**Figure 24.** *Cooling energy consumption during baseline test and smart test.*
