## 2 Research Background
### Dynamic FCU Temperature Settings  
An FCU distributes cold air produced by a chilled water system to an indoor area. In such an area, the demand for cold air can be determined by observing a user's adjustments of FCU temperature settings. The Daily Discomfort Score (DDS) is developed to assess demand response (DR) and thermal comfort in a building. Baseline and preconditioning scenarios are used to demonstrate the effectiveness of the Daily Discomfort Score in evaluation thermal comfort and demand response HVAC system set-point control [PERSON] et al. (2000). The ASHRAE Standard 55-1992 states that the comfort zone for summer conditions, air temperature to be between 23 \({}^{\circ}\)C and 26 \({}^{\circ}\)C and relative humidity between 20% and 60%. Surveys of human thermal response in South East Asia produce the Auliciems' equation for estimating thermal neutrality on the basis of the mean monthly dry bulb temperature [24]. In the proposed method, a regression model that includes outdoor temperature and indoor sensing temperature as variables is used to calculate the indoor HVAC temperature:  
\[Ta=\alpha\times(Ti)+\beta\times(To)+c \tag{1}\]  
where \(Ta\) is the indoor HVAC temperature, \(Ti\) is the indoor sensing temperature, \(To\) is the outdoor temperature, \(\alpha\) and \(\beta\) are regression coefficients, and \(c\) is a constant. In Equation (1), the range of the indoor HVAC temperature is not constrained; hence, the temperature may be excessively high when the indoor sensing temperature or outdoor temperature is markedly high. To maintain indoor temperature quality, the indoor HVAC temperature can be corrected using tan-1 and limited to the indoor comfortable range of 23-26 \({}^{\circ}\)C. The indoor comfort temperature (\(Tc\)) can be expressed as follows:  
\[Tc=[\tan^{-1}(Ta-24.5)]+24.5 \tag{2}\]  
An experiment conducted in this study revealed that when \(Top\) was adjusted, the indoor environmental temperature normally required 10-20 min to approach \(Top\). To maintain indoor temperature quality and meet the cooling air request, the proposed algorithm based on dynamic FCU temperature settings automatically sends an HVAC setting temperature (\(Ts\)) that is within the indoor comfort range every 30 min and calculates the temperature difference between \(Tc\) and \(Top\) to determine the indoor thermal comfort (\(ITC\)), as presented in Equation (3):  
\[ITC=Tc-Top \tag{3}\]  
The derived \(ITC\) is then used to correct \(Ts\) to achieve indoor comfort. The conditions for determining \(ITC\)' are as follows:  
\[ITC^{\prime}=\left\{\begin{array}{c}+1\sim-1,\;Ts=\;Ts+[\Delta T\times(C^{ \prime})]\\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; \;\;\;  
index is required to enable the chilled water system to adjust RT supplies. A conventional FCU provides only _Ts_ and _Ti_ information. By contrast, this study proposes a technique that integrates the difference between _Ts_ and _Ti_ with the FCU wind speed to obtain a comfort weight (W) for chilled water system control. Figure 1 illustrates the system framework.  
This system is based on a multiple-input single-output (MISO) fuzzy controller, where the output is W. The MISO controller is described as follows:  
1. The change in temperature settings (F1) is used to understand the required RT for a user, as presented in Equation (5). A negative F1 value (e.g., a change in temperature setting from 26 \({}^{\circ}\)C to 24 \({}^{\circ}\)C) indicates that the user feels hot, whereas a positive F1 value indicates that the user feels cold: \[F_{1}=T\mathrm{s}\left(t\right)-T\mathrm{s}\left(t-1\right)\] (5) In Equation (5), t is the sampling time. 2. The target temperature error (F2) is the difference between Ti and Ts, and it represents indoor cooling level, as presented in Equation (6). A negative F2 value (e.g., an actual indoor temperature of 24 \({}^{\circ}\)C and set temperature of 26 \({}^{\circ}\)C) signifies overcooling, whereas a positive F2 value signifies undercooling.