## 3 Experimental Data
### Hourly Change Coefficient of Outdoor Temperature \(\beta\) Modified
#### 3.2.2 CRITIC Empowerment  
The CRITIC method comprehensively measures the objective weight of an indicator based on the comparative strength of an evaluation indicator and the conflict between the indicators. Considering both the variability in indicators and the correlation between indicators, the larger the number is, the less important it is. A scientific evaluation by fully utilizing the objective attributes of the data is a better objective weighting method than the entropy weight method and standard deviation method. Therefore, based on correlation analysis, to ensure the simultaneous occurrence of meteorological parameters, the CRITIC weight method is adopted to give corresponding weight values for dry bulb temperature, wet bulb temperature, and wind speed, respectively. The values can be used to characterize the simultaneous occurrence of meteorological parameters.  
Firstly, relevant data were obtained--the hourly values of Beijing from 2003 to 2017 in summer (July and August). A total of \(m(m=23,\)808) subjects to be evaluated, dry bulb temperature \(\beta\), wet bulb temperature \(\beta\), and wind speed \(\beta\) were \(n(n=3)\) evaluation indexes, forming the original data matrix \(X\), where \(X_{m,n}\) represents the value of the nth evaluation index of the mth sample, see Formula (6).  
\[\mathrm{X}=\begin{pmatrix}x_{1,1}&\cdots&x_{1,n}\\ \vdots&\ddots&\vdots\\ x_{m,1}&\cdots&x_{m,n}\end{pmatrix} \tag{6}\]  
To eliminate the impact of dimensional inconsistencies on the evaluation results, it is necessary to conduct dimensionless processing of indicators so that all data can be measured by a unified standard. A larger value for the index is better, and the positive index is used, see Formula (7); if the value of the index used is smaller, the inverse index is used, see Formula (8).  
\[\mathrm{x}^{\prime}_{ij}=\frac{x_{ij}-\min(x_{j})}{\max(x_{j})-\min(x_{j})} \tag{7}\]  
\[\mathrm{x}^{\prime}_{ij}=\frac{\max(x_{j})-x_{ij}}{\max(x_{j})-\min(x_{j})} \tag{8}\]  
In calculating the amount of information before, we first calculated the volatility \(S_{j}\), as shown in Formula (9), where \(x_{j}\) is the average for each indicator, and \(S_{j}\) is the standard deviation of the first j an indicator.  
\[S_{j}=\sqrt{\frac{\sum_{i=1}^{n}\left(x_{ij}-\overline{x}_{j}\right)^{2}}{n-1}} \tag{9}\]  
Figure 2: Correlation analysis of \(\beta\) between Beijing (**a**) and Shanghai (**b**).  
The correlation matrix \(R\) of the index should be used to calculate the conflict, see Formula (10).  
\[=\frac{\sum_{j,k=1}^{n}\left(x_{ij}-\overline{x}_{j}\right)\left(x_{ik}- \overline{x}_{k}\right)}{\sqrt{\sum_{j=1}^{n}\left(x_{ij}-\overline{x}_{j} \right)^{2}\sum_{k=1}^{n}\left(x_{ik}-\overline{x}_{k}\right)^{2}}} \tag{10}\]  
Then, the conflict \(A_{j}\) is calculated, as shown in Formula (11), which represents the correlation coefficient between the \(i\)th index and the \(j\)th index. The stronger the correlation with other indicators, the less conflict, and the greater the weight.  
\[A_{j}=\sum_{i=1}^{n}\left(1-r_{ij}\right) \tag{11}\]  
To calculate the amount of information \(C_{j}\), see Formula (12). The larger \(C_{j}\) is, the greater the role of the jth evaluation index in the whole evaluation index system and the greater the corresponding weight.  
\[C_{j}=S_{j}\times A_{j} \tag{12}\]  
Finally, the weight \(W_{j}\) was calculated, as shown in Formula (13). The calculation results are shown in Table 3, and \(\beta_{syn}\) was synthesized by Formula (14).  
\[W_{j}=\frac{C_{j}}{\sum_{j=1}^{n}C_{j}} \tag{13}\]  
\[\beta_{syn}=W_{item}\beta_{tem}+W_{jwet}\beta_{wet}+W_{jves}\beta_{ws} \tag{14}\]