## 3 Experimental Data
### Hourly Change Coefficient of Outdoor Temperature \(\beta\) Modified
#### 3.2.4. LSTM Long Short-Term Memory Network Model Construction and Prediction
According to the prediction results of LSTM, the variation trend and amplitude of \(\beta\) predicted in the Beijing group data were roughly the same as those in the specification, and the peak appeared 2 h earlier, and the data from 7:00 to 15:00 were higher than those in the specification, while the predicted value of LSTM in the Shanghai group data was higher than that of K-means. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{.r^{2}}\) & \(\beta_{syn}\) & \(\beta_{wet}\) & \(\beta_{ws}\) \\ \hline Beijing & 0.96 & 0.89 & 0.89 \\ Shanghai & 0.93 & 0.90 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(r^{2}\) values for Beijing and Shanghai. Figure 8: Fitting degree of \(\beta_{syn}\), \(\beta_{wet}\), and \(\beta_{ws}\) in Beijing (**a**) and Shanghai (**b**). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hour** & **1** & **2** & **3** & **4** & **5** & **6** \\
**Beijing** \(\hat{\beta}_{tem}\) & \(-0.25\) & \(-0.34\) & \(-0.40\) & \(-0.42\) & \(-0.41\) & \(-0.37\) \\ \hline
**Hour** & **7** & **8** & **9** & **10** & **11** & **12** \\
**Beijing** \(\hat{\beta}_{tem}\) & \(-0.31\) & \(-0.24\) & \(-0.17\) & \(-0.05\) & \(0.07\) & \(0.21\) \\ \hline
**Hour** & **13** & **14** & **15** & **16** & **17** & **18** \\
**Beijing** \(\hat{\beta}_{tem}\) & \(0.34\) & \(0.44\) & \(0.48\) & \(0.47\) & \(0.41\) & \(0.32\) \\ \hline
**Hour** & **19** & **20** & **21** & **22** & **23** & **24** \\
**Beijing** \(\hat{\beta}_{tem}\) & \(0.22\) & \(0.10\) & \(-0.03\) & \(-0.15\) & \(-0.26\) & \(-0.35\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Predicted hourly variation coefficient of dry bulb temperature in Beijing. Figure 9: Predicted results of \(\beta_{sym}\), \(\beta_{wet}\), and \(\beta_{us}\) in Beijing (**a**) and Shanghai (**b**). Summer Air Conditioning Calculated Temperature and Summer Air Conditioning Calculated Daily Average Temperature Correction  
According to the \"Guidelines for Indoor and Outdoor Parameters for HVAC Design of Civil Buildings\" [56], the design day is mainly selected by dry bulb temperature, and the calculated temperature of air conditioning in summer and the daily average temperature of air conditioning in summer are selected (see Table 9). When selecting the calculated temperature of summer air conditioning, the data of the 151 st, 751 st, and 1501 st groups were selected in descending order according to the hourly value of dry bulb temperature, and then all the data within the design value of \(\pm\)0.5 \({}^{\circ}\)C were selected to take the mean value, corresponding to the calculated temperature of summer air conditioning that does not guarantee the annual average of 10 h, 50 h, and 100 h. When selecting summer air conditioning to calculate the daily average temperature, the data of the 16 th, 76 th, and 151 st groups were selected in descending order according \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hour** & **1** & **2** & **3** & **4** & **5** & **6** \\
**Shanghai** \(\hat{\beta}_{tem}\) & **0.17** & 0.08 & 0.03 & 0.03 & 0.06 & 0.13 \\ \hline
**Hour** & **7** & **8** & **9** & **10** & **11** & **12** \\
**Shanghai** \(\hat{\beta}_{tem}\) & **0.23** & 0.36 & 0.51 & 0.67 & 0.82 & 0.96 \\ \hline
**Hour** & **13** & **14** & **15** & **16** & **17** & **18** \\
**Shanghai** \(\hat{\beta}_{tem}\) & **1.08** & 1.60 & 1.20 & 1.21 & 1.17 & 1.10 \\ \hline
**Hour** & **19** & **20** & **21** & **22** & **23** & **24** \\
**Shanghai** \(\hat{\beta}_{tem}\) & **1.00** & 0.88 & 0.75 & 0.62 & 0.50 & 0.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Predicted hourly variation coefficient of dry bulb temperature in Shanghai. Figure 10: Predicted value \(\hat{\beta}_{tem}\) Comparison of \(\beta_{syn}\), \(\beta_{wet}\), and \(\beta_{us}\) in Beijing (**a**) and Shanghai (**b**).