## 3 Experimental Data
### Hourly Change Coefficient of Outdoor Temperature \(\beta\) Modified
#### 3.2.4. LSTM Long Short-Term Memory Network Model Construction and Prediction  
LSTM was designed to solve the problem of long-term dependence, which is common in general recursive neural networks; that is, it can effectively transmit information in a long time series without causing previously useful information to be ignored. Based on the K-means clustering analysis, the LSTM algorithm was used to build a prediction model. Thus, 24 h was taken as the length of a set of sample data, and 80% training set and 20% of the prediction set data were set to train and verify the model. Combined with the weight calculation results in Tables 4 and 5, the future, \(\beta_{syn}\), \(\beta_{wet}\), and \(\beta_{ws}\) were predicted, and the \(\beta_{tem}\) was calculated via Formula (14). Since the training set accounts for 80% and the test set accounts for 20%, in the prediction model based on the data for 15 years, we believe that the prediction of data changes in the next 3 years has strong confidence. The LSTM cell structure and prediction model structure are shown in Figure 6, where the red circle represents the operation on the vector (para operation), the yellow rectangle represents a neural network layer, and the characters represent the activation function used by the neural network layer. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hour** & **1** & **2** & **3** & **4** & **5** & **6** \\
**Beijing** \(\beta_{tem}\) & \(-0.67\) & \(-0.74\) & \(-0.78\) & \(-0.79\) & \(-0.76\) & \(-0.69\) \\ \hline
**Hour** & **7** & **8** & **9** & **10** & **11** & **12** \\
**Beijing** \(\beta_{tem}\) & \(-0.58\) & \(-0.42\) & \(-0.21\) & \(0.04\) & \(0.29\) & \(0.52\) \\ \hline
**Hour** & **13** & **14** & **15** & **16** & **17** & **18** \\
**Beijing** \(\beta_{tem}\) & \(0.71\) & \(0.83\) & \(0.87\) & \(0.84\) & \(0.76\) & \(0.63\) \\ \hline
**Hour** & **19** & **20** & **21** & **22** & **23** & **24** \\
**Beijing** \(\beta_{tem}\) & \(0.48\) & \(0.31\) & \(0.16\) & \(0\) & \(-0.13\) & \(-0.26\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hourly variation coefficient of dry bulb temperature in Beijing. Figure 5: Proportions of various types 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** \\
**Shanghai** \(\beta_{tem}\) & \(-0.13\) & \(-0.16\) & \(-0.17\) & \(-0.18\) & \(-0.18\) & \(-0.17\) \\ \hline
**Hour** & **7** & **8** & **9** & **10** & **11** & **12** \\
**Shanghai** \(\beta_{tem}\) & \(-0.13\) & \(-0.05\) & \(0.07\) & \(0.23\) & \(0.43\) & \(0.62\) \\ \hline
**Hour** & **13** & **14** & **15** & **16** & **17** & **18** \\
**Shanghai** \(\beta_{tem}\) & \(0.80\) & \(0.93\) & \(0.99\) & \(0.96\) & \(0.86\) & \(0.71\) \\ \hline
**Hour** & **19** & **20** & **21** & **22** & **23** & **24** \\
**Shanghai** \(\beta_{tem}\) & \(0.53\) & \(0.34\) & \(0.18\) & \(0.06\) & \(-0.04\) & \(-0.10\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hourly variation coefficient of dry bulb temperature in Shanghai. Figure 9 shows the predicted results of \(\beta_{syn}\), \(\beta_{wet}\), and \(\beta_{ws}\). In the figure, blue is the actual value, and green is the predicted value. Tables 7 and 8 are used to calculate \(\hat{\beta}_{tem}\) in Beijing and Shanghai based on the equation. A comparison between the revised value based on K-means clustering and the predicted value of LSTM model and the standard value is shown in Figure 10.  
According to the comparison results, compared with \(\beta\) in the specification, the amplitude of \(\beta\) obtained by the K-means method is larger in the Beijing area. In the data for the Shanghai group, except for the data at 9:00 am, \(\beta\) obtained by the K-means method is higher than \(\beta\) in the specification.