## 6 Case Analysis and Result
### Feature Selection and Feature Reconstruction  
Based on the existing power load data and meteorological data, a candidate feature set was constructed for feature selection, and the selected features were reconstructed into new input features according to their weights. The candidate feature set consisted of 40 different variables, and each variable collected data for five years from 2018 to 2022. Each feature's data were sampled every 15 min, with 96 data points per day. The five-year data for each feature formed a matrix of 1826 days \(\times\) 96 data points. The MIC values, which measure the correlation between each feature and the load, were calculated based on the columns (different sampling points) of the matrix. The top 12 variables were selected as the original input features for the model based on their MIC values, and each day's load corresponded to a 96 data points \(\times\) 12 dimensions input feature matrix. The input features were then reconstructed using the MIC values as weights and used as the final input for the model. For different types of days, the input features were reconstructed separately for each sampling point to obtain the input matrix I. Table 1 shows the different candidate variables and their descriptions. The 40-dimensional candidate variables included historical load data for each sampling point, the current day's meteorological data, and historical meteorological variables. As the BI-LSTM model already considers the bidirectional impact of the load, trend variables were no longer added to the input feature set for each sampling point. Figure 7: The third type of load curve. According to the method described in this article, after calculating the MIC values, the average MIC value of the 96 sampling points of each feature variable was used as the basis for the feature selection. The calculated average MIC values between the 40-dimensional variables and the daily load are shown in Tables 2-4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Category** & **Feature Name** & **Feature Description** \\ \hline Historical Load & \(L_{t,d}\) & The load before the \(t\)-th sampling point \(d\), \(d\) = 1,2..., 7 \\  & \(T_{t}\) & The temperature at the \(t\)-th sampling point of the day \\  & \(T_{t,d}\) & The temperature at the \(t\)-th sampling point \(d\) days ago \\ Temperature & \(T_{\text{mtx}}\), \(T_{\text{min}}\), and \(T_{\text{av}}\) & Maximum temperature, minimum temperature, and average temperature of the day \\  & \(T_{\text{cmu}}\) & Cumulative temperature \\ Humidity & \(H_{t}\) & The humidity at the \(t\)-th sampling point \\  & \(H_{t,d}\) & The humidity at the \(t\)-th sampling point \(d\) days ago \\  & \(S_{t}\) & The irradiance at the \(t\)-th sampling point of the day \\ Irradiance & \(S_{t,d}\) & The irradiance at the \(t\)-th sampling point \(d\) days ago \\ Precipitation & \(R_{t}\) & The precipitation at the \(t\)-th sampling point of the day \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Alternative features. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & **MIC** & **Variable** & **MIC** & **Variable** & **MIC** & **Variable** & **MIC** & **Variable** & **MIC** \\ \hline \(L_{t,1}\) & 0.825 & \(T_{t,2}\) & 0.675 & \(T_{cam}\) & 0.102 & \(S_{t}\) & 0.532 & \(R_{t,1}\) & 0.187 \\ \(L_{t,2}\) &  
From the table, it can be observed that although the MIC values of various influencing factors differed in magnitude for different types of loads, the main influencing factors were the same. According to the data in the table, the average MIC values were sorted, and the 12 most correlated variables were selected as the input variables before reconstruction. Other variables could be ignored, as their average MIC values were already less than 0.4, indicating their negligible impact on the load.