## 3 Experiments and Results Analysis
### Data Preprocessing  
In this subsection, the ISO-NE dataset was chosen as a typical case to elaborate the characteristics of load data in detail. In addition, the GEFCom2012 dataset, which is similar to the ISO-NE dataset, will no longer be discussed. The first step was to determine the number of decomposition models \(k\), the penalty factor \(\alpha\) and the updating step size \(\tau\) using the central frequency observation and the residual index minimization method [55]. In this research, it was convenient to obtain these parameters, i.e., \(k=8\), \(\alpha=419\) and \(\tau=0.19\). Figure 7 shows the decomposition results of load data using VMD. Firstly, the changing trend of the low-frequency component is smoother than that of the original signal, and is roughly consistent with the latter. It indicates that the noise interference would be effectively removed by VMD. Secondly, the influence of seasonal factors on load forecasting needed to be considered in the data preprocessing due to the smoothing and stability of the low-frequency component. Finally, all IMF components showed good periodicity and stability without irregular information, which facilitated the extraction of features in the subsequent stage. Changing trends in electric demand usually present continuous and periodic characteristics without breaking. However, the power consumption is easily influenced by external factors such as seasons, temperature, day types, etc. This results in complex patterns and dynamics in the load data [56]. The relationship between power load and temperature is illustrated in Figure 8, where it shows the distributions of power load and temperature from 1 March 2003 to 8 October 2007. One can clearly see that the power load demand will reach a peak as the temperature becomes very high or low. Thus, temperature has a strong correlation with power load data. Moreover, Figure 9 presents the evolutions of electric consumption in different time horizons. The time horizons of Figure 9a-c are one year from 1 January 2004 to 1 January 2005, one week from 7 to 13 April 2003, and the Christmas period from 22 to 28 December 2003, respectively. One can see from Figure 9a that the power \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Dropout** & **Convolutional Kernel** & **Dilation** & **Output Shape** \\ \hline
1 st 1D-CNN & - & 1 & - & [128; 72; 17] \\
2 st 1D-CNN & - & 4 & - & [128; 84; 14] \\
3 st 1D-CNN & - & 5 & - & [128; 42; 10] \\ TCN & 0.2 & 2 & [1; 2; 4; 8; 16] & [128; 20; 10] \\ LSTM & 0.2 & - & - & [128; 20; 16] \\ SAM & - & - & - & [128; 20; 16] \\ Reshape & - & - & - & [128; 256; - ] \\ Linear & - & - & - & [128; 160; - ] \\ Linear1 & - & - & - & [128; 1, -, - ] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The hyperparameters of each algorithm used in the proposed model. consumption is higher in January, June, July, August and December due to the summer and winter climates. Additionally, the power consumption on weekdays slightly fluctuates and is higher than that on weekends, as shown in Figure 9b. As an important holiday, one can see from Figure 9c that the power consumption during Christmas is significantly lower than at other times. Therefore, the day types such as weekdays and holidays also have important influences on prediction accuracy of the power load. According to the discussions mentioned above, this study selected power demand, holidays, quarters, temperature and weekends as the input variables for load forecasting. One-hot encoding was performed on quarters, holidays, weekends and weekdays. However, a min-max normalization was used to normalize the power load and temperature over a range of (0, 1). The formula of min-max normalization is shown as follows:  
\[\hat{x}=\frac{x-\min(X)}{\max(X)-\min(X)} \tag{22}\]  
where \(X\) is the input series, \(x\) indicates the data to be normalized, \(\hat{x}\) is the normalized data and max (\(\cdot\)) and min (\(\cdot\)) represent the maximum and minimum of the input series, respectively.