## 4 Experiment Simulation and Data Analysis  
### Experimental Environment and Load Data  
The experiment was completed using Python 3.8 n and the open source library vmdpy to write the VMD program. The neural network was implemented in Keras 2.6. The data set contains two parts in this experiment: the GEFCom2012 [36] load data and the actual load data of the power station in the southwest province in China during 1 January 2016 to 31 December 2018. The sampling time of the data set is 15 min.  
Firstly, the load data are classified by the density-based clustering algorithm (DBSCAN). The clustering result is shown in Figure 6. Normal data are divided into one cluster, and the remaining clusters can be regarded as abnormal data. For anomalous data, it will be replaced by the mean of before and after data.  
Figure 5: VMD-GRU-TCN based forecasting process.  
The root mean square error (RMSE) and mean absolute percentage error (MAPE) are used as the evaluation indicators in this paper. The smaller the value of evaluation indicators, the higher the forecast accuracy.  
\[\text{RMSE}=\sqrt{\frac{1}{n}\frac{n}{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{10}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{11}\]  
where \(y_{i}\) represents the actual data, and \(\hat{y}_{i}\) represents the forecast data.  
It is necessary to determine the value of the decomposition quantity to perform VMD decomposition. In general, the value is between 5 and 20 based on the literature. In this experiment, the center frequency value of each component is calculated with the decomposition quantity starting from 5. It can be seen that some important information in the original signal will be filtered when the decomposition quantity is too small, which will result in insufficient precision for the load forecast. However, the center frequencies of adjacent modal components will be too close, adding noise when the decomposition quantity increases.  
Table 1 shows the center frequency of each IMF component when the decomposition quantity is between 5 and 10. It can be seen that the center frequency decreases when \(K<8\), which indicates that the IMF mode may be under-decomposed. When \(K=9\), IMF6 and IMF7 have similar modes, so the modal decomposition number \(K=8\) is suitable in the experiment in this paper. The dynamic profile of different component is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline K & IMF1 & IMF2 & IMF3 & IMF4 & IMF5 & IMF6 & IMF7 & IMF8 & IMF9 & IMF10 \\ \hline
5  
The penalty parameter \(\alpha\) is generally set to be about twice the amount of data, and it will be 2000 in this paper. The other parameters will be set as follows: initial center frequency \(\omega\) = 0, and the convergence parameter is r = \(1\times 10^{-7}\). The first 1000 pieces of data in this data set are decomposed, and the modal components diagram is shown in Figure 6. It can be seen that the modal components decomposed by VMD are smoother than the original data, which is useful and helpful for subsequent neural network training. Among them, the component IMF1 is close to the trend of the original load sequence profile, and it reflects the overall load trend.  
Figure 7: Modal component of VMD.