# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 5 Experiment and Results
### Experiment in Place 2  
To further validate the high accuracy of the forecasting method SVLM-FE proposed in this paper, as well as its stability, experiments were conducted on another dataset and the forecasting results were analyzed. This dataset was from Place 2 in China and spanned the period from 1 January 2013 to 10 January 2015, with a sampling interval of 1 h, 24 sampling points per day and a total of 17,760 pieces of data. This experiment also divided the first 17,736 pieces of load data, with 85% assigned as the training set and 15% as the validation set, and the load data of 10 January 2015 were used as the test set. The trained model was used to predict the load values at 24 momentary points on 10 January 2015, again using single-step forecasting and iterative forecasting. The experimental results of the models on this dataset are analyzed below. From the analysis of the evaluation criteria in Table 7 and Figure 12, we can draw the following conclusions. The three indexes RMSE, MAE and MAPE of SVLM-FE are the best, and the three indexes of SVR are the worst. Compared to the SVLM model, the RMSE of SVLM-FE decreased by 72.845 MW (39.49 percentage points), the MAE decreased by 62.645 MW (40.43 percentage points), and the MAPE decreased by 35.39 percentage points. Compared to the classical LSTM model, the SVLM-FE showed a decrease in RMSE of 259.448 MW (69.92 percentage points), a decrease in MAE of 186.862 MW (66.94 percentage  
Figure 11: Load forecasting results of SVGM–FE and other models in Place 1.  
points) and a decrease in MAPE of 62.37 percentage points. Compared to the single GRU model, SVLM-FE saw a decrease in RMSE of 264.594 MW (70.36 percentage points), a decrease in MAE of 207.105 MW (69.17 percentage points) and a decrease in MAPE of 66.03 percentage points. Compared to the MLR model, SVLM-FE's RMSE decreased by 287.449 MW (72.03 percentage points), its MAE decreased by 273.503 MW (74.77 percentage points) and its MAPE decreased by 74.03 percentage points. In terms of computing time, SVLM-FE still requires the longest time of 273.897 s, which means it is able to meet the time requirements of practical applications. In Figure 13 it can be seen that the forecasting result of the SVLM-FE model is the closest to the real load value, and accurately predicts the key nodes in each moment of the day. The SVLM-FE model predicts the peak and trough values of the load, as well as the small peaks and troughs, with high accuracy. SVLM-FE accurately fits the load curve of the day, and gives a good judgment on the trend of the load and many details. Especially after 13:00 on the day, the SVLM-FE prediction model made an accurate judgment on the rapid change of load, while other models did not make an accurate judgment in the face of this rapid change. As can be seen in Table 8, and Figures 14 and 15, the SVGM-FE model has the highest prediction accuracy and the best fit to the load curve in the comparison of the four prediction models, SVGM-FE, SVGM, GRU and MLR, which further demonstrates the effectiveness of constructing a short-term load forecasting model based on SSA-VMD and feature selection. A comparison with Table 7, and Figures 12 and 13, shows that the forecasting performance of SVLM-FE is better than that of SVGM-FE. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **RMSE/MW** & **MAE/MW** & **MAPE/\%** & **Computing Time/s** \\ \hline
**SVLM–FE** & **111.636** & **92.291** & **1.426** & **273.897** \\ SVLM & 184.481 & 154.936 & 2.207 & 244.941 \\ LSTM & 371.084 & 279.153 & 3.790 & 83.172 \\ GRU & 376.590 & 299.396 & 4.198 & 64.352 \\ MLR & 399.085 & 365.794 & 5.490 & 1.636 \\ SVR & 808.662 & 642.188 & 11.435 & 14.842 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: SVLM–FE and other models’ evaluation criteria of Place 2. Figure 12: Comparison of the evaluation criteria of SVLM–FE and other models in Place 2. Figure 14: Comparison of the evaluation criteria of SVGM–FE and other models in Place 2. Figure 13: Load forecasting results of SVLM–FE and other models for Place 2.