# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 5 Experiment and Results
### Results
#### 5.4.1 Analysis of SVLM-FE Forecasting Results  
Several classical forecasting models were chosen for comparison in the experiments of this paper to evaluate the performance of the SVLM-FE hybrid model proposed in this paper, using SVR, MLR, the LSTM network and the GRU network, respectively. Experiments have also been conducted using the SVLM model as a means of comparing the improvement in prediction accuracy before and after error correction in the SVLM-FE model. Their feature vectors were constructed in the same way as the feature vectors in the first stage of the SVLM-FE model, and iterative forecasting was used to predict the load values for 24 moments in the future day. From the analysis of the data in Table 5 and Figure 8, we can conclude that the three criteria of RMSE, MAE and MAPE are optimal for SVLM-FE, and the three criteria of SVR are the worst. Compared to the SVLM model, the RMSE of SVLM-FE decreased by 56.277 MW (30.51 percentage points), MAE decreased by 48.347 MW (32.05 percentage points), and MAPE decreased by 32.64 percentage points. Compared to the classical LSTM model, the SVLM-FE showed a 233.742 MW (a 64.59 percentage point decrease in RMSE, a 165.639 MW (a 61.77 percentage point decrease in MAE) and a 59.78 percentage point decrease in MAPE. Compared to the single GRU model, SVLM-FE's RMSE decreased by 273.383 MW (68.08 percentage points), the MAE decreased by 252.030 MW (71.08 percentage points) and MAPE decreased by 70.80 percentage points. The indicators for MLR and GRU are relatively similar: compared to MLR, SVLM-FE's RMSE decreased by 264.848 MW (67.39 percentage points), the MAE decreased by 255.257 MW (71.34 percentage points) and the MAPE decreased by 71.57 percentage points. In all three metrics, both the comparison with the SSA-VMD-based hybrid model SVLM and the comparison with the single model showed higher forecasting accuracy. In terms of computing time, MLR takes the shortest time of 1.621 s, and has higher forecasting accuracy than SVR, so MLR is used to forecast the low-frequency components in the method proposed in this paper. LSTM requires a longer computing time than GRU, but LSTM has higher forecasting accuracy, so LSTM is used to forecast the high-frequency components in the method proposed in this paper. The forecasting method SVLM-FE proposed in this paper has the longest computing time of 276.158 s, which is due to the fact that the SSA-VMD decomposes the original load data into multiple components, and each component is input into a separate model for forecasting, which increases the computing time. At the same time, the SVLM-FE adds the error correction work of the second fully connected layer to SVLM, so SVLM-FE takes more time than SVLM. Considering that, in practical applications, power plants and grids need more accurate short-term load forecasting to assist the dispatching work of various departments, the research purpose of this paper is to improve the accuracy of short-term power load forecasting, and the computing time of the proposed SVLM-FE model in this paper can meet the time demand in practical applications. Moreover, with the continuous improvement of hardware computing power, the computing time of the proposed model SVLM-FE will be further reduced. To verify the ability of the SVLM-FE model to fit the load curve, a load curve plot was drawn, as shown in Figure 9.  
In Figure 9 it can be seen that the forecasting results of the SVLM-FE model are the closest to the true load values, thus successfully predicting key points in the day at all times. At 11 am, when the peak load is reached, the SVLM-FE model is the closest, and at 1 pm, when the load peaks, and 4 pm the model has the lowest error. The SVLM-FE accurately predicts this load path with less error at the peak and trough; the SVLM is also able to predict the load path for this time period, but with a larger error value than the SVLM-FE.