# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 6 Conclusions  
Accurate short-term power load forecasting is of great importance to the reliable and safe operation of power systems. Usually, the power load undergoes random fluctuations, and there are many complex factors influencing the load. In order to improve the accuracy of short-term load forecasting, assist in a higher level of production scheduling and planning between each department of the power system, and at the same time achieve energy saving and emission reductions, this paper proposes a short-term load forecasting model based on SSA-VMD and feature selection. The following conclusions were obtained experimentally:  
(1) In view of the non-stationary characteristics of the load, the VMD is used to decompose the original load into multiple components. It is proposed to use the mean absolute error as the decomposition quality evaluation criterion, which can be extended to apply to the decomposition problem of data in time series forecasting. In order to solve the problem whereby VMD decomposition relies on the artificial setting of key parameter sets, this paper adopts SSA for VMD optimization, and uses the decomposition evaluation criterion proposed in this paper as the fitness function to construct an SSA-VMD decomposition optimization algorithm. The algorithm reduces the signal loss in the decomposition process and improves the quality of VMD decomposition, thus reducing load volatility, more effectively mining the deep time series features in load data, and laying the foundation for building a high-precision forecasting model;  
(2) After decomposing the original load data via SSA-VMD, the different frequency components were forecasted separately using different methods. The SVLM model constructed in this paper shows a significant decrease in RMSE, MAE and MAPE compared to the single LSTM and MLR models. It proves the effectiveness of constructing a hybrid forecasting model based on SSA-VMD;  
(3) The SVLM-FE model constructed in this paper is based on a two-stage hybrid forecasting idea. The model takes full account of the influence of load trend on the load value of the node to be predicted and the influence of related factors on the load. In the  
Figure 15: Load forecasting results of SVGM–FE and other models in Place 2.  
first stage, only the trend influence of power load is considered, and the forecasting value of each component is predicted. The component forecasting value is input as the feature vector to the second stage for error correction to further reduce the error. In the second stage of the model, the load component forecasts obtained in the first stage, the selected influencing factors using PCC and MIC, and the selected load values of the time nodes using ACF are fused and reconstructed into the feature vector, which is fed into the fully connected layer for forecasting to obtain the final forecasting values. Compared with the SVLM forecasting model without error correction, the forecasting accuracy of SVLM-FE was further improved, with RMSE, MAE and MAPE decreasing by 30.51%, 32.05% and 32.64% respectively. Moreover, SVLM-FE performs well at both peaks and troughs of load, and can respond promptly and accurately to the small peaks and troughs that occur when high-frequency changes in load occur. This proves that the two-stage hybrid forecasting model is effective in error correction. Additionally, the SVGM-FE model built on this idea in this paper shows sub-optimal performance in forecasting. The forecasting method SVLM-FE proposed in this paper has a high forecasting accuracy. It is worth noting that some of the hyperparameters in the model need to be set artificially, such as the setting of hyperparameters in LSTM networks, which relies on a lot of artificial experience. In the future work, a swarm intelligence optimization algorithm can be used to determine some of the hyperparameters in the model. Conceptualization, Q.S. ; methodology, Q.S. ; formal analysis, W.H. and Q.S. ; data curation, Q.S. ; project administration, Y.H. ; software, Q.S. and Y.H.