# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 4 Experiments
### Ablation Experiments  
In order to verify the prediction effect produced by each step of the combined model proposed in this paper, we will build the TCN model, the VMD-TCN model, the VMD-TCN-Attention model, the VMD-TCN-TPA model, the final proposed EPT-VMD-TCN-TPA model, and the EPT-VMD-FE-TCN-TPA recombination model in this paper, respectively. Among them, these models are briefly described as follows:  
1. TCN model [27]: This model is a more advanced model in recent years, which not only can effectively extract the features of nonlinear time series data but also can effectively solve complex problems such as gradient explosion and gradient disappearance during model training. 2. VMD-TCN model: The load data are firstly decomposed using VMD, after which the dataset is trained and predicted using the TCN network. 3. VMD-TCN-Attention model: A general attention mechanism is added to the VMD-TCN model. 4. VMD-TCN-TPA model: Based on the VMD-TCN model, the prediction capability is improved by introducing a temporal pattern attention mechanism, which is especially suitable for handling multi-task temporal prediction. 5. EPT-VMD-TCN-TPA model: Our proposed final model. Before using the VMD-TCN-TPA model, the EPT method, which extracts the trend components of the load data, is first introduced. 6. EPT-VMD-FE-TCN-TPA model: This model adds a recombination layer to the proposed EPT-VMD-TCN-TPA model in this paper and merges the similar components of the intermediate processes according to FE values. The prediction effects of each of the above comparison models on the test set are shown in Figure 17 (only one day is shown), and the prediction error evaluation index data and fit effect data of each model are shown in Table 8.  
As can be seen in Table 8, the EPT-VMD-TCN-TPA forecasting model proposed in this paper has the smallest MAPE, RMSE, and MAE compared with other comparative models, which are 1.25%, 110.2692 MW, and 82.3180 MW, respectively. Furthermore, the EPT-VMD-TCN-TPA model has the highest R2 value, 0.9955. These evaluation indexes reflect that the EPT-VMD-TCN-TPA forecasting model proposed in this paper can achieve accurate forecasting results for short-term load forecasting. The TCN model, as the most basic model, has the worst forecasting effect and takes the shortest time. From the TCN, VMD-TCN, VMD-TCN-Attention, and VMD-TCN-TPA until the EPT-VMD-TCN-TPA model, its prediction accuracy improves step by step, which also indicates that each step in the EPT-VMD-TCN-TPA model and the combination together will be beneficial for the improvement of prediction precision. Compared with the TCN model, the MAPE, RMSE, and MAE of the EPT-VMD-TCN-TPA model are reduced by 55.99%, 55.52%, and 54.61%, respectively, which indicates that the EPT-VMD-TCN-TPA model has a high predictive fine-reading. As shown in Figure 17, the EPT-VMD-TCN-TPA model has a relatively accurate prediction effect both in the wave peak and the trough. In terms of model running efficiency, the EPT-VMD-TCN-TPA model proposed in this paper takes 443.232 s, which is the longest time among all the models. This is because the decomposition of the load using EPT and VMD makes the number of prediction components increase, which leads to the long running time of the model. In real life, the accuracy of the prediction results will be given priority, and with the continuous upgrading of hardware and the application of parallel computing, the running efficiency of the EPT-VMD-TCN-TPA model proposed in this paper will not be a problem.