## 2 Knowledge and Tools of Model Preparation
In addition to the proposed hybrid prediction model, the single model with high prediction accuracy is ARIMA and ENN. Therefore, we choose ARIMA and ENN as the model used in combination models, and the same circumstance as RBF. ARIMA is a linear model, therefore it can show that the power load data has certain linear characteristics, so it is a wise choice to take ARIMA into account in the proposed model. Additionally, although the prediction accuracy of BP is not shown, the experimental results show that it has relatively good prediction effect, so BP is selected as the model used in nonlinear combination. #### 4.4.2 Experiment II: Tests of Data Preprocessing Methods

This experiment is aiming at comparing the effectiveness of ICEEMDAN selected in this system with other common data preprocessing methods, which includes EMD, EEMD, CEEMD, SSA and WD. Therefore, the point forecasting models on the basis of different data preprocessing methods are EMD-based model, EEMD-based model, CEEMD-based model, SSA-based model and WD-based model. These models only use different decomposition method in the data preprocessing stage. Through the experiment, we can test whether the proposed prediction model is reasonable, and can also find the best method to remove the noises to improve the prediction effectiveness. The results obtained by models using different data preprocessing approaches are shown in Table 6. Figure 5 shows a clearer and more intuitive comparison. The conclusion can be drawn that the model on the basis of the ICEEMDAN decomposition technology has much better performance than other decomposition based prediction models. Say concretely, for site 2, the MAPE value of ICEEMDAN-based proposed model is 0.7373%, 1.8561% and 1.8413% for three steps, which is 0.1 to 4 percentage points higher than that of EMD-based model, EEMD-based model, CEEMD-based model, SSA-based model and WD-based model. Of all the benchmark models, EMD-based model is the worst. Compared with other models, the MAE, RMSE, IA and DC of the one to three step prediction of the developed model are also improved to different extent, which further shows the superiority of the data preprocessing method selected by this hybrid model. **Remark 2**. : _Experiment I and experiment II focus on proving the advantages of the proposed point forecast module, and the results show that the designed point forecasting model is a very promising power load forecasting method. It can also prove that the combination of data preprocessing technology, optimization algorithm and nonlinear combined method could successfully solve the difficulties of load prediction through appropriate prediction methods._
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Site \(\bullet\)**}} & \multicolumn{4}{c}{**One-Step**} & \multicolumn{4}{c}{**Two-Step**} & \multicolumn{4}{c}{**Three-Step**} \\ \cline{2-13}  & **Model** & **MAPE** & **MAE** & **RMSE** & **IA** & **DC** & **MAPE** & **MAE** & **RMSE



### Results and Analysis of Interval Prediction (Experiment III)

Base on the point power load forecasting, the probability interval forecasting could show more load information. In this part, we develop a method based on fuzzy clustering, which carries out interval forecasting on the basis of point forecasting. In addition, four datasets are applied in this experiment. To verifying the ability of the designed interval forecast module, we use the all the compared model of point prediction, and also use multi-step prediction to verify the interval predicted results. The results of the interval predicted model and other models are shown in Table 7. Due to the limited space, we only display the results of site 2 and site 3. We set the confidence interval to 90% to assess the effectiveness of the interval predicted model. (1) For site 2, the best values of all indexes in all models are obtained by the proposed prediction model.