###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**Advantage**} & **Disadvantage** \\ \hline \multirow{4}{*}{**Statistical models (AR, ARIMA, fractional-ARIMA.)**} & \(\bullet\) & Consider information from historical data & \(\bullet\) & Camnot mine the nonlinear law of data \\  & \(\bullet\) & High calculation efficiency & \(\bullet\) & Stable data only \\  & \(\bullet\) & Few adjustable model parameters & \(\bullet\) & Assuming the interference sequence is white noise \\  & \(\bullet\) & Can mine the linear law of data effectively & \(\bullet\) & is necessary \\ \hline \multirow{4}{*}{**Artificial neural network**} & \(\bullet\) & Can fit the non-linear patterns of data, and has good generalization ability and & \(\bullet\) & The calculation process is complex \\  & & self-learning ability & \(\bullet\) & High requirements on the number of training samples \\ \hline \multirow{2}{*}{**Hybrid model**} & \(\bullet\) & Integrate the advantages of each single model & \(\bullet\) & Improved spaces in each part of the model still exist \\  & \(\bullet\) & Improvement of prediction accuracy & \(\bullet\) & Non-linear patterns of data cannot be further mined \\ \hline \multirow{4}{*}{**Quantile regression**} & \(\bullet\) & Be able to deal with heterogeneity & \(\bullet\) & High training data set requirements \\  & \(\bullet\) & High tolerance for outliers & \(\bullet\) & Large amount of calculation \\  & \(\bullet\) & Consider the whole distribution & \(\bullet\) & Results are easily discarded \\  & \(\bullet\) & Ability to capture tail features of distribution & \(\bullet\) & Poor performance in handling large samples \\ \hline \multirow{2}{*}{**Bootstrap methods**} & \(\bullet\) & Avoid possible discarding in quantile regression & \(\bullet\) & Poor performance in handling large samples \\  & \(\bullet\) & Very effective when handling small samples & \(\bullet\) & Large amount of computation \\ \hline \multirow{2}{*}{**Kernel density estimation**} & \(\bullet\) & Fast construction prediction interval & \(\bullet\) & Strict distribution assumption \\ \hline \multirow{2}{*}{**LUBE**} & \(\bullet\) & Avoid the data distribution hypothesis & \(\bullet\) & The objective function is complex \\  & \(\bullet\) & High calculation efficiency & \(\bullet\) & Traditional mathematical methods cannot optimize \\  & \(\bullet\) & Model coefficient is easy to adjust & & the objective function \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of point prediction and interval prediction models. According to the review of the above literature and methods, the major contribution of this article is to design a hybrid double prediction system, including two parts: point forecasting and interval forecasting, which make up for the shortcomings of the existing research. Specifically, the double prediction system includes the preprocessing module based on Improved Complete Ensemble Empirical Mode Decomposition (ICEEMDAN), the prediction module based on nonlinear combination model, the interval prediction module and the evaluation module. As a new signal processing technology, ICEEMDAN decomposes and reconstructs the power load sequence to get a clear time sequence. The nonlinear combination model is an effective prediction model proposed in this study. Among it, Extreme Learning Machine (ELM) [36], RBF, Elman Neural Network (ENN) and ARIMA [37] are selected as the basic models of the combination model, and the prediction results of these four models are nonlinear aggregated by BP [38] neural network. BP network is very sensitive to the selection of parameters, which directly affects the validity of point prediction and interval prediction. Therefore, in order to find the optimal parameters in BP model, Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) is developed effectively. Finally, an interval prediction method based on fuzzy clustering is established, which does not need the hypothesis of distribution and model.