## 2 Knowledge and Tools of Model Preparation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Site 1**} & \multicolumn{3}{c}{**Site 2**} & \multicolumn{3}{c}{**Site 3**} & \multicolumn{3}{c}{**Site 4**} & \multicolumn{3}{c}{**Average**} \\ \cline{2-13}  & **1-Step** & **2-Step** & **3-Step** & **1-Step** & **2-Step** & **3-Step** & **1-Step** & **2-Step** & **3-Step** & **1-Step** & **2-Step** & **3-Step** & **1-Step** & **2-Step** & **3-Step** & **1-Step** & **2-Step** & **3-Step** \\ \hline
**SVR** & 10.2421 & 3.7990 & 5.4450 & 12.0094 & 7.7489 & 6.625 & 20.2033 & 14.8429 & 13.6510 & 15.4262 & 11.4861 & 2.7214 & 14.4770 & 4.4692 & 8.2482 \\
**GRNN** & 6.7005 & 2.7024 & 6.3444 & 5.8339 & 2.6905 & 4.958 & 5.9300 & 5.5643 & 2.7280 & 3.6094 & 3.3265 & 4.5505 & 5.3139 & 3.5662 & 5.6916 \\
**ENN** & 4.6088 & 0.7096 & 6.6419 & 3.8329 & 0.7633 & 5.4966 & 2.3136 & 4.0046 & 6.6916 & 4.6955 & 3.6181 & 5.0433 & 3.8537 & 2.2739 & 5.9616 \\
**ABMA** & 6.0544 & 4.2488 & 5.8515 & 6.8686 & 3.0821 & 7.4920 & 5.0000 & 2.5516 & 3.3544 & 3.8152 & 2.0078 & 2.4218 & 5.8795 & 2.3291 & 3.5267 \\
**ICEEMDAN-RBF** & 2.2726 & 3.4004 & 1.9043 & 3.9948 & 4.0035 & 1.6098 & 1.6168 & 1.1072 & 2.8530 & 1.6712 & 2.7843 & 4.9304 & 2.3889 & 2.8050 & 2.8423 \\
**ICEEMDAN-EM** & 3.3919 & 4.5582 & 7.6665 & 2.6865 & 4.4544 & 6.8413 & 1.25485 & 5.8037 & 7.1999 & 2.5268 & 6.2938 & 7.5247 & 4.2727 & 5.2776 & 7.3081 \\
**ICEEMDAN-EM** & 4.3495 & 2.5264 & 7.6215 & 3.6637 & 2.2246 & 6.4366 & 1.9592 & 6.5624 & 7.0688 & 4.0948 & 6.2054 & 4.9998 & 7.9204 & 3.9133 & 6.6488 \\
**ICEEMDAN-ARMA** & 5.0671 & 4.5028 & 4.7153 & 3.8896 & 3.8730 & 4.4482 & 1.6676 & 3.3002 & 2.7003 & 1.0344 & 3.7862 & 3.3557 & 3.1372 & 3.9451 & 3.8049 \\
**EMD-based model** & 15.1203 & 12.2324 & 12.1999 & 8.4157 & 6.0308 & 7.8571 & 6.7423 & 3.6974 & 4.5551 & 2.7678 & 3.0427 & 3.3676 & 7.6090 & 6.4418 & 7.0947 \\
**EMD-based model** & 2.7883 & 10.497 & 8.8332 & 8.8833 & 0.8833 & 1.2422 & 5.0220 & 3.7590 & 2.2394 & 2.2709 & 1.9947 & 2.0529 & 5.3102 & 4.0125 \\
**CEEMD-based model** & 8.5483 & 2.0599 & 1.7292 & 6.5568 & 2.0620 & 2.1299 & 5.0188 & 3.0053 & 3.5488 & 8.0726 & 2.3883 & 1.2385 & 5.5009 & 2.2786 & 2.2283 \\
**SSA-based model** & 6.1150 & 1.5486 & 1.5539 & 7.2391 & 1.9472 & 2.7182 & 5.3803 & 2.2793 & 1.5968 & 2.5317 & 2.2860 & 2.5448 & 5.3165 & 2.2655 & 2.0534 \\
**WD-based model** & 0.5509 & 3.1524 & 0.4901 & 2.0042 & 2.9452 & 4.3772 & 1.0097 & 3.6831 & 0.0090 & 1.8452 & 1.3756 & 3.3193 & 1.3526 & 2.7885 & 2.0039 \\ \hline \hline \multicolumn{13}{c}{\({}^{\bullet}\)**m**s refers to 10\% significance level. \({}^{\bullet}\)**= refers to 5% significance level. Considering the significance level \(\ell\), zero hypothesis H\({}_{0}\) indicates the predictive effectiveness of the proposed model and the comparison model are significantly different. The meaning of H\({}_{1}\) indicates the predictive effectiveness of the proposed model and the comparison model are significantly different. The relevant formulas are \(H_{0}:\hat{L}[(\sigma_{1}^{\pi})]=\hat{E}[(\sigma_{1}^{\pi})],H_{1}:\hat{L}[( \sigma_{1}^{\pi})]\
eq\hat{E}[(\sigma_{1}^{\pi})]\). In the formula, \(\hat{L}\) represents the loss function of prediction error. Error,\({}^{\bullet}\) and Error,\({}^{\bullet}\) are the error sequence predicted by selected model. & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistics value of DM test for experimented models. ### Performance Testing of Optimization Algorithms

This section first introduce the parameter settings of BP network and MOEA/D algorithm, then implement the convergence testing of metaheuristic algorithms. #### 5.2.1 Parameter Settings

The artificial intelligence algorithm BP is used to combined the power load results. In BP neural network, the weights and thresholds of input, hidden and output layer occupy an important position in network performance. In order to effectively determine the connection weight and node threshold, we choose the MOEA/D algorithm to optimize its parameters.