## 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 c} \hline \hline  & & \multicolumn{4}{c}{**One Step**} & \multicolumn{4}{c}{**Two-Step**} & \multicolumn{4}{c}{**Three-step**} \\ \cline{2-13}
**Site** & **Model** & **MAPE** & **MAE** & **RMSE** & **IA** & **DC** & **MAPE** & **MAE** & **RMSE** & **IA** & **DC** & **MAPE** & **MAE** & **RMSE** & **IA** & **DC** \\ \hline  & SVR & 1.5099 & 156,7013 & 187,9195 & 0.9944 & 0.8202 & 2.5219 & 210.3356 & 258.5180 & 0.9894 & 0.7474 & 3.0868 & 262.812 & 341.3188 & 0.9817 & 0.614 \\  & GRNN & 1.2373 & 107,3457 & 140.2399 & 0.9964 & 0.9215 & 2.1193 & 183.9606 & 244.3408 & 0.9885 & 0.8632 & 3.1821 & 276.0676 & 368.4048 & 0.9727 & 0.7778 \\  & RBF & 1.3330 & 120,048 & 162,040 & 0.9551 & 0.9508 & 2.5730 & 227.9829 & 303.8281 & 0.9819 & 0.8053 & 5.7991 & 581.6876 & 0.71910 & 0.8528 & 0.6143 \\  & ELM & 0.9860 & 75,3729 & 107.529 & 0.9981 & 0.9476 & 2.5427 & 246.4413 & 372.8275 & 0.97733 & 0.8040 & 6.7093 & 518.3696 & 0.66388 & 0.9529 & 0.7570 \\
**Site 1** & ENN & 0.9387 & 1466,186 & 1856,1947 & 0.9979 & 0.9476 & 2.1680 & 188.5757 & 246.8761 & 0.9886 & 0.8053 & 4.6199 & 222.2378 & 378.4269 & 0.9727 & 0.6641 \\  & AHMMA & 1.2105 & 1089,109 & 137.2456 & 0.9955 & 0.9058 & 2.5099 & 224.8970 & 200.2767 & 0.9886 & 0.7268 & 24.2883 & 220.2062 & 280.2499 & 0.9847 & 0.0727 \\  & ICEEMDAN-RBF & 0.7367 & 65.4720 & 49.1611 & 0.9984 & 0.9746 & 2.2996 & 196.5771 & 287.7452 & 0.9862 & 0.8368 & 5.2147 & 415.0091 & 0.9532 & 0.8148 & 0.7920 \\  & ICEEMDAN-ELM & 0.8543 & 74.4721 & 98.4847 & 0.9983 & 0.9244 & 2.7422 & 299.953 & 197.9640 & 0.8610 & 0.7579 & 5.6894 & 494.3496 & 366.1800 & 0.9215 & 0.6190 \\  & ICEEMDAN-ENN & 0.9216 & 81.0992 & 105.1646 & 0.9803 & 0.9139 & 1.8271 & 199.9742 & 204.5320 & 0.9923 & 0.8579 & 0.8038 & 201.6713 & 0.83107 & 0.9788 & 0.6984 \\  & ICEEMDAN-ARMA & 1.0469 & 64.0123 & 4123,499 & 0.9973 & 0.9256 & 2.3377 & 229.7987 & 281.9299 & 0.9857 & 0.8796 & 2.9985 & 2156.069 & 282.7683 & 0.9586 & 0.7831 \\  & Proposed Model & **0.6857** & **574.791** & **80.1018** & **99.898** & **0.9732** & **1.5640** & **13.9974** & **129.2176** & **0.9932** & **0.8474** & **1.7941** & **1.5631** & **2.0249** & **0.9926** & **0.7370** \\ \hline  & SVR & 3.6548 & 2212.2143 & 250.9721 & 0.9780 & 0.6754 & 3.8775 & 228.6832 & 273.7403 & 0.9737 & 0.6386 & 3.5513 & 208.0377 & 261.8331 & 0.97348 & 0.6085 \\  & GRNN & 1.5213 & 363.3035 & 177.6547 & 0.9932 & 0.8377 & 2.3967 & 135.6083 & 189.9335 & 0.9822 & 0.6947 & 3.33418 & 1872.7346 & 0.9918 & 0.6058 \\  & RBF & 9.9823 & 58.2252 & 0.76482 & 0.9973 & 0.9508 & 3.1978 & 245.615 & 248.3032 & 0.9
Figure 4.— The multi-step prediction ability of all models in Experiment I for site 1. The center of the picture is a one-step prediction time series figure. Around it are the error diagrams of eight comparison models and the proposed model. (1) For comparison (a), the developed hybrid nonlinear model has the best ability in one to three step load forecast in four datasets, whose error index was superior to other models. For instance, in one-step forecasting, the MAPE of the established model is about 0.6577%, 0.7373%, 2.0714% and 1.3288%, while the prediction accuracy of the other ICEEMDAN-based model is 0.1% to 2% lower than that of the established model. For the two and three step prediction, the proposed model is also better than other models in four sites. (2) For comparison (b), comparing ICEEMDAN-ENN and ICEEMDAN-ARIMA models with ARIMA and ENN without data preprocessing, it can be found that data preprocessing is very important to enhance the ability of load forecasting. For site 1, the MAPE of ENN and ARIMA are higher than that of ICEEMDAN-ENN and ICEEMDAN-ARIMA in one, two and three step prediction. The accuracy of ICEEMDAN-RBF and ICEEMDAN-ELM which are not shown in the table are also improved. For site 2, site 3 and site 4, and Figure 4, the situation is similar. (3) For comparison (c), it can be seen from five indexes MAPE, MAE, RMSE, IA and DC of Table 5, the proposed model is more accurate than other individual models, such as SVR and GRNN.