## 3 Empirical Analysis
### Model Comparison and Error Analysis  
In fact, EEMD-ISFLA-LSSVM is a complex combination model, which comes from the improvement and combination of a variety of single models. The EEMD-ISFLA-LSSVM model has evolved from the models of BP neural network, LSSVM, SFLA-LSSVM and ISFLA-LSSVM, which shows the thinking process for proposing the model. For proving the validity and superiority of the EEMD-ISFLA-LSSVM model further, four models of ISFLA-LSSVM, SFLA-LSSVM, LSSVM, and BP neural network are selected to make a comparison with the EEMD-ISFLA-LSSVM model by the same sample, which can prove that the combination model is obviously superior to single algorithm and introducing the noise reduction method of EEMD is helpful for improving the prediction accuracy. The parameter settings of the ISFLA-LSSVM, SFLA-LSSVM, and LSSVM models are the same as those of the EEMD-ISFLA-LSSVM model. The parameter settings of the BP neural network are as follows: the initial weight is a random number between \((-1,1)\); the activation functions between the input layer and the hidden layer, the hidden layer and the output layer are both Sigmoid functions; the hidden layer function is a tansig transfer function; the output layer function is a logsig transfer function; the learning rate is 0.1; the momentum factor is 0.8; the network global error is 0.001; the maximum number of training is 500; the number of hidden layer nodes is 11. The comparison results, relative error, and boxplot of relative error are shown in Figures 8-10. Figure 8 shows the fitting degree between the energy consumption curve forecasted by different models and the actual curve of energy consumption. The forecasting curve of the EEMD-ISFLA-LSSVM model fits the actual curve best. Figure 9 shows the relative errors of each model for energy consumption forecasting. The boxplot in Figure 10 shows the relative errors' minimum, first quartile, the median, third quartile and the maximum in each model. From Figures 9 and 10, it can be seen that EEMD-ISFLA-LSSVM model has the minimum relative error, followed by ISFLA-LSSVM model, SFLA-LSSVM model, LSSVM model, and BP has the maximum relative error. Figure 8: Comparison of forecasting results. For evaluating the forecasting effect of each model more objectively, _MAPE_ (mean absolute percentage error), _RMSE_ (root mean square error) and _MAE_ (mean absolute error) are applied to compare the prediction accuracy. The calculation equations are shown as follows:  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{\dot{y}_{i}-y_{i}}{y_{i}}\right| \tag{13}\]  
Figure 9: The relative errors of five models: (**a**) EEMD-ISFLA-LSSVM; (**b**) ISFLA-LSSVM; (**c**) SFLA-LSSVM; (**d**) LSSVM; (**e**) BP. \[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(\hat{y}_{i}-y_{i})^{2}}}} \tag{14}\]  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}{|\hat{y}_{i}-y_{i}|}}. \tag{15}\]  
The calculation results of \(MAPE\), \(RMSE\) and \(MAE\) for different models are shown below. From Table 3, it can be concluded that the \(MAPE\), \(RMSE\) and \(MAE\) of the EEMD-ISFLA-LSSVM model are the smallest of all the models, reaching 1.47%, 3.27, and 2.72, respectively. Next is the ISFLA-LSSVM model: the \(MAPE\), \(RMSE\) and \(MAE\) are 3.16%, 8.46, and 7.04, respectively. The \(MAPE\), \(RMSE\) and \(MAE\) of the BP model are the largest, reaching 7.68%, 19.68, and 15.94, respectively. Moreover, SFLA-LSSVM model has better forecasting ability than LSSVM. It can be seen that the evaluation results of the three indexes for the five models tend to be consistent. Ranking the prediction accuracy of five models based on different indexes, we can get the same ranking result as follows: EEMD-ISFLA-LSSVM > ISFLA-LSSVM > SFLA-LSSVM > LSSVM > BP. According to the ranking results, as a complex combination model for energy consumption prediction, EEMD-ISFLA-LSSVM can combine the advantages of different algorithms, whose forecasting effect is obviously superior to that of a single algorithm, such as LSSVM and BP neural network.