## 2 The Forecasting Model
### Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm  
Using the Least Squares Support Vector Machine with RBF kernel function to forecast energy consumption, two parameters that will directly affect the performance of the model including the parameter of regularization and the width of the radial basis function should be determined. In this article, Improved Shuffled Frog Leaping Algorithm is used to optimize the parameters of Least Squares Support Vector Machine. The flow is as follows:  
1. Set the Shuffled Frog Leaping Algorithm parameters and initialize the frog population;
2. Calculate the frog individuals' fitness values and sort them;
3. Divide sub-populations and determine the global optimal solution of the whole population, the optimal solution and the worst solution of each sub-population;* Search and update the worst local frog individuals in each sub-population until the local search ends;
* Mix the updated subgroups;
* Determine whether the maximum iterative number is reached. If so, stop optimization and output the optimal solution. Otherwise, turn to Step (2);
* Assign the optimized parameters to Least Squares Support Vector Machine for constructing the forecasting model. The flowchart of Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm (ISFLA-LSSVM) is shown in Figure 2.  
The Forecasting Model Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm (EEMD-ISFLA-LSSVM)  
The forecasting accuracy of energy consumption is influenced by various factors. For improving the accuracy, in this article, on the basis of considering the energy consumption influencing factors of population, GDP, industrial structure (the proportion of the second industry added value), energy consumption structure, energy intensity, carbon emission intensity and total imports and exports,  
Figure 2: The flowchart of Improved Shuffled Frog Leaping Algorithm (ISFLA-LSSVM). the EEMD-ISFLA-LSSVM model for energy consumption forecasting is proposed. The forecasting process is as follows:  
(1) Data collection and influencing factors screening. Collect the sample data, including historical energy consumption data, population, GDP, industrial structure (the proportion of the second industry added value), energy consumption structure, energy intensity, carbon emission intensity and total imports and exports, and do the dimensionless processing for the data. Then, the grey relational degrees between each influencing factor and the energy consumption are calculated, and the factors for model input are screened according to the ranking of grey relational degree. (2) Sequence noise reduction based on Ensemble Empirical Mode Decomposition. Do Ensemble Empirical Mode Decomposition for energy consumption sequence to obtain multiple IMF components. (3) Energy consumption forecasting based on Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm. On the basis of considering the influencing factors of energy consumption prediction, the ISFLA-LSSVM model is used to forecast the IMF components that are obtained through EEMD, and the prediction results are reconstructed for obtaining the final energy consumption forecasting result. First, set the Shuffled Frog Leaping Algorithm parameters and initialize the frog population. Calculate the frog individuals' fitness values and sort them. Then, divide sub-populations and determine the global optimal solution of the whole population, the optimal solution and the worst solution of each sub-population. Next, search and update the worst local frog individuals in each sub-population until the local search ends, and mix the updated subgroups. Repeat the above process until the number of iterations is reached. Finally, assign the optimized parameters to Least Squares Support Vector Machine, and the forecasting model is established to predict the IMF components.