## 1 Introduction  
For social development, energy is a crucial material whose consumption affects the stable and sustained development of the natural environment and economy. The rapidly developing economy in China means that energy consumption has an upward trend. In 2016, the total amount of energy consumption was 4360 million tons standard coal in China, which was nearly twice that of 2000. Currently, China is the largest energy consumer in the world. Therefore, establishing appropriate energy consumption prediction model and accurately forecasting the energy consumption in China have important practical significance, and can provide a scientific basis for China to formulate a reasonable energy production plan and energy-saving and emissions-reduction-related policies to boost sustainable development. Energy systems are complex non-linear systems whose consumption shows growth and volatility. So far, scholars have proposed various models for energy consumption forecasting, such as grey theory [1; 2; 3], fuzzy logic method [4; 5; 6], and multiple regression model [7; 8]. [PERSON] et al. [9] adopted a Markov chain model to improve the dynamic GM(1,n) (Grey Model) based on grey theory for predicting the energy consumption. [PERSON] et al. [10] applied fuzzy logic approach based on the fuzzy rule reasoning mechanism to produce a forecasting system of energy consumption change. [PERSON] et al. [11] put forward a model for predicting the annual energy consumption in the Spanish banking sector based on multiple regression analysis, which was proven to be effective. In recent years, artificial intelligence technology has been developing rapidly. Artificial intelligence models have been widely used in the field of forecasting, including the field of energy consumption forecasting. SVM (Support Vector Machine) is a widespread artificial intelligence prediction model. SVM is a supervised learning model with excellent promotion ability, and is used to find the best tradeoff between model complexity and learning ability. It can be popularized and applied to the forecasting field of machine learning [12; 13; 14; 15]. LSSVM (Least Squares Support Vector Machine) is a modified algorithm for standard SVM based on least squares value function and equality constraint. Compared with standard SVM, it has better performance for prediction [16; 17; 18]. Some scholars have used LSSVM to forecast energy consumption. [PERSON] et al. [19] put forward a new energy consumption prediction model with LSSVM optimized by a hybrid quantum harmony search algorithm. [PERSON] et al. [20] reviewed LSSVM and Group Method of Data Handling use in the field of prediction and proposed a novel model of GMDH (Group Method of Data Handling) and LSSVM for building energy consumption forecasting. In order to improve prediction accuracy, researchers have used various algorithms to select LSSVM parameters, such as Ant Colony Optimization [21; 22; 23], Artificial Bee Colony [24; 25], and Grey Wolf Optimization [26; 27; 28]. [PERSON] et al. [29] proposed a modified ant colony optimization (ACO) algorithm for optimizing LSSVM parameters and applied the model of ACO-LSSVM to predict the short-term electrical power load. [PERSON] et al. [30] proposed a prediction model by LSSVM and Artificial Bee Colony for gold prices. [PERSON] et al. [31] proposed a novel hybrid prediction model of Grey Wolf Optimization (GWO) and LSSVM for realizing gold price forecasting, which was proven to make good predictions. In this article, we use the Shuffled Frog Leaping Algorithm (SFLA) to realize the optimization of LSSVM for energy consumption prediction. SFLA was first proposed by [PERSON] and [PERSON] in 2003, which has the advantages of fewer parameters to adjust, fast computation speed, and strong global searching ability [32; 33; 34]. In addition, the energy consumption sequence contains some noise whose forecasting is affected by various external factors, so Ensemble Empirical Mode Decomposition (EEMD) is adopted for de-noising of the energy consumption sequence before forecasting.