## 1 Introduction  
Electricity consumption is one of the significant indices of electrical power supply system planning and operation management. The characteristics analysis of electric power consumption is the primary requirement for the quality, stable, economic and safe operation of power grid. Since it is very difficult to store electric energy, precise and reliable forecasting on electricity consumption is of great significance to satisfy the continuously increasing demand for electricity, sustain the industrialization development, and formulate long-term stable energy policy [1]. Since the 1970s, much literature has focused on developing various kinds of forecasting techniques for electric energy [2]. The traditional forecasting methods mainly include electric elasticity coefficient method [3], trend extrapolation technique [4], regression analysis method [5; 6; 7], time series method [8], and so on. However, the traditional approach greatly depends on an accurate model. Some parameters are set as fixed values in the process of prediction so that the forecasting results would be difficult for reflecting the real electricity demand accurately, which causes the traditional models to show a poor performance for electricity consumption forecasting in terms of accuracy and reliability [9]. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern power grid is becoming increasingly complex in terms of structure and function. Simultaneously, with the continuous progress of modern science and technology and the gradual deepening of basic theory research, the emergence of interdisciplinary theory provides a solid theoretical and mathematical foundation for the accurate prediction of electric energy demand. [PERSON] _et al._[10] stated that the artificial intelligence algorithms have been widely applied in various fields, mostly due to their symbolic reasoning, flexibility and explanation capabilities. [PERSON] and [PERSON] [11] proposed a prediction model in terms of non-linear combined neural network, which can effectively take account of the merits of non-linear regression analysis and artificial neural network [PERSON] _et al._[12] used time, temperature, and historical loads as inputs of an artificial neural networks (ANN) model for a more accurate short-term forecasting, and ANN-based results are compared to that of conventional regression models. It is observed that ANN models are superior to regression models in most cases in accordance with mean absolute percentage error (MAPE). [PERSON] _et al._[13] presented a new power load forecasting method on the basis of fuzzy inference and ANN, and a practical example was used to show that the prediction accuracy could be enhanced using the proposed method. [PERSON] and [PERSON] [14,15] presented a collaborative fuzzy-neural approach based on fuzzy back propagation networks to predict annual electricity consumption. [PERSON] and [PERSON] [16] presents a particle swarm optimization (PSO) as an innovation to forecast annual peak load for Kuwait and Egypt. [PERSON] _et al._[17] used three kinds of kernel functions (radial basis kernel functionlinear, and poly) to establish a prediction model based on support vector machine (SVM) theory, and the fitting degree was tested with three evaluating indicators, namely MAPE, PMSE and Theil IC. [PERSON] and [PERSON] [18] combined BP, GM (1,1), triple exponential smoothing model, and polynomial trend extrapolation model together to accurately forecast energy consumption. [PERSON] and [PERSON] [19] established a combination forecasting model consisting of neural network, grey forecasting model and time series model to predict Chinese energy consumption. Because the selection of the parameters of some intelligent algorithms and the weight coefficients of the combination forecasting models exerted a great influence on the prediction accuracy, many scholars have performed a number of researches related to the method of parameter selection and optimization.