## 1 Introduction  
The concept of energy internet will effectively promote world energy production, consumption and system reform, and drive energy transformation in all countries so as to achieve energy cleanliness, efficiency, safety, convenience, and sustainable use [1]. As an important component of energy internet, distributed energy system (DES) has attracted widespread attention due to its outstanding features for instance safety and re-liability, high energy efficiency, environmental friendliness, and sustainability [2]. However, the use of distributed energy (DE) has malpractices such as random output. Therefore, precise prediction of space-time load of DES has great significance owing to the assistance of improving the overall stability of the system operation and promoting the effective use of distributed energy. For years, with the advancement of mathematical theory and the rapid development of modern computer technology, the technology and methods of load forecasting are developing all the time, which mainly includes classical prediction method and modern intelligent prediction method. Traditional prediction methods, which include time series method [3], regression analysis method [4], grey theory method [5] and so on, is simple and mature. However, due to certain defects, the prediction accuracy is unsatisfactory. Analysis of the time series method ignores the influence of other external factors on account of taking time factors as the only variable. Consequently, there will be a serious error in the prediction results when the external environment changes greatly [6]. The regression prediction method belongs to a speculate when choosing the explanatory variable or the way that the explanatory variable is expressed, which affects the diversity of the explanatory variables and the intractability of some explanatory variables to a certain extent, so as to cause the regression analysis method to be limited in predicting the load of DES [7]. The grey prediction method has a great fitting effect on the raw data as a smooth discrete sequence [8]. In contrast, the date of influencing factors of DES load are discrete. Thus, the accuracy of prediction will be greatly reduced by using grey prediction method. In summary, classical prediction method is not suitable for load forecasting of DES. Currently, scholars gradually applied intelligent algorithms into load forecasting as the emergence of various algorithms. Since intelligent algorithms can simulate the human brain mechanism, simulation forecast the change of objects through the function of self-learning and self-optimization, establish suitable models, the prediction accuracy of intelligent algorithms such as artificial neural network (ANN) has been improved [9]. Due to these elements, scholars began to focus on continuous optimization of ANN and support vector machine (SVM), so as to enhance the convergence speed and the accuracy of the prediction results. [PERSON], et al. took full account of the weather factors and established a short-term load prediction method in view of back propagation neural network (BPNN) which is a typical representative of ANN algorithm [10]. [PERSON] proposed a load prediction model in view of improvement differential evolution (IDE) and wavelet neural network (WNN) [11]. The ANN model can be used to control the error in a small range, however, this kind of algorithm converges slowly and is likely to fall into local optimum. Therefore, for the local optimization of neural networks, some scholars use support vector machine (SVM) for load forecasting research [12]. Based on SVM, [PERSON] et al. [13] and [PERSON] et al. [14] established load prediction models which promoted the prediction accuracy compared with the BPNN model. In contrast, for large-scale training samples, SVM is hard to achieve, and it is hard to solve the multi-classification problem [15].