## 1 Introduction  
A reliable and accuracy short-term load forecasting system is the basis of energy trade between the customers and electrical utility companies [1, 2]. With the increasing penetration of distributed generations and consumer energy systems, the randomness and variability of load profiles bring more challenges for short-term load forecasting systems. Researchers around the world have focused on short-term load forecasting in recent years and tried to get a more accuracy forecasting result using variable new technologies. The traditional load forecasting method uses statistics [1, 3, 4], which has appeared in former studies. However, large amounts of precise historical data are needed, which increases the challenges of accurate prediction. Artificial neural network-based methods are the most popular among the data-driven methods due to their strong capability of nonlinear approximation and self-learning. Different types of neural networks such as back propagation (BP) [5], radial basis function (RBF) [6], and extreme learning machines (ELM) [7, 8] have been proposed and applied in short-term load forecasting. Furthermore, a regularizing term and the combination of multiple ELM is added to reduce the randomness of traditional ELM in photovoltaic power forecasting in [8]. However, low convergence speed is always an obstacle to the large-scale application of neural networks. The rapid development of a deep learning framework and artificial intelligence (AI) technology brings more choices for the power system load forecasting. In recent years, convolutional neural network (CNN) [9, 10], deep belief network (DBN) [11, 12, 13], and deep residual networks (DRN) [14] have been developed and applied in load forecasting, which shows a promising prospect in load forecasting areas. These methods can extract the key elements of the load profile. A multiple-input deep convolutional neural network (CNN) model is proposed and applied in the short-term photovoltaic power forecasting in [9], in which solar radiation and ambient temperature combined with the historical output power of a PV system are collected as the input data of the forecasting model. In [10], a deep convolutional neural network-based forecasting method is proposed for the short-term PV power forecasting; here, the original meter data are decomposed into a two-dimensional timescale by convolution kernels and refined into advanced features by a CNN model. Deep belief network is applied in photovoltaic power forecasting in [11]; the proposed methodology is focused on real data capturing to establish the optimum architectural of deep belief network. An improved deep belief network is applied in load forecasting considering demand-side response in [12]; three aspects of the DBN are optimized to dispose the predictive accuracy. The deep belief network method is incorporated into a feed-forward neural network in [13], in which the layer-by-layer unsupervised training procedure is combined with parameters' fine-tuning based on a supervised back-propagation training method. In [14], a two-stage ensemble strategy of deep residual network is formulated to enhance the generalization capability of load forecasting. Due to its advantages to solve the vanishing gradient issues, LSTM is more effective than a recurrent neural network to deal with industrial problems that are highly related to time series [15; 16; 17]. A LSTM neural network has been successfully deployed in many practical applications; it can learn longer-term dependencies due to the associated memory units [18; 19; 20]. An LSTM architecture-based method is used in a distributed network in [18], in which the LSTM-based structure is used for the linear regression of each node and receives a variable length data sequence with its neighbors to train the LSTM architecture. A video-captioning method based on adversarial learning and LSTM is proposed in [19]; it is used to handle the temporal nature of video data exponential error accumulation.