## 1 Introduction  
Power load forecasting is the foundation of the operation and planning of a power system. The generation, transmission, distribution and consumption of electricity occur almost simultaneously. It is well known that massive storage of electric power is still difficult for current technologies. Thus, power supply sides and demand sides should be balanced dynamically to ensure the safety and reliability of the power grid [1]. However, the high penetration of renewable energy sources and electric devices in modern power systems result in significant uncertainties in the load demand. Motivated by this standpoint, power load forecasting has become increasingly important in modern power systems. Power load forecasting involves forecasting the load demand for a time span in the future, using historical load data and other external factors such as meteorological conditions, seasonal effects, economic factors and social activities. Accurate load forecasting can help power companies to make efficient decisions, avoid resource waste and improve grid stability and reliability [2]. Therefore, load forecasting has attracted increasing attention from researchers all over the world. Short-term load forecasting (STLF) is an approach that predicts power load consumption within an interval of an hour to a week, based on historical load data and other external factors [3]. STLF provides a basis for generating units to schedule their start-up and shut-down times, prepares the rotating reserve and carries out in-depth analyses of the limitations in the transmission system [4]. It has been demonstrated that a 1% increase in STLF error increases the operating cost by GBP 17.7 million [5] or EUR 4.55 to 9.1 million [6]. In addition, the use of distributed generation and intelligent devices generates load data with more nonlinear patterns and dynamics [7]. Furthermore, the scale of the load data also sharply increases with the widespread use of smart meters [8]. Therefore, an accurate STLF model is a premise for the safe, stable and economic operation of an electric grid. In the early years, a variety of prediction models were developed based on statistical methods and artificial intelligence methods. The statistical methods usually include autoregressive moving average [9], autoregressive integrated moving average [10], exponential smoothing [11], etc. Although these methods are easy to implement without additional feature inputs, they are not suitable for handling load data with nonlinear patterns and dynamics [12; 13]. In recent years, artificial intelligence (AI) methods have been widely used in power load forecasting, along with progressive computer technologies. As a typical AI algorithm, artificial neural networks (ANNs) have strong nonlinear modeling capabilities, and can obtain any nonlinear function without having information of the relationships between the training model and data in advance [14]. However, ANNs set hyperparameters manually, and are thus unable to effectively tackle large-scale load data. Thus, it is difficult to obtain greater prediction accuracies of load data in modern power systems [15]. Moreover, the output of the training model depends not only on the current input, but also on the previous input [16]. Therefore, traditional artificial intelligence methods cannot fully extract the features of time series to achieve greater prediction accuracies. Recently, deep learning methods have become increasingly popular in the field of time series. Long short-term memory (LSTM) developed from a recurrent neural network (RNN) can deal with long-term dependencies of time series, avoiding gradient vanishing and exploding of the RNN [17]. A convolutional neural network (CNN) can effectively capture the local relationships among adjacent time points [18] and is also widely used in many fields, such as image recognition [19], renewable energy forecasting [20], as well as load forecasting [21]. However, a large number of hyperparameters of CNN can easily result in overfitting of the training model.