# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 1 Introduction  
As a key industry to economic development, power provides a basic guarantee for social production and life. With the advantages of low pollution, high efficiency, low distribution costs and a wide range of applications, power will remain an irreplaceable source of energy for a considerable period of time in the future. With the rapid development of the electricity market, the electricity demand for social production and life is increasing, which places high demands on the production planning and scheduling of the power system. Therefore, the accuracy of short-term electricity load forecasting is very important [1], as it will enable us to ensure the reliable and economic operation of the power system [2]. However, the electricity load is a random and non-stationary series that is affected by various influencing factors, such as the time of day, weather conditions, economic indicators, etc., presenting many challenges to load forecasting [3]. With the constant development of smart grids, various distributed smart meters have been installed and configured in the power system, collecting a large amount of accurate and reliable load data, which provide the basis for short-term power load forecasting. For the purpose of improving the accuracy of short-term load prediction, many experts have done research work on the subject. These methods mainly include traditional forecasting methods, artificial intelligence forecasting methods and hybrid forecasting methods. The traditional forecasting methods include regression analysis [4], autoregressive integrated sliding average model (ARMIA) [5; 6], seasonal exponential smoothing [7], and Kalman filter [8], which are all implemented based on statistics and have the advantages of being simple and fast, with good fitting and forecasting effects on smooth curves. However, as a typical time-series forecasting problem, short-term power load prediction usually entails strong volatility in the load data, and the factors affecting the load are numerous and complex, so it is difficult to guarantee the accuracy of forecasting using traditional statistics-based forecasting methods. Experts and scholars from various countries have applied machine learning and deep neural networks (DNN) in short-term load prediction. Typical algorithms for forecasting using machine learning approaches include expert systems [9; 10] and support vector regression [11; 12], but expert systems do not have self-learning capabilities, and support vector regression has difficulties in handling large-scale data. In addition, random forests [13] and regression trees [14] have been used in short-term load prediction. With the development of artificial intelligence technology, artificial neural network (ANN) forecasting methods [15], deep learning methods [16] and deep belief networks (DBN) [17] have also been used in short-term load prediction in large numbers, and have achieved better forecasting results. Aiming to more fully tap the information within the historical data of power loads and to fully consider the relevant influencing factors affecting integration within the forecasting model, many experts have proposed hybrid forecasting methods to make full use of the advantages of various forecasting methods, forming a complementary effect and further improving the accuracy of short-term load prediction. Among the hybrid prediction methods used for short-term electricity load, the combined use of convolutional neural networks (CNN) and recurrent neural networks (RNN) is a classical approach. The references [18; 19] used CNNs to extract feature vectors, the reference [18] fed the processed feature vectors into a long short-term memory network (LSTM) for forecasting, and the reference [19] fed the feature vectors constructed from CNN extraction into a gated recurrent unit (GRU) for forecasting, and both models obtained high accuracy forecasting values. Both GRU and LSTM are RNN networks, and are designed to solve the problem of gradient disappearance in RNN networks.