## 1 Introduction
### Proposed Solutions  
Over the past few decades, scientists have proposed various solutions to improve the accuracy of short-term load forecasting (STLF) methods including traditional methods, the SD selection, the EMD methods, artificial intelligence (AI), and hybrid forecasting models [4, 5, 6, 7, 8, 9, 10, 11, 12, 13]. With energy grid diversification, an increasing number of factors impact the load demand such as weather, holidays, and electricity prices [3]. Traditional load forecasting methods are unable to provide prediction models with sufficient accuracy [14]. These models are based on mathematical methods that often perform poorly when predicting non-linear problems [15]. The SD selection is based on historical days that have comparable features to capture the features of load. To predict short-term load, a weekday index and weather events similar to the forecasted day were used [16]. The SD selection was applied to assess the attribute weights using the XGB algorithm and to compute the distance between the chosen day and the day that depends on different measurement attributes in different weights [12]. The authors also used the k-means algorithm to merge SDs into one cluster and applied it as input data for the succeeding load prediction based on the XGB distance. However, scientists found that the SD method cannot sufficiently capture the complex electric load features alone and it should be combined with other methods [12; 16; 17; 18]. AI and machine learning (ML) modeling techniques are broadly used by many electric utility companies to handle accurate load forecasting problems [19]. Although comprehensive research has been accomplished, accurate STLF remains a challenge due to its non-stationary electric load data and long-term dependencies estimating horizon [12]. The LSTM model, which is a special type of recurrent neural network (RNN), was used to predict the aggregated demand-side electric load over the short- and medium-term horizons [20]. The BiLSTM has been applied in several areas of study to provide accurate aggregated electric load prediction results [21; 22; 23]. The GRU model was used to forecast the short-term load of EV charging stations and the state of charge of batteries [3; 24]. A comparative study of ML approaches using LSTM, BiLSTM, and GRU was evaluated for day-ahead charging of the EV fleet in Canada [25]. The results showed that the BiLSTM algorithm has the lowest error among the used models and was best suited for load prediction of the EV fleet. However, in view of the complexity and non-stationarity of the aggregate load of the EV fleet, it is difficult to obtain accurate results using neural networks. The EMD method has been used by many scholars in a wide range of applications including electric load, wind speed, solar radiation, and crude oil price to improve prediction accuracy [5; 6; 7; 12; 26; 27]. The EMD method can sufficiently extract the features of the original data from non-stationary and unstable time series into a series of frequency components [28]. There are many types of EMD methods that can be applied to the time series including ensemble EMD (EEMD), complete EEMD (CEEMD), and CEEMD with adaptive noise (CEEMDAN). Compared with EMD and EEMD, the CEEMDAN method can perform a better spectral separation of the IMFs at a lower computational cost [29]. More recently, CEEMDAN was used to reconstruct the original input/output variables for electricity demand forecasting [30]. They found that the accuracy of the load model with the CEEMDAN method is greater than that without it. Based on the above-mentioned solutions, a hybrid approach consisting of the SD selection, the CEEMDAN, and deep neural networks is proposed to forecast the aggregated load of an EV fleet. The SD method is used to capture the features of load using the XGB algorithm and cluster them using the k-means method. The CEEMDAN technique is introduced to decompose historical data into a set of IMFs and a residue.