# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 1 Introduction  
Short-term load forecasting (STLF) is a method for predicting consumption in a particular area [1]. In the power industry, STLF has evolved into a crucial tool. With the growing use of renewable energy sources, such as hydroelectricity and wind energy, STLF has also become an important part of modern power grids [2]. Starting from power generation to distribution, all enterprises require load forecasting. The reason for this is that it helps form a sensible power generation plan [3, 4]. Scholars have been performing research about short-term electricity load forecasting [5]. There are various research models for STLF, including statistical learning models, machine learning models, and deep learning models [6]. The above models have their own characteristics, but they also have limitations. Statistical learning methods primarily utilize historical time series data for load forecasting, offering a relatively faster computational speed. Common statistical approaches include the Autoregressive Integrated Moving Average method (ARIMA) [7] and exponential smoothing (ES) [8]. ARIMA can fit stable electricity load data. However, it cannot identify unstable characteristics of electricity loads, limiting its forecasting performance. [PERSON] et al. [9] adopted ARIMA to predict the load data for the subsequent day. The simulations show that ARIMA can predict the change trend of the electricity load. [PERSON] et al. [10]adopted the ARIMA method to fit the load data of biogas plants. [PERSON] et al. [11] proposed an ensemble model incorporating ARIMA and support vector regression (SVR) to capture the relationship between input and output data. This model aims to generate accurate load-forecasted results for buildings. [PERSON] et al. [12] proposed an adaptive forecasting framework consisting of a Recurrent Neural Network (RNN) and ARIMA. The above method can learn the features influencing load variations from new data, producing accurate forecasted results of the electricity load in the presence of concept drift. [PERSON] et al. [13] adopted the exponential smoothing (ES) method to forecast the electricity load of universities and improve the efficiency of power utilization. With the development of machine learning, significant achievements have been made in the field of STLF [14; 15; 16; 17; 18]. The machine learning method has achieved a higher forecasting accuracy in STLF [14; 15; 16; 17; 18]. Machine learning models can use historical data to model the relationship between inputs and outputs [16; 17; 18; 19]. [PERSON] et al. [20] employed a distribution system STLF approach, which consists of support vector regression (SVR) and a parameter optimization model for accurate STLF. Hybrid optimization involves grid traversal and particle swarm optimization. The experiment demonstrates that the above hybrid model can produce accurate forecasted results. [PERSON] et al. [21] adopted weather data as input data for the SVR method to forecast the load for the two hours before a disaster's occurrence. The model can improve the operational performance of smart grids and power companies, ensuring the stable operation of the smart grid. [PERSON] et al. [22] adopted the Ensemble Empirical Mode Decomposition (EMD) algorithm to decompose the input series into distinct components. The multivariate linear regression method was used to predict the low-frequency components, while a Long Short-Term Memory (LSTM) network predicted the high-frequency components. The final forecast was obtained by integrating these components. [PERSON] et al. [23] employed Empirical Mode Decomposition (EMD) to decompose the input series into different frequency components, followed by Extreme Gradient Boosting to forecast building loads. [PERSON] et al. [24] developed a hybrid method combining Random Forest (RF), an enhanced Sparrow Search Algorithm, and LSTM.