# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 1 Introduction
RF was used to reduce input data dimensionality, and the improved Sparrow Search Algorithm optimized model parameters to ensure precise forecasting, predictive performance, and generalization ability. Compared with common machine learning methods, deep neural networks have the capability to enhance the forecasting performance by learning the essential information from electricity load data through multiple layers [25; 26; 27; 28]. [PERSON] et al. [29] designed a forecasting model which consists of Artificial Neural Network (ANN) layers and Long Short-Term Memory (LSTM). Initially, the model employs an ANN layer with 11 neurons to capture the trends of the electricity load data. Subsequently, the above information is applied as the input data for the LSTM to obtain the forecasted results. [PERSON] et al. [30] proposed the STLF model consisting of LightGBM and FB-Prophet. The model incorporates a location encoding layer into the FB-Prophet method, capturing the nonlinear components. Subsequently, the grid search approach is employed to tune the parameters of the LightGBM model, enhancing the forecasting performance. [PERSON] et al. [31] used the Prophet algorithm to extract the trend information of the electricity load series. Subsequently, the LSTM method was utilized to extract the temporal features for forecasting the peak power of the distribution network. The experiments demonstrated that the forecasting performance of the Prophet-LSTM method surpassed that of the Prophet and LSTM methods. [PERSON] et al. [32] adopted the Prophet model to decompose the electricity load into periodic and trend components. Then, the Gated Recurrent Unit (GRU) method was employed to learn the nonlinear relationship between the input data and forecasted result. [PERSON] et al. [33] used the Prophet method to extract periodic and trend components from electricity load data. They then applied Ensemble Empirical Mode Decomposition (EEMD) to extract specific modal components from the residuals. Finally, an LSTM network was used to learn the temporal features of these components, yielding the final output. Deep Neural Networks (DNNs) improve in forecasting performance with increased depth; however, beyond a certain point, they often suffer from overfitting, which limits their forecasting ability [34]. Residual learning can mitigate this issue by using shortcut connections to enhance the DNN framework, facilitating effective training. [PERSON] et al. [34] developed an innovative residual learning approach that integrates Bidirectional Long Short-Term Memory (BiLSTM) networks with fully connected layers to improve forecasting performance. [PERSON] et al. [35] adopted the CEEMDAN method to decompose input data into different components. They used sample entropy (SE) to reconstruct these components into two sequences, capturing trend characteristics and nonlinear fluctuations. These sequences were then fed into an LSTM network to produce the final output. In summary, scholars have conducted numerous studies about electricity load forecasting, and the statistical learning-based method can effectively fit the stable load data. Statistical learning-based methods effectively fit stable load data but struggle with nonlinear load data. Machine learning methods can handle nonlinear load data, enhancing forecasting performance, yet they cannot predict complex load variation trends. While individual deep learning methods can alleviate these above issues, they often exhibit poor accuracy in long-term forecasts. Additionally, most studies focus on modeling factors influencing electricity load changes, neglecting the intrinsic value of electricity data, which hinders model performance. This paper proposes a forecasting model that integrates the Prophet method, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and an advanced residual Bidirectional Long Short-Term Memory (ARBiLSTM) block. The Prophet method extracts periodic and trend components from the electricity load series.