## 2 Background
### Basic Methodology and Shortcomings of Existing Studies
Compared to an RNN, LSTM has advantages in preventing gradient explosion and vanishing when dealing with time series data correlation issues, and it has been widely used in the field of forecasting and time series-related fields due to its correlation with time series, advantages in long-term memory, and strong learning ability on nonlinear data. The learning effect of a prediction model is often affected by the quality of the input data; therefore, appropriate processing of the data is key to improving the accuracy of the prediction model. Some common data decomposition methods have been introduced above, among which EMD is widely used due to its multi-resolution and adaptive nature [20]. However, EMD may encounter the problem of information feature extraction and obfuscation when constructing solid-mode functions. EEMD effectively solves this problem by introducing noise-assisted signal processing [21]. Although EEMD has improved in solving the mode-aliasing problem of EMD, determining the amplitude of the white noise added to EEMD is still challenging [22]. CEEMDAN is adaptive and suitable for decompression of complex signals requiring high adaptability and high resolution, but it is computationally complex and sensitive to noise [23]. To overcome the limitations of the EMD variant, white noise is added during operation to eliminate mode mixing. After several mixings, the noise remains in the signal. The empirical wavelet transform (EWT) is a revolutionary innovation based on a filter structure that is closely related to wavelet theory, which fundamentally solves the mode-mixing problem [24]. The core of the EWT lies in the design of adaptive filters, which requires the determination of the filter boundaries based on the spectral characteristics of the signal. This process not only relies on the choice of parameters but may also require complex algorithms to determine these boundaries automatically, increasing the complexity of the implementation. However, EWT may require more computational resources for filter design and signal reconstruction than variants of EMD, which may not work well when applied to short-term power load forecasting. The VMD is adaptive, does not require preset basis functions, and is free of modal aliasing. Its ability to provide stable modal decompositions helps to extract clear periodic and trending components from power load data, which is crucial for the accuracy of forecasting models. VMD, which is capable of decomposing complex sequences into multiple signals with adjustable amplitude and frequency, overcomes the difficulty of EEMD in distinguishing the frequency characteristics, achieves accurate signal decomposition, and improves the operational efficiency [25]. VMD is more suitable than CEEMDAN and EWT if the signal needs to be processed quickly and the parameters can be chosen rationally, e.g., for applications in short-term power load forecasting. In the initial stages of deep-learning exploration, the power load was primarily seen as a regression task, with reliance on conventional statistical models and artificial neural networks. However, the autoregressive nature, dynamism, and weather-related aspects of power load data make it challenging to achieve precise load predictions solely through technologies like artificial neural networks. These methods struggle to effectively capture the complexities of nonlinear time series, such as the power load, making it hard for a singular model to meet the precision standards for load forecasting. Employing a blend of diverse models stands as a practical approach to enhance the precision of power load predictions. In the literature [26], a power load-forecasting method based on the combination of composite adaptive filtering and fuzzy BP neural network is suggested. The proposed short-term power load algorithm is validated using real power load data to confirm its effectiveness.