# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 1 Introduction  
Electricity, as a foundational public utility underpinning national socioeconomic development, plays a critical role in sustaining industrial production, urban infrastructure, and the residential quality of life. As living standards rise, the electricity demand not only increases in quantity but also evolves toward more stringent requirements for power quality, including reliability, stability, and sustainability. The stochastic nature and time-varying volatility of power load profiles pose fundamental challenges to forecasting accuracy, particularly for short-term predictions. Therefore, rational power load planning and stable power system operations are critical for achieving smart grid objectives and enabling efficient energy management [1]. Current power load forecasting methods can be categorized into two primary groups: time-series statistical models and machine learning-based artificial intelligence techniques. Statistical load forecasting techniques encompass classical methods such as exponentialsmoothing [2], multiple linear regression [3], and autoregressive moving average models [4]. Classical statistical methods offer parsimonious theoretical foundations and a high computational efficiency, demonstrating a superior performance in linear problem domains. However, a fundamental drawback of classical statistical approaches lies in their linearity assumptions, which result in an inadequate nonlinear fitting and deteriorated prediction performance when applied to power loads characterized by nonlinear dynamics and high volatility. The machine learning algorithm has been widely used in the field of power load forecasting with its strong adaptive ability and excellent ability to process complex data. In particular, deep learning methods, which have flourished in recent years, have been favored by a wide range of scholars. Popular deep learning architectures for load forecasting include convolutional neural networks (CNNs) and recurrent neural networks (RNNs) [5] and so on. However, RNNs suffer from gradient vanishing or explosion issues in long-sequence modeling, prompting the development of Long Short-Term Memory (LSTM) [6; 7; 8] and the gated recurrent unit (GRU) [9; 10; 11]. These architectures mitigate gradient problems through gated mechanisms, demonstrating a superior performance in capturing long-term temporal dependencies. To further enhance feature extraction, some scholars have introduced a bidirectional LSTM (BiLSTM) [12; 13] and bidirectional GRU (BiGRU) [14], leveraging forward-backward contextual information to improve trend capture. While bidirectional architectures enhance prediction accuracy, they concurrently increase computational complexity, creating a trade-off between performance and efficiency. As the power load presents nonlinear and fluctuating characteristics, raw data inherently contain complex features that are challenging for single models to capture. To address this, a number of researchers have adopted some decomposition techniques, commonly including wavelet decomposition [15] and model decomposition [16; 17; 18; 19], etc. These methods decompose load sequences into intrinsic mode components, effectively mitigating non-stationarity and reducing the adverse impact of random volatility on forecasting accuracy [20], and the experimental results show that these methods improve the prediction accuracy. The literature [21] proposes a forecasting method based on EMD-BiLSTM, which firstly uses EMD to decompose the original power load data; then the decomposed data of different signals and weather features are reconstructed as input data. Then it uses BiLSTM to make a prediction, and finally superimposes the prediction results to obtain the final load. Notably, this model achieves an \(E_{MAPE}\) of 0.28% and an \(R^{2}\) of 0.84 in empirical validation. Although the authors considered other feature factors, including weather, EMD suffers from computational inefficiency and is prone to modal aliasing.