## 2 Background
### Basic Methodology and Shortcomings of Existing Studies
In addition to this, in the literature [27] is an electricity consumption prediction model based on a Bayesian regularized BP neural network, which proves that the proposed model has a better impact on electricity consumption prediction in Xinjiang compared with the general gradient descent method BP neural network model. However, the BP neural network has the phenomenon of \"overfitting\" and poor generalization ability, and the above method does not consider the problem of temporal correlation. A study [28] used multilayer perceptron neural networks, an adaptive network-based fuzzy inference system (ANFIS) and SARIMA to predict the power loads, respectively, and then proposeda direct and accurate algorithm to estimate the corresponding weight coefficients of each model. Despite the successful application of many optimization algorithms to determine the weighting coefficients of individual models, there is no evidence that optimization algorithms perform better in all cases. The deep RNN and single-layer RNN proposed in the literature [29] were applied to two different loaded datasets. The results show that the model has better prediction ability. When the time series data are long, the traditional recurrent neural network (RNN) implements short-term memory through neurons with self-feedback, which is theoretically capable of handling time series of arbitrary length. However, traditional RNNs sometimes encounter the problem of gradient explosion and vanishing, which is especially evident when dealing with long sequences. To address this issue, long short-term memory (LSTM) has been suggested as an enhanced version of an RNN, incorporating a gating mechanism to effectively tackle the challenges of gradient vanishing and explosion [30]. In the literature [25], a hybrid network for short-term load prediction is introduced, merging variational modal decomposition (VMD), gated recurrent unit (GRU), and a time convolution network (TCN). The VMD is applied to break down the original electrical load sequence data containing noise into fundamental IMF components with diverse frequencies and amplitudes. Subsequently, a joint load forecasting approach utilizing GRU and TCN networks is suggested for tracking high-frequency and low-frequency load variations, correspondingly; and finally, the high- and low-frequency signal prediction results are reconstructed for the GRU and TCN networks. However, the prediction error effect of the proposed method is not good, and the prediction accuracy is still not up to 90%. In the literature [31], a VMD-IWOA-LSTM approach for short- and long-term load forecasting (STLF) is proposed by also applying VMD to decompose the historical electricity load signals and optimizing the parameters in the LSTM using the improved whale optimization algorithm (IWOA). Finally, the prediction results of each component are summed up to obtain the final prediction results. The proposed method has strong anti-interference performance. However, the parameters of VMD need to be set manually, the workload will be larger in comparison, and the LSTM optimized by IWOA takes longer.