 This paper proposes a hybrid algorithm combining similar days selection, SDS, Empirical Mode Decomposition, EMD, Long Short-Term Memory, LSTM, Neural Networks, Extreme Gradient Boosting, XGBoost, and K-Means Clustering to create a more accurate load forecasting model. The XGBoost algorithm was used to identify the most similar days in the past, while the EMD method was used to decompose the data into intrinsic mode functions, IMFs. Finally, separate LSTM neural networks were trained on each IMF and residual to generate more accurate predictions. Testing showed that this approach outperformed other methods in terms of accuracy and precision. This article was authored by Huiding Xing, Jebin Yuan, and Long Chen.