Improving short-term load forecasting accuracy with LSTM: replacing traditional methods with deep learning

This paper first proposes a load data preprocessing method to support forecasting, including horizontal processing, vertical processing, and missing value handling. Then, a Long Short-Term Memory (LSTM) forecasting model is constructed, which leverages its deep learning capabilities to improve prediction accuracy. Finally, simulation results show that the proposed model can effectively enhance the accuracy of load forecasting.