Optimization and Comparison of Power Load Forecasting Models Based on Long Short Term Memory Networks

In this article, we cited the use of LSTM (Long Short Term Memory) model to calculate power load, demonstrating its powerful ability in processing time series data The article first introduces the importance of data preprocessing, including data cleaning, normalization, and feature selection, which help improve the accuracy of the model. By comparing with SVM and RNN models, we find that LSTM performs better in prediction accuracy and stability, with a Mean Squared Error (MSE) of 4.5875 and a Mean Absolute Error (MAE) of 1.8243 for the LSTM model. From the results of the experimental phase, it can be seen that the LSTM algorithm can more accurately capture the trend of power load changes, while possessing high stability and reasonable computational efficiency. Finally, we propose to explore more external variables and advanced network structures in the future to improve prediction accuracy, while achieving real-time prediction and online learning, providing support for the optimization management of the power system.