Deep learning-based short-term load forecasting for power grids

With the development of smart grids, the accuracy of short-term load forecasting is of great significance for the stable operation and optimal scheduling of power grids. This paper proposes a short-term load forecasting model for power grids based on deep learning technology. The paper first reviews the current state of research on power load forecasting, including traditional methods and modern deep learning approaches. It then introduces the theoretical foundations of deep learning, with a particular focus on the application of Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM) in time series prediction. Next, the paper designs a load forecasting model that integrates CNN and LSTM, and its effectiveness is verified through experimental validation. In the section on model implementation and training, detailed information is provided on data preprocessing, model training processes, and hyperparameter tuning. Additionally, the paper explores methods for model optimization and proposes improvement strategies to further enhance forecasting accuracy. A case study is presented to demonstrate the application effect of the model in real power grid load forecasting. Finally, the paper summarizes the research findings and prospects future research directions.