Distribution Network Load Forecasting Based on Deep Learning

In order to improve the accuracy and robustness of load forecasting in distribution networks, a Long Short Term Memory (LSTM) model was constructed based on deep learning techniques. Design a multi-layer LSTM network architecture through correlation analysis and feature importance assessment, and improve the predictive performance of the model. The experimental results show that the LSTM model exhibits excellent accuracy in hourly, daily, and weekly predictions, significantly better than traditional ARIMA and Support Vector Regression (SVR) models, and maintains good robustness in noisy environments. This method provides effective technical support for the development of smart grids.