Research on Electricity Load Forecasting Based on Deep Learning
With the continuous growth of energy demand and adjustment of energy structure,power load forecasting has become a key issue in power system operation and planning.Traditional power load forecasting methods are often limited by the limitations of feature extraction and model modelling,which cannot fully explore the information in the data,thus leading to poor forecasting accuracy.In order to overcome these problems,a deep learning-based approach is adopted with the aim of improving the accuracy and stability of power load forecasting.Through experimental validation,it is found that the deep learning-based power load prediction model exhibits high prediction accuracy and stability at different time scales and prediction periods.Compared with traditional methods,the deep learning model is able to better capture the nonlinear relationships and time-series features in the data,thus improving the prediction effect.
万方数据
维普
