Research on Classification Forecasting Method Based on Global Load Division of Typical day and Holiday Load

The national holiday policy has a significant impact on the holiday load, and the curve's shape differs from the regular daily load, making it challenging to directly anticipate the overall load. The categorization predicting approach presented in this research is based on the division of regular days and holidays. First, the load is divided depending on the date variable after an analysis of the characteristics of an ordinary day and a holiday load. The combined model based on similar day selection and generalized regression network is then utilized to forecast in accordance with the usual daily load. In order to predict the holiday load, a fusion model based on LightGBM and XGBoost is used. The experimental results demonstrate, using the data set provided by a power supply bureau in southern China as a practical example, that the classification forecasting method put out in this study increases the precision of global load forecasting.