Research on Short-Term Load Forecasting Using XGBoost Based on Similar Days

In this paper, the power load data is increasing exponentially and the traditional forecasting model is fatigued and difficult to achieve high efficiency when dealing with massive data. A XGBoost load forecasting model based on similar days is proposed. This model analyzes the common laws of meteorological and daily types on the load, The XGBoost model with the second-order Taylor expansion and loss function is added to the regular term to control the complexity and over-fitting. The real charge data and temperature data in a certain area are taken as samples. The simulation results show that the XGBoost model based on similar days can predict the load in short-term load forecasting effectively.