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.
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL
₹10000 (INR)
2019